Three-quarters of a million babies are born extremely preterm (< 28 weeks’ gestation) world-wide with devastating outcomes: 20% die in the newborn period, a further 35% develop bronchopul-monary dysplasia (BPD) and 10% suffer from cerebral palsy. Pioglitazone, a Peroxisome Proliferator Activated Receptor Gamma (PPARγ) agonist, may reduce the incidence of BPD and improve neurodevelopment in extreme preterm babies. Pioglitazone exerts an anti-inflammatory action mediated through Nuclear Factor-kappa B repression. PPARγ signalling is underactive in preterm babies as adiponectin remains low during the neonatal pe-riod. In newborn animal models, pioglitazone is shown to be protective against BPD, necrotising en-terocolitis, and lipopolysaccharide-induced brain injury. Single Nucleotide Polymorphisms of PPARγ are associated with inhibited preterm brain development and impaired neurodevelop-ment. Pioglitazone was well tolerated by the foetus in reproductive toxicology experiments. Bladder cancer, bone fractures, and macular oedema seen rarely in adults may be avoided with a short treatment course. Other effects of pioglitazone including improved glycaemic control and lipid metabolism may provide added benefit in prematurity. Currently, there is no formulation of pioglitazone suited for administration to preterm babies A liquid formulation of pioglitazone needs to be developed before clinical trials. The potential benefits are likely to outweigh any anticipated safety concerns.

As the global human population increases, animal agriculture must adapt to provide more animal products while also addressing concerns about animal welfare, environmental sustainability, and public health. The purpose of this review is to discuss the digitalization of animal farming with Precision Livestock Farming (PLF) technologies, specifically biosensors, big data, and block chain technology. Biosensors are noninvasive or invasive sensors that monitor an animal’s health and behavior in real time, allowing farmers to monitor individual animals and integrate this data for population-level analyses. The data from the sensors is processed using big data-processing techniques such as data modelling. These technologies use algorithms to sort through large, complex data sets to provide farmers with biologically relevant and usable data. Blockchain technology allows for traceability of animal products from farm to table, a key advantage in monitoring disease outbreaks and preventing related economic losses and food-related health pandemics. With these PLF technologies, animal agriculture can become more transparent and regain consumer trust. While the digitalization of animal farming has the potential to address a number of pressing concerns, these technologies are relatively new. The implementation of PLF technologies on farms will require increased collaboration between farmers, animal scientists, and engineers to ensure that technologies can be used in realistic, on-farm conditions. These technologies will call for data models that can sort through large amounts of data while accounting for specific variables and ensuring automation, accessibility, and accuracy of data. Issues with data privacy, security, and integration will need to be addressed before there can be multi-farm databases. Lastly, the usage of blockchain technology in animal agriculture is still in its infancy; blockchain technology has the potential to improve the traceability and transparency of animal products, but more research is needed to realize its full potential. The digitalization of animal farming can supply the necessary tools to provide sustainable animal products on a global scale.

Victor Vasarely’s (1906-1997) important legacy to the study of human perception is brought to the forefront and discussed. A large part of his impressive work conveys the appearance of striking three-dimensional shapes and structures in a large-scale pictorial plane. Current perception science explains such effects by invoking brain mechanisms for the processing of monocular (2D) depth cues. Here in this study, we illustrate and explain the local effects of 2D color and contrast cues on the perceptual organization in terms of figure-ground assignments, i.e. which local surfaces are likely to be seen as “nearer” or “bigger” in the image plane. Paired configurations are embedded in a larger, structurally ambivalent pictorial context inspired by some of Vasarely’s creations. The figure-ground effects these configurations produce reveal a significant correlation between perceptual solutions for “nearer” and “bigger” when no other monocular depth cues are given in the image. In consistency with previous findings on similar, albeit simpler visual displays, a specific color may compete with luminance contrast in resolving the planar ambiguity of a complex pattern context. Vasarely intuitively understood, and successfully exploited, this kind of subtle context effect in his art, well before empirical investigations had set out to study and explain their genesis in terms of information processing by the visual brain.

Objective: The aim of our study was to perform a retrospective analysis of the volume of cervical screening tests, the number of patients treated with an excision method, and the incidence of invasive and non-invasive cervical during a pandemic and pre-pandemic period of 24 months. Results: There was a statistically significant age difference between the two study periods, both by the average age of patients and by age group. The mean difference was 32 years before the pan-demic and 35 years during the pandemic (p-value >0.05). The majority of patients presenting for investigations before and during the pandemic were in the 30-39-year-old age group (31.95%, respectively; 34.2% (p-value = 0.003). The biggest patient loss ratio identified by age group was in the 50–59 years: 14,53% in the pre-pandemic period and 9,1% in the pandemic period. In the pan-demic period, patients from rural areas presented in the clinical trial with a lower rate of 39.52% (83 patients) vs. 60.47% (127 patients) in urban areas. A higher percentage of patients experiencing cervicorrhagia as a clinical manifestation in the pandemic period vs. the prepandemic period, with an increase in more severe lesions in the pandemic period, has a statistical significance of 8% more newly diagnosed compared to the pre-pandemic period. Conclusion: The addressability of the patients during the COVID period was not affected in a drastic way in our study. We encountered a decrease in appointments in the age group 50–59 years and a decrease in patients with rural residence. In our study, we found an increase in cervicorrhagia as a reason for consultation in the pandemic period with a higher lesion degree, both on a pap smear and on a cervical biopsy.

Considering the worldwide impact of heart failure, it is crucial to develop approaches that can help us comprehend its root cause and make accurate predictions about its outcome. This is essential for lowering the suffering and death rates connected with this widespread illness. Cardiomyopathies frequently result from genetic factors, and the study of heart failure genetics is advancing quickly. Dilated cardiomyopathy (DCM) is the most prevalent kind of cardiomyopathy, encompassing both hereditary and nongenetic abnormalities. It is distinguished by the enlargement of the left ventricle or both ventricles, accompanied by reduced contractility. The discovery of the molecular origins and subsequent awareness of the molecular mechanism is broadening our knowledge of DCM development. Additionally, it emphasizes the complicated nature of DCM and the necessity to formulate several different strategies to address the diverse underlying factors contributing to this disease. Genetic variations that can be transmitted from one generation to another can be a significant contributor to causing family or isolated DCM. Genetic variation also plays a significant role in determining susceptibility for acquired triggers for DCM. The genetic causes of DCM can have a diverse range of phenotypic expressions. It is crucial to select the most probable patients to have advantages from genetic testing. The purpose of this research is to emphasize the significance of identifying genetic DCM, the crucial relationships between genotype and phenotype, and the usefulness of genetic testing in the therapeutic care of both those affected and their relatives. This approach is expected to gain importance once treatment is guided by genotype-specific advice and disease-modifying medications.

This paper provides an in-depth exploration of the role of Big Data and Artificial Intelligence (AI) in advancing dairy farming towards net zero emissions, a critical goal in the face of the global climate crisis. The study emphasizes how these technologies significantly enhance the management of greenhouse gas (GHG) emissions and optimize resource use, thereby contributing to environmental sustainability in agriculture. A key aspect of this transition is the alignment with international climate commitments, such as the Paris Agreement, which are instrumental in steering global efforts toward emission reduction and mitigating climate change. The integration of Big Data and AI in dairy farming emerges as a powerful tool to reduce the sector's environmental impact while sustaining economic growth. The paper delves into the specific applications of these technologies in emission management, including predictive analytics for feed optimization, manure management, and energy efficiency enhancements. It also addresses the broader implications of technological integration in dairy farming, considering aspects like benchmarking standards, data privacy, and the role of policy in fostering sustainable practices. The study underscores the challenges inherent in adopting these advanced technologies, including the need for improved farmer training, data quality, and compatibility with existing systems. It also advocates for enhanced policy frameworks that support sustainable practices, encourage technological adoption, and balance economic viability with environmental responsibility. This comprehensive analysis offers valuable insights into harnessing digital technologies for climate change mitigation and delineates a path for the dairy industry towards achieving net zero emissions, thereby contributing significantly to global environmental sustainability efforts.

A global event such as the COVID-19 crisis presents new, often unexpected responses that are fascinating to investigate from both, scientific and social standpoints. Despite several documented similarities, the Coronavirus pandemic is clearly distinct from the 1918 flu pandemic in terms of our exponentially increased, almost instantaneous ability to access/share information, offering an unprecedented opportunity to visualise rippling effects of global events across space and time. Personal devices provide “big data” on people’s movement, the environment and economic trends, while access to the unprecedented flurry in scientific publications and media posts provides a measure of the response of the educated world to the crisis. Most bibliometric (co-authorship, co-citation, or bibliographic coupling) analyses ignore the time dimension, but COVID-19 has made it possible to perform a detailed temporal investigation into the pandemic. Here, we report a comprehensive network analysis based on more than 20000 published documents on viral epidemics, authored by over 75,000 individuals from 140 nations in the past one year of the crisis. In contrast to the 1918 flu pandemic, access to published data over the past two decades enabled a comparison of publishing trends between the ongoing COVID-19 pandemic and those of the 2003 SARS epidemic, to study changes in thematic foci and societal pressures dictating research over the course of a crisis.

The emergence of precision and digital livestock farming presents an opportunity for sustainable animal farming practices that enhance animal welfare and health. However, this transformation of modern animal farming through digital technology has several implications for the technological, social, economic, and environmental aspects of farming. It is crucial to analyze the ethical considerations associated with the digitalization of modern animal farming, particularly in the context of human-animal relationships and potential objectification. This analysis can help develop frameworks for improving animal welfare and promoting sustainability in animal farming.One of the primary ethical concerns of digital livestock farming is the potential for a digital divide between farmers who have access to advanced technologies and those who do not. This could lead to a disparity in animal welfare and health outcomes for different groups of animals. Additionally, the use of artificial intelligence in digital livestock farming may lead to a loss of personal connection between farmers and animals, which could impact the animal's well-being. Another ethical concern of digital livestock farming is the potential for objectification of animals as mere data points. The use of sensors and other monitoring technologies can provide valuable data on animal health and behavior, but it is important to remember that animals are sentient beings with complex emotional and social needs. The use of digital technologies should not lead to neglect of animal welfare or a lack of human responsibility towards animals.Furthermore, social context becomes essential while integrating technologies in the livestock farming to overcome ethics. By considering the cultural and societal norms of different communities, we can ensure that the use of digital technologies does not undermine these values. To address these ethical challenges, the development of standards and codes of conduct for the adoption and implementation of digital livestock farming tools and platforms can help ensure that animal welfare and sustainability are prioritized. This can help alleviate the privacy concerns of stakeholders and improve sustainability in animal farming practices. Additionally, the use of virtual and augmented reality technologies can provide a way to enhance human-animal interactions and provide more personalized care to animals, further promoting animal welfare.

Early disease detection in livestock allows for target treatment decreasing antibiotics use and allow advancements in precision veterinary medicine. MicroRNA (miRNA) -driven signaling cascades play a crucial role in the context of farm animal disease diagnostics and prediction, and their proper understanding remains a challenge. In livestock farm animals, only a small number of miRNAs have been fully validated with respect to disease conditions and physiological or behavioral traits. Low abundance of miRNAs in blood and bodily fluids, along with a small number of nucleotides, makes detection and discrimination tedious and challenging task in. miRNAs usually are homologous, owing to which detection specificity becomes next to impossible when screening for multiple miRNAs in the same analyte sample. Hence, a concurrent, multiplexing, approach becomes crucial for the development of on-farm point-of-care based detection systems. Comprehensive screening methods demand broad dynamic range and enhanced specificity. For on-farm handheld platform development, the ability to screen for multiple varieties of miRNA is essential. In this review paper, I provide an overview of the recent developments of miRNA sensing and the current bottlenecks in the realization of the sensors for detecting miRNAS as target analyte for various livestock disease detection applications. Due to the nascent stages of this research, the possibilities of exploiting miRNAs as a biomarker opens up ways to move from reactive to predictive possibilities in diseases detection in the modern digital livestock farming.

Sensor enabled big data and Artificial Intelligence platforms has the potential to address global socio-economic trends related to the livestock production sector through advances in the digitization of precision livestock farming. The increased interest in animal welfare, the likely reduction in the number of animals in relation to population growth in the coming decade and the growing demand for animal proteins pose an acute challenge to prioritizing animal welfare on the one hand while maximizing the efficiency of production systems on the other. To stimulate a sustainable, digital, and resilient recovery of the agricultural and livestock industrial sector, there is an urgent need for testing and develop new ideas and products such as wearable sensors. By validating and demonstrating a fully functional wearable sensor prototype within an operational environment on the livestock farm that includes a miniaturized animal-borne biosensor and an artificial intelligence (AI) based data acquisition and processing platform, the unmet current needs can be fulfilled. The expected quantifiable results from wearable biosensors will demonstrate that the digitization technology can perform acceptably within the performance parameters specified by the agricultural sector and under operational conditions, to measurably improve livestock productivity and health. There is a need for a multimodal, digitized, automated biosensor and health monitoring platform containing AI algorithms for optimized stress and disease prediction in farm animals. An experimental development of non-invasive wearable and wireless physiological sensor networks that can be deployed in agricultural environments would allow for real-time extraction and integration of physiological data and display on decision support dashboards for livestock workers. By testing the effectiveness of the system in generating meaningful longitudinal data for further research into physiological and behavioral characteristics of farm animals, novel insights about animal welfare can be developed. The successful implementation of the digital wearable sensor networks would provide actionable real-time information on animal health status and can be deployed directly on the livestock farm, which will strengthen the green and digital recovery of the economy due to the significant innovation potential. Continuous monitoring of animal health and physiological functioning promotes more environmentally and socially acceptable developmental pathways. Once demonstrated, the portable animal health monitoring platform will close a critical gap in digitized livestock farming and position the agricultural industry as a frontrunner in the sector of farm animal monitoring and measurement systems. The multimodal wearable sensor-driven AI approach is expected to significantly improve the effectiveness of livestock decision support systems and the selection of resilient animals for the next generation and provide predictive data for end-users in livestock farming.

This paper pioneers a novel exploration of environmental impacts in livestock farming, with a focus on pig farming's intersection with climate change and sustainability. It emphasizes the transformative potential of data-driven Artificial Intelligence (AI) methodologies, specifically the Internet of Things (IoT) and multimodal data analysis, in promoting equitable and sustainable food systems. The study observes five pigs, aged 86 to 108 days, using a tripartite sensor that records heart rate, respiration rate, and accelerometer data. The unique experimental design alternates between periods of isolation during feeding and subsequent pairing, enabling the investigation of stress-induced changes. Key inquiries include discerning patterns in heart rate data during isolation versus paired settings, fluctuations in respiration rates, and behavioral shifts induced by isolation or pairing. The study also explores potential detection of gait abnormalities, correlations between pigs' age and their gait or activity patterns, and the evolution of pigs' walking abilities with age. The paper scrutinizes accelerometer data to detect activity changes when pigs are paired, potentially indicating increased stress or aggression. It also examines the adaptation of pigs to alternating isolation and pairing over time, and how their heart rate, respiration rate, and activity data reflect this process. The study considers other significant variables, such as time of day and isolation duration, affecting the pigs' physiological parameters. Sensor data is further utilized to identify behavioral patterns during periods of feeding, isolation, or pairing. In conclusion, this study harnesses IoT and multimodal data analysis in a groundbreaking approach to pig welfare research. It underscores the compelling potential of technology to inform about overall pig welfare, particularly stress levels and gait quality, and the power of data-driven insights in fostering equitable, healthy, and environmentally conscious livestock production systems.

In this paper, we critically examine the burgeoning role of advanced computational methodologies in deciphering the complex tapestry of farm animal behaviors and emotions. Leveraging digital imaging and artificial intelligence, we unearth nuanced behavioral patterns and micro-expressions, offering predictive insights into animal emotional states. Sound vocalization analysis, often overlooked, emerges as a pivotal tool, decoding intricate communicative nuances and emotional undertones. Cognitive tests, including mirror and bias assessments, challenge long-standing perceptions, revealing surprising depths of animal self-awareness and cognitive sophistication. However, the paper also underscores the imperative of integrating these tools with a profound understanding of animal psyche, ensuring technology serves as an enhancer, not a replacement, of traditional observational methods. This research not only highlights the transformative potential of cognitive computing in animal welfare but also calls for a judicious application, ensuring technology augments, not undermines, the intrinsic value of human-animal interactions and understanding.

Respiratory syncytial virus (RSV) is a common respiratory virus that can cause mild to severe illness in people of all ages. The most common symptoms of an RSV infection are cough, runny nose, and fever. However, RSV can also cause more serious complications, such as tracheobronchitis, bronchiolitis, pneumonia, and apnea. RSV is especially dangerous for infants, young children, and older adults. RSV is a seasonal virus, with peak incidence in the winter months. The virus spreads through respiratory droplets produced when an infected person coughs or sneezes. The virus can also be spread through contact with contaminated surfaces. Treatment is aimed at relieving symptoms and preventing complications. This review will provide an overview of the medical significance, virology, pathogenesis, immunity, epidemiology, symptoms, risk factors, and complications associated with RSV, and the diagnosis and management of RSV infections.

This critical review illuminates the transformative potential of Artificial Intelligence (AI) and sensor technologies in the dairy livestock export industry, an area facing mounting pressure for heightened efficiency and sustainability. We rigorously scrutinize the uptake of these novel technologies in identifying 'shy feeders,' automating weight monitoring of individual livestock, and refining cattle enumeration procedures. The investigation unravels their capacity to bolster animal welfare standards, minimize supply chain discrepancies, and amplify operational productivity. Moreover, the research delves into how these innovations may enhance market access and competitiveness in a swiftly shifting global dairy landscape. We further highlight the challenges encountered and future trajectories, providing a strategic framework for technology integration within the livestock export sector. Ultimately, this review underlines the importance of adopting AI and sensor technologies, indicating a shift towards precision digital livestock farming that amalgamates efficiency, animal welfare, and profitability.

The metaverse, a virtual world comprising a collective virtual shared space where users interact with one another through avatars and computer-generated objects, aims to closely mimic our real world by integrating elements of Artificial Intelligence (AI), immersive reality, advanced connectivity, and Web3. As metaverse technologies gain momentum across multiple sectors, including animal farming, their potential for addressing complex challenges such as climate change and sustainability in precision food production systems becomes increasingly apparent. However, it is crucial to consider the ethical implications and the role of sensor data and livestock behavior analysis in developing metaverse technologies for modern animal farming, given the sensitive and controversial nature of animal welfare. Failure to address these ethical considerations and harness the power of sensor data and behavior analysis could lead to a lack of credibility and insensitivity towards adopting metaverse technologies in the animal farming sector. It is essential to ensure that the development of metaverse technologies does not prioritize technology over animal welfare, ethics, socio-economic implications, and the potential for data-driven insights. Addressing diversity and equity in the context of animal farming and the metaverse is crucial to avoid perpetuating existing inequalities during the implementation of metaverse technologies. This groundbreaking paper ventures into unexplored territory, shedding light on the untapped potential of the metaverse for modern animal farming. While research on this topic is still in its infancy, we embark on a journey of visionary speculation, presenting a compelling technology forecast that envisions the extraordinary possibilities awaiting us in the future. By delving into the metaverse's transformative capabilities, we provide a glimpse into a world where animal farming transcends its traditional limitations and embraces a new era of efficiency, sustainability, and ethical practices.

Long COVID is a phenomenon in which individuals experience persistent symptoms after recovering from COVID-19. The symptoms are discovered to be unique for every individual and can affect multiple organs systems in the body. This study aims to identify effective natural remedies for long COVID by analyzing hub genes associated with the symptoms of the condition and evaluating the repurposed drugs catered and used for treating the symptoms of long COVID. The most common and prevalent symptoms of long COVID were identified; Fatigue, Shortness of Breath, Loss of Smell, Headache, Brain Fog, Chest Pain, Insomnia, Heart Palpitations, Dizziness, Joint Pain, Depression, Anxiety, Tinnitus, Loss of Appetite. Hub genes for each of the symptoms provided an insight on the key biological pathways of the symptom. Repurposed drugs identified, provided the template to identify the natural compounds with similar structure as a potential therapeutic drug. The natural compounds were retrieved using fingerprint search of the repurposed drugs from the NPASS Database. The findings of this study suggest several natural remedies for each symptom based on the molecular docking of the hub gene and natural compound using iGEMDOCK. The identified natural remedies may hold promise in treating long COVID, but further research is required to explore the efficacy and effectiveness of the proposed natural compounds. The results of the study pose important implications for the development of effective treatments for long COVID.

The recent outbreak of the new virus in Wuhan city, China from the sea food market has led to the identification of a new strain called the corona virus and named as novel corona virus (2019-nCoV) belonging to Coronaviridae family. This has created major havoc and concern due to the mortality of 250 persons and affecting more than 10,000 people. This virus causes sudden fever, pneumonia and also kidney failure. In this study a computational approach is proposed for drug and vaccine design. The spike protein sequences were collected from a protein database and analysed with various bioinformatics tools to identify suitable natural inhibitors for the N-terminal receptor binding domain of spike protein. Also, it is attempted to identify suitable vaccine candidates by identifying B-Cell and T-cell epitopes. In the drug design, the tanshinone Iia and methyl Tanshinonate were identified as natural inhibitors based on the docking score. In the vaccine design, B-cell epitope VLLPLVSSQCVNLTTRTQLPPAYTN was found to have the highest antigenicity. FVFLVLLPL of MHC class-I allele and FVFLVLLPL of MHC class-II allele were identified as best peptides based on a number of alleles and antigencity scores. The present study identifies natural inhibitors and putative antigenic epitopes which may be useful as effective drug and vaccine candidates for the eradication of novel corona virus.

Background: Obesity remains a widely debated issue, often criticized for the limitations in its identification and classification. This study aims to compare two distinct systems for classifying obesity: Body Mass Index (BMI) and body fat percentage (BFP) as assessed by bioelectrical impedance analysis (BIA). By examining these measures, the study seeks to clarify how different metrics of body composition influence the identification of obesity-related risk factors. Methods: The study enrolled 1255 adults, comprising 471 males and 784 females, with a mean age of 36 ± 11.90 years. Participants exhibited varying degrees of weight status, including optimal weight, overweight, and obesity. Body composition analysis was conducted using the TANITA Body Composition Analyzer BC-418 MA III device (T5896, Tokyo, Japan), evaluating the following parameters: current weight, basal metabolic rate (BMR), adipose tissue (%), muscle mass (%), and hydration status (%). Results: Age and psychological factors like cravings, fatigue, stress, and compulsive eating were significant predictors of obesity in the BMI model but not in the BFP model. Additionally, having a family history of diabetes was protective in the BMI model (OR: 0.33) but increased risk in the BFP model (OR: 1.66). The BMI model demonstrates exceptional predictive ability (AUC=0.998). In contrast, the BFP model, while still performing well, exhibits a lower AUC (0.975), indicating slightly reduced discriminative power compared to the BMI model. Conclusions:BMI classification demonstrates superior predictive accuracy, specificity, and sensitivity. This suggests that BMI remains a more reliable measure for identifying obesity-related risk factors compared to the BFP model.

Ever since the virus was discovered in the 80s of the 19th century, plant viruses had a great impression within the field of plant pathology. In this book we trace the growth in the research on plant viruses beginning from traditional era (1882-1900) in which a new pathogen was discovered other than fungi and bacteria, later known as virus; biological era (1900-2000) in which the mode of spread and relationship of the virus with its vector was understood; biochemical/biophysical era (1930-1968) that marked the basis of virus purification and chemical characterization; molecular era of plant virology (1943-1995) in which establishment, progression & transmission of plant viruses was described within the host at molecular details ; era of viral molecular genetics (1980–2000), in which a significant role was played by the plant virologists to understand the position and function of genes, methods of viral gene cloning, gene silencing and other viral genome associated properties ; era of plant viral diagnostics (1980 – 2021) which led to rapid, specific and sensitive detection of plant viruses.

COVID-19 (2019-nCoV) is a pandemic disease with an estimated mortality rate of 3.4% (estimated by the WHO as of March 3, 2020). Until now there is no antiviral drug and vaccine for COVID-19. The current overwhelming situation by COVID-19 patients in hospitals is likely to increase in the next few months. About 15 percent of patients with serious disease in COVID-19 require immediate health services. Rather than waiting for new anti-viral drugs or vaccines that take a few months to years to develop and test, several researchers and public health agencies are attempting to repurpose medicines that are already approved for another similar disease and have proved to be fairly effective. This study aims to identify FDA approved drugs that can be used for drug repurposing and identify biomarkers among high- risk and asymptomatic groups. In this study gene-disease association related to COVID-19 reported mild, severe symptoms and clinical outcomes were determined. The high-risk group was studied related to SARS-CoV-2 viral entry and life cycle by using Disgenet and compared with curated COVID-19 gene data sets from the CTD database. The overlapped gene sets were enriched and the selected genes were constructed for protein-protein interaction networks. Through interactome, key genes were identified for COVID-19 and also for high risk and asymptomatic groups. The key hub genes involved in COVID-19 were VEGFA, TNF, IL-6, CXCL8, IL10, CCL2, IL1B, TLR4, ICAM1, MMP9. The identified key genes were used for drug-gene interaction for drug repurposing. The chloroquine, lenalidomide, pentoxifylline, thalidome, sorafenib, pacitaxel, rapamycin, cortisol, statins were proposed to be probable drug repurposing candidates for the treatment of COVID-19. However, these predicted drug candidates need to be validated through randomized clinical trials. Also, a key gene involved in high risk and the asymptomatic group were identified, which can be used as probable biomarkers for early identification.

Long COVID, also referred to as post-COVID-19 condition, describes the lingering after-effects of COVID-19 infection. This complex syndrome encompasses a range of symptoms affecting physical well-being, cognitive function, and mental health. The widespread impact of long COVID extends beyond individual sufferers, placing significant strain on healthcare systems and causing far-reaching socioeconomic consequences. The scale of this multifaceted health crisis is unparalleled in recent history, affecting millions globally and challenging our understanding of post-viral conditions. This study aimed to explore the possibility of targeting G protein-coupled receptors (GPCRs) signaling pathway-related genes as therapeutics for long COVID using computational analysis. Targeting GPCRs and their signaling pathways holds significant promise for long COVID therapeutics, as these receptors are involved in crucial physiological processes including immune regulation, inflammation control, and neurotransmission. All of these are implicated in the diverse and persistent symptoms of long COVID, potentially offering a versatile approach to addressing multiple aspects of the condition simultaneously. This study identified 21 genes associated with GPCRs for further exploration as potential targets. Notably, GPCR genes such as HRAS, KRAS, GNAQ, and GNA11 were determined to be possible therapeutic targets for the pathophysiological mechanisms of long COVID. FDA-approved drugs targeting these genes were identified, including Binimetinib, Cabozantinib, Selumetinib, Panitumumab, Cetuximab, Adagrasib, Tipifarnib, and Sotorasib. Additionally, Naltrexone and Tipifarnib have emerged as potential new treatment options for long COVID. SARS-CoV-2 may hijack GPCR signaling pathways to dysregulate lung ion and fluid transport, contributing to the pathophysiology of pulmonary edema in patients with COVID-19. Further research is required to translate the findings into the development of therapeutics and to investigate the relationship between viral infection and GPCR signalling.

Groove pancreatitis represents a chronic focal form of pancreatitis affecting the zone between the pancreatic head and the duodenal „C” loop, known as the groove area. This is a rare condition that affects the pancreatic periampullary part, including the duodenum and the common bile duct, associated usually with long-term alcohol and tobacco misuse and being more frequent in men than in women. The most common clinical symptoms of groove pancreatitis include weight loss, acute abdominal pain, nausea, and jaundice. This report is about a 66 year old woman with a history of heavy smoking, presenting with weight loss, nausea, and upper abdominal pain. Contrast enhanced computed tomography revealed the existence of a chronic pancreatitis as well as the dilatation of the main pancreatic duct, a cyst of the pancreatic head and enlargement of the biliary tract. Conservatory treatment was initiated but with no improvement of symptoms. Since there was no indication for endoscopic retrograde cholangiopancreatography, we decided to perform a pancreatoduodenectomy, as surgery appears to be the single effective treatment.

Background and Objectives: Knee osteoarthritis (KOA) is a degenerative disease that is targeting continuously people of different ages, but especially the elderly population, the number of which tends to increase continuously at the global level. Apart from age, excess weight can influence the evolution of the disease, with obesity being associated with a weak inflammation stage, and an imbalance between pro-inflammatory and anti-inflammatory cytokines. The present work aimed to identify specific biomarkers alike ACRP-30, IL-10, TNF-α, and IL-6 in synovial fluid and correlate them with patients’ clinical data. Materials and Methods: 24 subjects with KOA and over 50 years of age are involved in the present study. All patients performed a supine X-ray at the target knee. Ultrasound was performed using an ESAOTE MyLab Omega (ESAOTE Spa, Italy) ultrasound device and a 6-19 MHz linear array transducer. Results: The obtained results highlighted a significant correlation between age and adiponectin level (p=0.0451, r=-0.412). Also, the IL-10 values are lower in cases where the intensity of the pain is more pronounced (p=0.0405, r=-0.421). In addition, analyzing the data by gender, it was observed that in the case of males, stiffness is more related to age (p=0.0079, r=0.7993), compared to women (p=0.0203, r=0.6223). In the case of women, the progression of the disease tends to increase more intensively total WOMAC score’s values (p=0.00031, r=0.8342), compared with men (p=0.0289, r=7013). Regarding interleukins and BMI, significant correlations were observed only in the case of men. Conclusions: A significant correlation between age and adiponectin, and adiponectin and IL-6, suggests that advanced age may contribute to adiponectin reduction. Comparing men with women, it was observed that men's age is more related to rigidity, and IL-6 and IL-10 are directly correlated to BMI and women seem to be more sensitive to pain and stiffness.

Although ovarian cystic teratoma is the most common ovarian tumor, complications are quite rare. However, it is important to be recognized by the radiologist in order to avoid inaccurately diagnosing them as malignant lesions. This case report describes a 61-year-old postmenopausal woman, who presented to the emergency room with abdominal pain following a minor blunt abdominal trauma. In this context, a CT scan was performed, which showed the presence of round, hypodense masses randomly distributed in the peritoneum, with coexisting ascites in moderate amount; ovarian carcinoma with peritoneal carcinomatosis was suspected. The patient was hospitalized and an MRI of the abdomen and pelvis was recommended for a more detailed lesion characterization. Following this examination, the patient was diagnosed with mature cystic ovarian teratoma complicated by rupture. Surgery was performed, and the outcome was favorable. The cases of ruptured cystic teratomas are rare, and to our knowledge, this is the first occurrence described in literature. Special attention must be paid when confronting with such a case in medical practice, since it can easily misdiagnosed as peritoneal carcinomatosis

Background/aims: Prior research identified a significant association between heart disease and metabolic dysfunction-associated liver steatohepatitis (MASLD), but the mechanisms are unclear. This study aims to search predictive biomarkers for grade I left ventricular diastolic dysfunction (LVDD), an early stage of heart failure (HF), in type 2 diabetes mellitus (T2DM) patients. Methods: This single-center, prospective observational study screened 73 T2DM patients for grade 1 LVDD and MASLD using 2D echocardiography, tissue analysis, spectral color Doppler, and Fibromax. Results: The study analyzed 50 patients (mean age 58±11.3 years) with a median diabetes duration of 7 years, abdominal obesity (mean BMI 34.4±5.9 kg/m2), and a mean HbA1c of 7.9±1.5%. The prevalence of grade I LVDD, fibrosis, mild steatosis, moderate to severe liver steatosis, mild MASLD, and moderate MASLD was 54%, 44%, 14%, 80%, 43%, and 34%, respectively. Regression analysis revealed that grade 1 LVDD was positively associated with age, Fibrotest, ɑ2-macroglobulin, epicardiac adipose tissue (EAT), and negatively associated with lateral s`, E wave, E/e`, E/A, medium E`, and septal e` (p&lt; 0.05 for all). ɑ2-macroglobulin &gt; 1.92 g/L (AUROC= 0.782, sensitivity 70.4%, specificity 81.2%) and fibrotest score &gt; 0.11 (AUROC 0.766, sensitivity 92.6%, specificity 56.2%) were significant predictors of grade I LVDD. Conclusion: HF is a multifaceted disorder associated with multisystem organ damage, including nonalcoholic MASLD. Although the underlying mechanisms remain unclear, innovative non-invasive biomarkers, such as ɑ2-macroglobulin or fibrotest, could concurrently indicate liver stiffness and the likelihood of grade I LVDD, an early, asymptomatic HF stage in T2DM patients.

This review aims to explore the intricate relationship between epigenetic mechanisms, stress, and affective disorders, focusing on how early life experiences and coping mechanisms contribute to susceptibility to mood disorders. Epigenetic factors play a crucial role in regulating gene expression without altering the DNA (deoxyribonucleic acid) sequence, and recent research has revealed associations between epigenetic changes and maladaptive responses to stress or psychiatric disorders. A scoping review of 33 studies employing the PRISMA-S (Preferred Reporting Items for Systematic Reviews and Meta-Analyses – Statement) guidelines investigates the role of stress-induced epigenetic mechanisms and coping strategies in affective disorders' occurrence, development, and progression. The analysis encompasses various stress factors, including childhood trauma, work-related stress, and dietary deficiencies, alongside epigenetic changes such as DNA methylation and altered gene expression. Findings indicate that specific stress-related genes frequently exhibit epigenetic changes associated with affective disorders. Moreover, the review examines coping mechanisms in patients with bipolar disorder and major depressive disorder, revealing mixed associations between coping strategies and symptom severity. While active coping is correlated with better outcomes, emotion-focused coping may exacerbate depressive or manic episodes. Overall, this review underscores the complex interplay between genetic predisposition, environmental stressors, coping mechanisms, and affective disorders. Understanding these interactions is essential for developing targeted interventions and personalized treatment strategies for individuals with mood disorders. However, further research is needed to elucidate specific genomic loci involved in affective disorders and the clinical implications of coping strategies in therapeutic settings.

Chronic kidney disease patients treated by hemodialysis present a high cardio-vascular morbidity and mortality. There is an imperative need for novel biomarkers in identifying and to offer possible therapeutically intervention in these patients. We performed a prospective observational cohort study on 77 patients in the period October 2021 – October 2023. We measured serum plasma levels of Interleukin 1-Beta, Galectin 3, human suppression of tumorigenicity factor 2, bone morphogenetic protein 2 and fibroblastic growth factor 23 at the inclusion. We evaluated the correlations of these biomarkers with cardiac function and structure evaluated by echocardiography. Mean age was 61.02 (±11.81) years, 45 (56.2%) males and with a dialysis vintage of 4.95 (2.4-7.8) years. Median ejection fraction was 51 (43-54 %) and more than two thirds of the patients presented valvular calcifications. Overall mortality was 22%. Interleukin 1-Beta correlated positively with ejection fraction and global longitudinal strain and negatively with left atrium diameter and left ventricle tele systolic diameter. Galectin 3 values negatively correlated with aortic valve fibrosis and mitral valve calcifications and human suppression tumorigenity factor 2 negatively correlated with mitral valve calcifications. Some of these novel biomarkers could be used to better asses the cardio-vascular disease in patients on maintenance hemodialysis.

Background: Removable prostheses have seen a fundamental change recently because of advances in polymer materials, allowing improved durability and performance. Despite these advancements, notable differences still occur amongst various polymer materials and processing technologies, requiring a thorough grasp of their mechanical, physical, and therapeutic implications. Methods: Fours different processing techniques, all utilizing Polymethyl methacrylate (PMMA) as the main material, were used to construct complete dentures. The specimens underwent a mechanical test. Results: All the specimens broke under the influence of a certain force, rather than yielding through flow, as is characteristic for plastic materials. For each specimen, the maximum force (N) was recorded (figure 4), as well as the breaking energy, Conclusions: The best results were obtained in the case of the thermoplastic injection system and, surprisingly and contrary to those in the specialized literature, the worst results were recorded in the case of 3D printed samples.

Background and objective: Iliopsoas abscess (IPA) is a rare condition with varied symptomology and etiology. Less than one-third of patients with IPA present on the emergency department (ED) admission with the traditional triad of fever, back pain, and restricted hip motion (or limp), leading to delays in diagnosis and management. This case report aims to bring awareness among healthcare professionals regarding the atypical presentation and introduce a potential differential diagnosis when evaluating patients with fever and back pain. The intention is to provide insights into the signs and symptoms that may indicate the presence of an iliopsoas abscess and prompt additional investigations. Case report: Here, we describe a case of primary iliopsoas abscess associated with drug-induced acute liver failure in our ED. The patient complained of pain in the left lumbar region and fatigue that started two weeks before this presentation, claiming that, during the previous night, the pain suddenly worsened. She had a fever, pain at palpation in the right hypochondriac and left lumbar regions, accompanied by fever and vomiting, jaundiced skin, and sclera at the examination in ED. On abdominal ultrasonography, the diagnosis of acute cholangitis was suspected. The laboratory test shows leukocytosis with neutrophilia, thrombocytosis, hepatocytolysis syndrome, and hyperbilirubinemia with the predominance of indirect bilirubin. We performed then abdominal and pelvic computed tomography, which confirmed the diagnosis of cholelithiasis observed with the diameter of the bile duct within normal limits but also showed an abscess-like collection fused to the interfibrillar level of the left iliopsoas muscle; a diagnosis we most likely would have been missed. After that, the patient was hospitalized in the General Surgery Department, and surgery drainage of the abscess was performed. The patient's evolution was excellent; she was discharged after 11 days. Conclusions: The case presented here exemplifies how iliopsoas abscess, a rare cause of back pain, can quickly go unrecognized, especially in the emergency department. Our experiences will raise awareness among doctors in emergency departments about this uncommon but essential diagnosis. With advancements in diagnostic tools and techniques, we aspire that more cases of iliopsoas abscess will be accurately diagnosed.

Background: Acute kidney disease (AKD) is a known risk factor for increased mortality and evolution towards chronic kidney disease (CKD) in adults. The data regarding children are scarce. The purpose of our study is to explore the risk factor for AKD and impact of AKD following an acute kidney injury (AKI) episode in children on mortality and new-onset CKD. Methods: We retrospectively evaluated 736 children with ages between 2 and 18 years old with AKI admitted in a tertiary care hospital from the west Romania over a 9 years period. Results: AKD incidence following AKI was 17%. Patients that developed AKD were older, with higher baseline serum creatinine values, urea, C reactive protein and lower proteins, haemoglobin and sodium. They were more often exposed to sepsis, critical illness and nephrotoxic medications and presented certain susceptibilities like CKD, neoplasia, anemia, heart failure, arterial hypertension, stem cell transplant and female gender. AKI causes that increased the risk of AKD were intrinsic ones. AKD increased the overall mortality by three times. The factors that independently increased the risk of evolution towards CKD were AKD, acute tubular necrosis and higher baseline serum creatinine values. Conclusions: AKD is an independent risk factor for mortality and new-onset CKD in children.

The new coronavirus (2019-nCoV) in Wuhan has caused virus outbreaks in many provinces and cities in China, and several neighboring countries were also affected. In recent years, coronavirus several outbreaks around the world were reported, however researchers could not predict its onset. Coincidentally, the birthplace of another coronavirus (SARS-CoV) that causes serious public health problems is also in China. This review compares and analyzes the external environment, natural hosts, intermediate hosts, and susceptible populations when these two coronaviruses occurred. Based on the analysis results, we found that the 2019-nCov virus outbreak in Wuhan was not an accidental phenomenon, but a result of a combination of factors. At the same time, through the conclusions of these analyses, we will be able to get a glimpse of the trajectories of new coronaviruses and curb the virus outbreak in future.

Pregnancy is a complex biological process which is associated with significant metabolic and hemodynamic changes of the maternal body which allow a normal fetal growth. The inability to adapt to these changes can affect the cardiovascular health. During pregnancy, women may experience mood swings, anxiety and emotional ambivalence. These symptoms can lead to stress and have a negative impact on the mental well-being of expectant mothers. It is crucial to know the aspects that can influence the development of cardiovascular problems among pregnant women in order to manage these cases properly. The current narrative review explores and summarizes the evidence regarding the impact of stress and anxiety on cardiovascular health in pregnant women. Understanding the physiological adaptations that the body undergoes throughout pregnancy is crucial for educating and supporting individuals as well as healthcare professionals. This enables them to recognize any risk factors or pathological conditions that may exacerbate pregnant women's health. In order to reduce maternal morbidity and mortality from cardiovascular disease, a psychological and cardiovascular risk factor screening that is done either before or during pregnancy may be able to identify situations that call for particular medical and/or psychological therapies.

Sepsis still remains one of the most difficult conditions to diagnose as it presents unspecific clinical signs and symptoms, which makes early detection crucial. Our study aimed to enhance the accuracy of sepsis diagnosis by integrating multimodal monitoring technologies with traditional diagnostic methods. The research involved 121 newborns, comprising 39 cases of late-onset sepsis, 35 cases of early-onset sepsis, and 47 control subjects. Biosignals including pulse oximetry (PO), near-infrared spectroscopy (NIRS), and skin temperature (ST) were continuously monitored and an algorithm was developed using Python for identifying early signs of sepsis. The model demonstrated the ability to detect sepsis 6 to 48 hours in advance with an accuracy rate of 87.67% ± 7.42%. Sensitivity and specificity were recorded at 76% and 90%, respectively, with NIRS and ST having the most significant impact on predictive accuracy. Despite the promising results, limitations such as sample size, data variability, and potential biases were noted. The findings highlight the critical role of non-invasive biosensing methods in conjunction with traditional biomarkers and cultures, offering a robust framework for early sepsis detection and improved neonatal care. Further studies are recommended to validate these results across diverse clinical settings.

Synchronous electrical cardioversion is a relatively common procedure in the emergency department (ED), often performed for unstable supraventricular tachycardia (SVT) or unstable ventricular tachycardia (VT). However, it is also used for stable cases resistant to drug therapy, which carry a risk of deterioration. In addition to the inherent risks linked with procedural sedation, there is a possibility of malignant arrhythmias or bradycardia, which could potentially result in cardiac arrest following this procedure. Here, we present a case of complete heart block unresponsive to transcutaneous pacing and positive inotropic and chronotropic drugs for 90 minutes, resulting in multiple cardiac arrests. The repositioning of the transcutaneous cardio-stimulation electrodes, one of them placed in the left latero-sternal position and the other at the level of the apex, led to immediate stabilization of the patient. Extubation of the patient was performed the following day, while full recovery and discharge within 7 days, after insertion of a permanent pacemaker.

In recent years, the Canadian dairy sector has faced escalating challenges due to its significant contribution to greenhouse gas emissions, particularly methane. This paper critically examines a spectrum of innovative techniques aimed at mitigating methane emissions within this sector, scrutinizing their cost-effectiveness, efficiency, compatibility with animal welfare standards, and adherence to both existing and prospective Canadian environmental legislations. The discourse commences with an exhaustive overview of contemporary methane reduction methodologies pertinent to dairy farming, followed by a rigorous analysis of their economic feasibility. This includes a detailed cost-benefit analysis, juxtaposed with the efficiency and technological advancements these techniques embody. A pivotal aspect of this examination is the alignment of animal welfare with emission reduction objectives, ensuring that strategies employed do not compromise the health and well-being of dairy cattle. Furthermore, the paper delves into the legislative landscape of Canada, evaluating the congruence of these techniques with current environmental laws and anticipating future regulatory shifts. Performance indicators for emission reduction are critically assessed, establishing benchmarks tailored to the Canadian context. This is complemented by an exploration of the market potential of these innovations, including factors influencing their adoption and scalability in the market. The analysis culminates with a synthesis of case studies and best practices within Canada, offering insights into successful implementations and drawing lessons for future endeavors. This comprehensive approach serves not only to address the immediate environmental and health impacts associated with dairy farming emissions but also contributes significantly to the overarching goal of sustainable development in the agricultural sector.

In the wake of rapid advancements in artificial intelligence (AI) and sensor technologies, a new horizon of possibilities has emerged across diverse sectors. Livestock farming, a domain often sidelined in conventional AI discussions, stands at the cusp of this transformative wave. This paper delves into the profound potential of AI and sensor innovations in reshaping animal welfare in livestock farming, with a pronounced emphasis on a human-centric paradigm. Central to our discourse is the symbiotic interplay between cutting-edge technology and human expertise. While AI and sensor mechanisms offer real-time, comprehensive, and objective insights into animal welfare, it's the farmer's intrinsic knowledge of their livestock and environment that should steer these technological strides. We champion the notion of technology as an enhancer of farmers' innate capabilities, not a substitute. Our manuscript sheds light on: Objective Animal Welfare Indicators: An exhaustive exploration of health, behavioral, and physiological metrics, underscoring AI's prowess in delivering precise, timely, and objective evaluations. Farmer-Centric Approach: A focus on the pivotal role of farmers in the adept adoption and judicious utilization of AI and sensor technologies, coupled with discussions on crafting intuitive, pragmatic, and cost-effective solutions tailored to farmers' distinct needs. Ethical and Social Implications: A discerning scrutiny of the digital metamorphosis in farming, encompassing facets like animal privacy, data safeguarding, responsible AI deployment, and potential technological access disparities. Future Pathways: Advocacy for principled technology design, unambiguous responsible use guidelines, and fair technology access, all echoing the fundamental principles of human-centric computing and analytics. In essence, our paper furnishes pioneering insights at the crossroads of farming, animal welfare, technology, and ethics. It presents a rejuvenated perspective, bridging the chasm between technological advancements and their human beneficiaries, resonating seamlessly with the ethos of the Human-Centric Intelligent Systems journal. This comprehensive analysis thus marks a significant stride in the burgeoning domain of human-centric intelligent systems, especially within the digital livestock farming landscape, fostering a harmonious coexistence of technology, animals, and humans.

In response to escalating global demand for poultry, the industry grapples with an array of intricate challenges, from enhancing productivity to improving animal welfare and attenuating environmental impacts. This comprehensive review explores the transformative potential of digital phenotyping, an emergent technological innovation at the cusp of dramatically reshaping broiler production. The central aim of this study is to critically examine digital phenotyping as a pivotal solution to these multidimensional industry conundrums. Our investigation spotlights the profound implications of 'digital twins' in the burgeoning field of broiler genomics, where the production of exact digital counterparts of physical entities accelerates genomics research and its practical applications. Further, this review probes into the ongoing advancements in the research and development of a context-sensitive, multimodal digital phenotyping platform, custom-built to monitor broiler health. I critically evaluate this platform's potential in revolutionizing health monitoring, fortifying the resilience of broiler production, and fostering a harmonious balance between productivity and sustainability. Subsequently, the paper provides a rigorous assessment of the unique challenges that may surface during the integration of digital phenotyping within the industry. These span from technical and economic impediments to ethical deliberations, thus offering a comprehensive perspective. The paper concludes by highlighting the game-changing potential of digital phenotyping in the broiler industry and identifying potential future directions for the field, underlining the significance of continued research and development in unlocking digital phenotyping's full potential. In doing so, it charts a course towards a more robust, sustainable, and productive broiler industry. The insights garnered from this study hold substantial value for a broad spectrum of stakeholders in the broiler industry, setting the stage for an imminent technological evolution in poultry production.

Current commercial, pre-commercial, and experimental in ovo techniques for the sex determination of fertilised eggs employ either minimally invasive biomolecular assays (extracting fluid via a small laser-drilled window in the eggshell, for detection of genetic or hormonal biomarkers), analysis of volatile compounds emitted from the eggshell, visible imaging, and reflectance or transmission spectroscopic analysis exploiting molecular optical fluorescence, polarisation, and scattering phenomena, including various combinations of these modalities. , to date no endeavour employing the NIR and FTIR based spectroscopic techniques has resulted in a commercially sustainable solution to the egg sexing problem. Besides achieving only subpar performance in overall accuracy, specificity, and sensitivity, the least invasive of the current state-of-the-art optical methods still requires, creating a transmission window (fenestration) of 12–15 mm diameter through to the mammillae layer of the shell, proximal to the external shell membrane, which can affect the incubation or post-hatch development viability of up to 10% of incubated eggs. Multimodal solution combining Raman spectroscopy and hyperspectral imaging has strong prospects to overcome the hard barriers existing before the perfection of a non-invasive in-line process for high reliability and rapid throughput for sex determination of eggs within 3 days of incubation. The method for sexing of chicken embryos needs to take a multipronged approach in collecting and analyzing spectral data that points to biomarkers using the machine learning approaches to look for nanomolar to picomolar concentrations of these in the fluid.

Emotions or affective states recognition in farm animals is an underexplored research domain. Despite significant advances in the animal welfare research, the animal affective computing through the development and application of devices and platforms that can not only recognize but interpret and process the emotions, are in nascent stage. By capitalizing on the immense potential of biometric sensors, the artificial intelligence enabled big data methods substantially offers advancement of animal welfare standards and meet the urgent need of caretakers to respond effectively to maintain the wellbeing of their animals. Farm animals, numbering over 70 billion worldwide, are increasingly managed in large-scale, intensive farms. With both public awareness and scientific evidence growing that farm animals experience suffering, as well as affective states such as fear, frustration and distress, there is an urgent need to develop efficient and accurate methods for monitoring their welfare. At present, there are no scientifically validated ‘benchmarks’ for quantifying transient emotional (affective) states in farm animals, and no established measures of good welfare, only indicators of poor welfare, such as injury, pain and fear. Conventional approaches to monitoring livestock welfare are time consuming, interrupt farming processes and involve subjective judgments. Biometric sensors data enabled by Artificial Intelligence are an emerging smart solution to unobtrusively monitoring livestock, but their potential for quantifying affective states and groundbreaking solutions in their application are yet to be realized. This review provides innovative methods for collecting big data on farm animal emotions, which can be used to train artificial intelligence models to classify, quantify and predict affective states in individual pigs and cows. Extending this to the group level, social network analysis can be applied to model emotional dynamics and contagion among animals. Finally, ‘digital twins’ of animals capable of simulating and predicting their affective states and be-havior in real time are a near-term possibility.

The demand for animal products is expected to continue to rise, which requires the development of efficient livestock farming systems. Environmental, societal and economic concerns regarding this industry are however accumulating, addressing the large resource demand, pollutants and greenhouse gas emissions and health concerns that the livestock industry is responsible for. Precision livestock farming systems allow the continuous automatic monitoring of various physiological, behavioural and phenotypic parameters of animals in order to increase productivity and animal welfare while controlling and minimizing the environmental impact. There is a high potential for digital farming to be the solution for responsibly and ethically feeding the growing and urbanizing population. However, many problems and concerns are still present in this developing industry and remain relatively unaddressed, starting with the ethical aspects in regard to the animal, including its objectification, human-animal relationships and welfare and ending with the societal implications of this digitalization. Concrete frameworks, inter-disciplinary studies and global legislation need to be put in place in order to ensure the safety and protection of the animals, farmer and society. Here, implications of digital farming for the animals, farmers, society and the planet are critically reviewed with the future outlook of digital farms.

Deepfake technologies are known for the creation of forged celebrity pornography, face and voice swaps, and other fake media content. Despite the negative connotations the technology bears, the underlying machine learning algorithms have a huge potential that could be applied to not just digital media, but also to medicine, biology, affective science, and agriculture, just to name a few. Due to the ability to generate big datasets based on real data distributions, deepfake could also be used to positively impact non-human animals such as livestock. Generated data using Generative Adversarial Networks, one of the algorithms that deepfake is based on, could be used to train models to accurately identify and monitor animal health and emotions. Through data augmentation, using digital twins, and maybe even displaying digital conspecifics where social interactions are enhanced, deepfake technologies have the potential to increase animal health, emotionality, sociality, animal-human and animal-computer interactions and thereby animal welfare, productivity, and sustainability of the farming industry.

The world's growing population is highly dependent on animal agriculture. Animal products provide nutrient-packed meals that help to sustain individuals of all ages in communities across the globe. As the human demand for animal proteins grows, the agricultural industry must continue to advance its efficiency and quality of production. One of the most commonly farmed livestock is poultry and their significance is felt on a global scale. Current poultry farming practices result in the premature death and rejection of billions of chickens on an annual basis before they are processed for meat. This loss of life is concerning regarding animal welfare, agricultural efficiency, and economic impacts. The best way to prevent these losses is through the individualistic and/or group level assessment of animal on a continuous basis. On large-scale farms, such attention to detail was generally considered to be inaccurate and inefficient, but with the integration of Artificial Intelligence (AI) assisted technology individualized and per-herd assessments of livestock are possible and accurate. Various studies have shown cameras linked with specialized systems of AI can properly analyze flocks for health concerns, thus improving the survival rate and product quality of farmed poultry. Building on the recent advancements, this review explores the aspects of AI in the detection, counting and tracking of the poultry in commercial and research-based applications.

Despite recent scientific advancements, there is a gap in the use of technology to measure signals, behaviors, and processes of adaptation physiology of farm animals. Sensors present exciting opportunities for sustained, real-time, non-intrusive measurement of farm animal behavioral, mental, and physiological parameters with the integration of nanotechnology and instrumentation. This paper critically reviews the sensing technology and sensor data-based models used to explore biological systems such as animal behavior, energy metabolism, epidemiology, immunity, health, and animal reproduction. The use of sensor technology to assess physiological parameters can provide tremendous benefits and tools to overcome and minimize production losses while making positive contributions to animal welfare. Of course, sensor technology is not free from challenges; these devices are at times highly sensitive and prone to damage from dirt, dust, sunlight, colour, fur, feathers, and environmental forces. Rural farmers unfamiliar with the technologies must be convinced and taught to use sensor-based technologies in farming and livestock management. While there is no doubt that demand will grow for non-invasive sensor-based technologies that require minimum contact with animals and can provide remote access to data, their true success lies in the acceptance of these technologies by the livestock industry.

As the haze engulfed the Delhi NCR after Diwali there is a panic in public about its causes and consequences. Here are some facts and figures to know the truth behind the scene. Temperature fell down from 24’C on October 31 to 21’C on November 2 as recorded at UPPCB station at sector-125, Noida which is adjacent to Delhi a very strategic location and can be considered as reference point for Delhi-NCR. The details of the study conducted is given below.

Genome-wide epigenetic changes in plants are being reported during the development and environmental stresses, which are often correlated with gene expression at the transcriptional level. Sum total of the biochemical changes in nuclear DNA, post-translational modifications in histone proteins and variations in the biogenesis of non-coding RNAs in a cell is known as epigenome. These changes are often responsible for variation in expression of the gene without any change in the underlying nucleotide sequence. The changes might also cause variation in chromatin structure resulting into the changes in function/activity of the genome. The epigenomic changes are dynamic with respect to the endogenous and/or environmental stimuli which affect phenotypic plasticity of the organism. Both, the epigenetic changes and variation in gene expression might return to the pre-stress state soon after withdrawal of the stress. However, a part of the epigenetic changes may be retained which is reported to play role in acclimatization, adaptation as well as in the evolutionary processes. Understanding epigenome-engineering for improved stress tolerance in plants has become essential for better utilization of the genetic factors. This review delineates the importance of epigenomics towards possible improvement of plant’s responses to environmental stresses for climate resilient agriculture.

Post severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2, Covid-19) sequalae’s, immune and RNA based treatments are associated with the development of micro-stones and chronic kidney disease (CKD). In addition, circulating cytokines cocktail and viral envelope spike protein (SP) instigates CKD. During epigenetic regulation, Covid-19 methyltransferase (MT) methylates the DNA/RNA/Proteins/Histones and generates homocysteine (Hcy). Although cysteine promotes the growth and aggregation of calcium oxalate crystals in normal undiluted human urine. However, during elevated levels of Hcy (i.e., hyperhomocysteinemia, HHcy) due in part to the impaired transsulfuration (i.e., mitochondrial sulfur metabolism) pathways, homocysteine is the only sulfur-denoting non-protein amino acid may promote crystals. HHcy is associated with severity of Covid-19 infection and pneumonia post Covid-19. It is unclear however whether the HHcy facilitates homocysteine crystals. Although an E. coli strain degrades tricarboxylic acid (TCA, citric acid), and disrupts mitochondrial TCA cycle, to dicarboxylic acid (DCA, oxalic acid), DCA also promotes calcium oxalate crystals and may contributes to kidney stones, it is unclear whether gut-dysbiosis by Covid-19 infection contributes to kidney stones. There is robust succinylation of enzymes in TCA cycle which causes dysfunctional mitochondrial bioenergetics during Covid-19 infection. In this regards interaction between SIRT5 and mitochondrial transsulfuration during Covid-19 infection attenuates TCA cycle. Interestingly, the mitochondrial bioenergetics though sulfur metabolism participate in TCA cycle and epigenetic acetylation/succinylation, therefore, Covid-19 infection transition acute kidney injury (AKI) to CKD by defective mitochondria sulfur metabolism (transsulfuration) pathways, forming uricemia micro stones.

The incidence of diabetes mellitus (DM) continues to rise worldwide and one of the most serious microvascular complications is diabetic kidney disease (DKD), which is the leading cause of end-stage renal disease. Current bi-omarkers such as urinary albumin excretion rate have limitation for early detection of DKD. In our study we used ultra-high-performance liquid chromatography coupled with electrospray ionization-quadrupole-time of flight-mass spectrometry (UHPLC-QTOF-ESI+-MS) techniques to quantify previously analyzed metabolites, such as tryptophan, kynurenic acid, taurine, l-acetylcarnitine, glycine, and tiglylglycine. We performed the targeted analy-sis of metabolites from urine and serum samples, collected from 110 subjects. Of these, 90 patients with type 2 DM (T2DM) were divided according to the urinary albumin/creatinine ratio (UACR) into normoalbuminuria 300 mg/g groups, respectively, while 20 subjects were rep-resented by healthy controls. Through various validation methods, we identified several metabolites as potential biomarkers, such as kynurenic acid, glycine, and l-tryptophan in serum and l-acetylcarnitine, tiglylglycine, and tau-rine in urine.

Lynch syndrome (LS) is one of the most common hereditary cancer syndrome in human populations, associated with germline variants in MLH1, MSH2/EPCAM, MSH6 and PMS2 genes. The advent of next generation sequencing has proven a significant impact in germline variant detection in the causative genes; however, a large proportion of patients with clinical criteria still receive uncertain or negative results. PMS2 is the least frequent reported gene, associated with up to 15% of LS cases with late-onset disease and low penetrance phenotype; however, the proportion of PMS2-LS cases is considered to be highly underestimated. In this context, we consulted public available databases (ClinVar, LOVD) for missense and intronic PMS2 variants and performed an in silico analysis of the genomic sequence. Splicing bioinformatics tools proven effective in other genes were used to assess both the wild-type DNA sequence and reported genetic variants. Splicing alterations were predicted in silico and using GTEx short-read RNA expression data. Out of the 2384 missense variants discovered, 90% were classified with uncertain significance (VUS). 4.9% of missense variants were shown to have a potential splicing consequence (DS &gt; 0.2) using SpliceAI. As described in the original publication, SpliceAI-visual was proven effective in annotation of short intronic variants (&lt; 50 bp). Four short intronic variants were identified using SpliceAI-visual as potentially splicing disturbing, in spite of using a lower threshold (DS &gt; 0.1). Moreover, we observed that Splicing Regulatory Elements (SREs) may play a fundamental role in alternative splicing in PMS2 gene, ESE motifs being overrepresented in highly expressed exons 5, 11 and 14. Computational analysis performed in our study serves as a valuable filtering step for guiding further RNA sequencing experiments. Additional functional data is necessary to evaluate the significance of our findings.

The chronic high fat dysbiosis diet (HFD) instigates cardiac remodeling, dysfunction and chronic heart failure (CHF). The therapeutic approaches focusing oxidative stress, inflammation and hyperlipidemia have not yielded significant positive clinical outcomes. Recent studies report that although mitochondrial ATP citrate lyase (ACLY) supports cardiac function, however, decreases more in human HFpEF than HFrEF. Interestingly, the ACLY synthesizes lipids and creates hyperlipidemia. Epigenetically, ACLY acetylates histone. The mechanism(s) are unknown. One hypothesis is that the HFD induces epigenetic folate 1-carbon metabolism (FOCM), heperhomocysteinemia (HHcy, a cause of abrogation of sleep-time dipping in blood pressure and hypertension) and heart failure. Interestingly, the probiotics utilizes the fat/lipids post-biotically, and increases the mitochondrial bioenergetics and attenuates the HFD induced heart failure. We suggest a novel and paradigm-shift epigenetic mitochondrial sulfur transsulfuration pathways that selectively target HFD-induced CHF. The HFD via transport and metabolomic pathways instigates cardiac remodeling and dyfunction. Previous studies from our laboratory, using a single cell analysis, revealed increase in transporter (SLC25A) of s-adenosine-methionine (SAM) during elevated levels of homocysteine (Hcy, i.e., hyperhomocysteinemia, HHcy), a consequence of impaired epigenetic recycling of Hcy back to methionine by increase in FOCM methylation of H3K4, K9, H4K20 and gene writer (DNMT) and decrease in eraser (TET/FTO). The Hcy transported to mitochondria by SLC7A for clearance via sulfur metabolomic transsulfuration by 3-mercaptopyruvate sulfur transferase (3MST). Interestingly, the HHcy decreases ATP, OCR, H₂S, PGC1α, TFAM, and increase in fission/fusion ratio, causing mitochondria dysfunction. We suggest that gut dysbiosis by HFD disrupts rhythmic epigenetic memory via FOCM and increase in DNMT1 and creates HHcy, leading to decrease mitochondrial transsulfuration and bioenergetics. The treatment with a probiotic, lactobacillus will eat the fat/lipids post-biotically and bi-directionally will produce folic acid and lactone-ketone body that will mitigate the HFD-induced mitochondrial remodeling and heart failure.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes an outbreak of COVID-19 disease in humans with the aid of spike protein. It consists of a receptor-binding domain (RBD) that recognizes and binds to the host receptor angiotensin-converting enzyme 2 (ACE2). The aim of this study was to examine the mutational effect of spike protein on the sequence through an interaction study of the mutant spike protein and the human ACE2 protein at the structural level. A total of 17,227 spike proteins from Asia, Africa, Europe, Oceania, South America, and North America were compared to the Wuhan spike protein reference sequence (Wuhan-Hu-1). The structural and stability implications of D614G, N501Y, and S477N mutations were evaluated. The binding affinity between mutated RBD and human ACE2 protein was also studied. The D614G mutation may have originated in Germany, Europe based on the date of the first sample collection report. It is now widely circulated all over the world with most occurrences in North America. The mutations N501Y and S477N may have originated from Oceania based on the date of the first sample collection report and also have the highest occurrences in Oceania. Based on the computational analysis of mutational effects, the D614G, N501Y, and S477N mutations decreased stability and were tolerated. For disease propensity prediction, N501Y was more prone to disease compared to D614G, while S477N was not prone to disease. The mutation of D to G at position 614 and S to N at position 477 for secondary structure prediction shows no changes in secondary structure while remaining in the coil region, whereas the mutation of N to Y at position 501 changes from coil structure to extended strand. N501Y mutation has a higher affinity to human ACE2 protein compared to D614G and S477N based on a docking study. D614G spike mutation was identified to exist between the two hosts based on a comparison of SARS-CoV-2 derived between the mink and human. Further research is needed on the link between the mink mutation N501T and the mutation N501Y in humans, which has evolved as a separate variant.

Long COVID, characterized by persistent symptoms lasting from weeks to several months following initial infection with Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), significantly impacts individuals' quality of life and poses a potential long-term burden on global healthcare systems. Clinical manifestations of Long COVID are diverse, affecting multiple systems including respiratory, cardiovascular, neurological, and gastrointestinal. While the precise mechanisms underlying Long COVID remain unclear, emerging evidence suggests involvement of various bodily pathways. This study employs bioinformatics tools to investigate the mechanisms of Long COVID and identify potential drug targets associated with key pathways, including inflammation, cytokines, immune response, coagulation, mitochondrial function, oxidative stress, and autonomic dysfunction. Tools such as the National Center for Biotechnology Information (NCBI) database, PubChem, UniProt, and iGEMDOCK were utilized for pathway analysis and molecular docking. The analysis aimed to identify drug molecules that may interact with target proteins to regulate or inhibit pathway activity, yielding several potential drug candidates for further investigation.

This randomized control trial aims to test the hypothesis that twice-daily nurse-administered saline enema(SE) is comparable to physician-administrated SE in very low birth weight infants (VLBW) with meconium obstruction of prematurity (MOP). MOP is defined as no bowel opening for 48 h, and treatment failure is defined as the need for additional intervention and safety. Twice-daily SE was administered by accredited nurses, surgeons/neonatologists using standardized protocols. The outcomes were comparability of nurse to physician-administered SE in terms of frequency of administration, catheter insertion length and volume of enema, daily maximum meconium output, and treatment failure, defined as the need for additional intervention. Safety outcomes were evaluated. Twenty-eight infants were managed with SE. 389 SE were administered, 96 and 293 in 750-999g and 1000-1500g birth weight groups, respectively. Nurses administered 72.9% and 81.5% of SE in 750-999g and 1000-1500g birth weight groups, respectively. Physicians performed the rest of the SE. Daily meconium output was comparable in nurse and physician-administered SE in both groups. There were no treatment failures or adverse events in both SE group. SE administered by nurses was found to be comparable in terms of safety, efficacy, and frequency of administration to that of physician-administered SE.

Long COVID is the collective term used to describe the persistence of symptoms in individuals who have recovered from SARS-CoV-2 infection. Some people who have healed from COVID-19 continue to experience new or persistent symptoms that last for weeks or months. While Long COVID is becoming more widely recognized, its fundamental causes and effective therapies remain largely unclear. This study aims to explore the potential of phytochemical medications as a cutting-edge therapeutic strategy for treating Long COVID, also known as post-acute sequelae of SARS-CoV-2 infection (PASC).G protein-coupled receptors (GPCRs), which play a critical role in cell signaling, were the focus of this study. GPCRs are known to be involved in immunological and inflammatory responses and are considered potential targets for Long COVID therapy. The GPCRs examined included chemokine receptors, serotonin receptors, adrenergic receptors, and histamine receptors. The docking results demonstrated high binding affinity between phytochemical drug structures and these receptors for most drug-receptor combinations.The study consists of three main parts. First, data on receptors and drugs were retrieved from the Protein Data Bank (PDB) and PubChem, respectively. The receptors selected are those predicted to be linked to Long COVID. The structure of the drugs was converted from SDF to PDB format using OpenBabel from ChemInfo. Docking studies were performed to assess the binding affinity between the drugs (ligands) and their respective receptors (binding sites) using iGemDock v2.1.In conclusion, the findings of this study could have significant implications for the development of alternative therapies for Long COVID.

Autosomal dominant polycystic kidney disease (ADPKD) is an inherited disorder characterized by renal tubular cystic dilatations. The cysts can develop anywhere along the nephron and over time the cystic dilatation lead to kidney enlargement. On the other hand, the cysts begin to reduce the number of functional nephrons as a consequence of cystic expansion that further contributes to the decline in renal function over the years. The pressure exerted by the dilated cysts lead to compensatory mechanisms that further contribute to the decline in renal function. These structural changes are responsible of glomerular hyperfiltration states, albuminuria, proteinuria, and haematuria. However, the presentation of ADPKD varies in children from a completely asymptomatic child with incidental ultrasound detection of cysts to a rapidly progressive disease. There have been reports of early onset of ADPKD in children younger than 2 years that showed a more rapid decline in renal function. ADPKD is caused by mutation in PKD1 and PKD2 genes. Today, the PKD1 gene mutation seems to account for up to 85% of the cases worldwide, and it is associated with worse renal outcomes. Individuals with PKD2 gene mutation seem to present a milder form of the disease, with a more delayed onset of end stage kidney disease. The cardinal sign of ADPKD is the presence of renal cysts during renal ultrasound. The current guidelines provide the clinicians the recommendations for genetic testing in children with a positive family history. Given that the vast majority of children with ADPKD present with normal or supra normal kidney function we explored the glomerular filtration rates dynamics and the renal ultrasound adjusted percentiles. 14 out of 16 patients had kidney percentile over 90%. The gene mutations were equally distributed among our cohort. In addition, we compared the modified Schwartz formula to the quadratic equation after adjusting the serum creatinine measurements. It seems that even though children with ADPKD have enlarged kidneys, the renal function is more likely normal or near normal when the quadratic estimation of glomerular filtration rate is used (qGFR tended to be lower, 111.95 ± 12.43 ml/min/1.73m2 compared to Schwartz eGFR 126.28 ± 33.07ml/min/1.73m2, p=0.14). Also, when the quadratic equation was employed, not even a patient reached the glomerular hyperfiltration thresholds. The quadratic formula showed that glomerular filtration rates are linear or slightly decreasing after 1-year of follow-up (Quadratic ΔeGFR=0.32±5.78ml/min/1.73m2) as opposed to Schwartz formula that can falsely classify children in a hyperfiltration state ΔeGFR=7.51±19.46ml/min/1.73m2), p=0.019.

Aim: The purpose of this study is to evaluate the remineralization potential of primary teeth enamel after being exposed to different laser diode therapy. Methods: Ninety-six vestibular primary teeth enamel samples were divided into eight groups (n=12) with varying treatments: control (G1), CPP-ACP-fluoride varnish (G2), diode lasers at 980 nm (G3), 808 nm (G4), 450 nm (G5), 980 nm + CPP-ACP-fluoride varnish (G6), 808 nm + CPP-ACP-fluoride varnish (G7), and 450 nm + CPP-ACP-fluoride varnish (G8). Each sample was assessed using DIAGNOdent® at baseline, post-treatment, and post-pH cycle re-mineralization. SEM imaging was performed before and after treatment and following the pH cycle. Results: The results indicated that the 980 nm and 808 nm diode lasers, both alone and in combination with CPP-ACP-fluoride varnish, either maintained or increased the calcium (Ca) weight percentage (Wt%) in the enamel. The 980 nm diode laser combined with CPP-ACP-fluoride varnish (G6) showed a significant increase in Ca Wt%, suggesting a strong remineralization effect. Similarly, the 808 nm diode laser alone (G4) also promoted a substantial increase in Ca Wt%. In contrast, the 450 nm diode laser, whether applied alone or in combination with CPP-ACP-fluoride varnish, resulted in a lower Ca Wt% and an increase in phosphorus (P) Wt%. Most groups, except for the CPP-ACP-fluoride varnish alone (G2), demonstrated an increase in P Wt%, indicating a complex interaction between laser therapy and enamel remineralization. Conclusions: The combined use of laser therapy with CPP-ACP-fluoride varnish significantly enhanced the remineralization of temporary teeth enamel. The 980 nm diode laser + CPP-ACP-fluoride varnish showed the most pronounced improvement in remineralization, while the 808 nm diode laser alone also effectively increased calcium solubility. These findings suggest that higher wavelength diode lasers, particularly when combined with remineralizing agents, can effectively enhance the mineral content of primary teeth and promote enamel remineralization.

Nocardia is an emerging bacterial disease that often affects patients with compromised immune systems. As survival increases, more and more patients are receiving solid organ transplantations, and more patients are cancer survivors and receiving long-term immune system modifying agents or corticotherapy and immunosuppressive agents, organisms that are not harmful in usual circumstances become a public health problem. Carbapenems are usually a second-line therapy in the setting of Nocardia infections. We present the case of a patient who had an immunocompromised status and was diagnosed with Nocardia farcinica, which showed in vitro resistance to carbapenems. The symptoms were non-specific and had a common presentation, although the infection was disseminated and had severe CNS and ocular involvement. The clinical course was more severe when the carbapenem was withdrawn based on the provided antibiogram and markedly improved when clinical judgment considered it might be beneficial. The first case in our region to describe an „in vitro” carbapenem-resistant Nocardia species, that responded „in vivo” differently and we believe it is important to remind clinicians of this potentially severe disease in difficult cases.

Hybrid electric vehicles (HEVs) are perceived as a first step towards the future of sustainable transport. Of course, battery electric vehicles (EVs) are currently ideal for what is wanted to stand for transport in the future. The lack of infrastructure for these vehicles makes many potential users choose hybrid vehicles in these conditions. This paper analyzes the influence of the engine and electric motor of hybrid vehicles on their performances. Three engines with slightly different power and close characteristics were considered for configured models. Also, two electric motors with very different power but having a very close maximum continuous torque have been used in models. One is an induction motor, and the other is a permanent magnet synchronous motor. ADVISOR software has been used for vehicles configuration and simulation. Series and parallel hybrid vehicles have been considered. The main dynamic performances and the fuel consumption in two driving cycles were compared for the configured models. Three conventional models with the same engines used in HEVs have also been simulated for reference. The results highlight that in the case of HEVs, the choice of combination engine/electric motor is crucial in obtaining the best report between dynamic performances and low fuel consumption, and implicitly a low negative impact on the environment.

Aortic stenosis (AS) is a prevalent valvular disorder that often requires surgical aortic valve replacement (SAVR) to alleviate symptoms and improve survival. However, the selection of an appropriate prosthetic valve size is crucial to avoid patient-prosthesis mismatch (PPM), which can lead to suboptimal hemodynamic outcomes. Von Willebrand factor (VWF), a key component of hemostasis, has been shown to be altered in AS patients and may contribute to the hemostatic imbalance observed following valve replacement. This prospective study aimed to investigate the impact of PPM on postprocedural VWF levels in patients undergoing SAVR for severe AS. The study included 31 consecutive patients diagnosed with severe AS who underwent SAVR. By focusing on the relationship between PPM and VWF dynamics, this study sought to provide valuable insights into the potential role of VWF as a marker for hemostatic changes associated with PPM, ultimately informing strategies for optimal valve selection and perioperative management to minimize complications.

With the interest and attention that the Blockchain and the Distributed Ledger Technologies (DLT) have recently demanded, the technology is advancing at a very high rate. With investors and applications in a wide variety of fields, a lot of funding and efforts are being driven into bringing the technology to everyday use. The community and companies are coming up with new ways to collaborate, which makes the blockchain ecosystem evolve at full tilt. Consequently, this paper’s aim is to review the academic and grey literature and to provide readers with information about the evolution, benefits and challenges of the Public Distributed Ledger Technologies and to discuss the latest solutions, which are being developed for bringing decentralization closer to the mainstream. The paper reviews the Directed Acyclic Graph (DAG) structured distributed ledgers with focus on the Hedera Hashgraph, a novelty DLT bringing a unique consensus algorithm with new use-cases enabled by new cryptoeconomic mechanisms as well as vital services, such as Solidity smart contracts and distributed file storage. Lastly, we are going to explore second-layer network protocols, a major topic for solving the scalability issues, empower Bitcoin and DLTs and also disrupt cryptocurrency exchanges focusing on the Lightning Network.

Early discharge after childbirth has led to a rise in neonatal readmission, becoming a major concern in recent decades. Our research aimed to identify the risk factors and incidence of neonatal readmission, as well as explore preventive measures. Our study at the Clinical Hospital of Pediatrics in Ploiești, Romania included 108 neonates admitted during the neonatal period. This accounted for 2.06% of all admissions (5226). The most prevalent cases were malnutrition (25%), fever (20.3%) and bronchiolitis (17.5%). Diarrhea and infectious gastroenteritis were also observed (14.8%), along with acute rhinoconjunctivitis (9.2%) and late onset sepsis (3.7%). No deaths were recorded. The most significant characteristics identified were number of children (p&lt;0.001) and age at maternity discharge (p&lt;0.001). By following the prevention rules, malnutrition, feeding errors, and infections can be avoided. This includes practicing proper hand hygiene for both mothers and medical staff, as well as educating and demonstrating to mothers the benefits of breastfeeding. In addition, all newborns discharged from the maternity ward should benefit from follow-up at 7-10 days of life. Our results confirm the effectiveness of a multidisciplinary team and endorse promoting breastfeeding. Implementing quality control measures and regularly evaluating the surveillance program will help improve its effectiveness.

The integration of Artificial Intelligence (AI) in dairy farm management through biometric facial recognition for cows is a significant stride in livestock management. This review critically evaluates the evolution, applications, and challenges of AI-driven biometric facial recognition in dairy farming. It emphasizes the role of this technology in enhancing individual monitoring of dairy cows, providing accurate health, behavior, and productivity tracking. Originally derived from human facial recognition systems, this approach utilizes distinctive bovine facial features for essential, non-invasive, real-time monitoring in large-scale operations. The progression of AI from elementary pattern recognition to advanced Convolutional Neural Networks (CNNs) and deep learning models marks a shift toward data-driven farming. This study addresses key challenges such as environmental variability, data collection hurdles, ethical concerns, and technological limitations. It also contrasts various AI models, spotlighting their unique strengths and practical utility in dairy farming scenarios. Despite these challenges, facial recognition technology holds promise for improving farm efficiency, animal welfare, and sustainable practices, highlighting the need for continuous research and development. The review concludes by advocating for future research focused on environmental adaptability, ethical AI application, cross-breed compatibility, and integration with other farming technologies. Ultimately, it underscores AI's transformative potential in modernizing dairy farming towards a more data-oriented, responsible agricultural future.

Currently, large volumes of data are being collected on farms using multimodal sensor technol-ogies. These sensors measure the activity, housing conditions, feed intake, and health of farm animals. With traditional methods, the data from farm animals and their environment can be collected intermittently. However, with the advancement of wearable and non-invasive sensing tools, these measurements can be made in real-time for continuous quantitation relating to clinical biomarkers, resilience indicators, and behavioral predictors. The digital phenotyping of humans has drawn enormous attention recently due to its medical significance, but much research is still needed for the digital phenotyping of farm animals. Implications from human studies show great promise for the application of digital phenotyping technology in modern livestock farming, but these technologies must be directly applied to animals to understand their true capacities. Due to species-specific traits, certain technologies required to assess phenotypes need to be tailored ef-ficiently and accurately. Such devices allow for the collection of information that can better inform farmers on aspects of animal welfare and production that need improvement. By explicitly ad-dressing farm animals’ individual physiological and mental (affective states) needs, sensor-based digital phenotyping has the potential to serve as an effective intervention platform. Future re-search is warranted for the design and development of digital phenotyping technology platforms that create shared data standards, metrics, and repositories.

Digital twin technology is already improving efficiencies and reducing costs across multiple industries and sectors. As the earliest adopters, space technology and manufacturing sectors have made the most sophisticated gains with automobile and natural resource extraction industries following close behind with recent investments in digital twin technology. The application of digital twins within the livestock farming sector is the next frontier. The possibilities that this technology may fuel are nearly endless as digital twins can be used to improve large-scale precision livestock farming practices, machinery and equipment usage, and the health and well-being of a wide variety of farm animals. Currently, many pioneers of digital twins in livestock farming are already applying sophisticated AI technology to monitor both animals and environment around the clock, which leads to a better understanding of animal behavior and distress, disease control and prevention, and smarter business decisions for the farmer. Mental and emotional states of animals can be monitored using recognition technology that examines facial features such as ear postures and eye white regions. Used with modeling, simulation and augmented reality technologies, digital twins can help farmers build more energy-efficient housing structures, predict heat cycles for breeding, discourage negative behaviors of livestock, and potentially much more. As with all disruptive technological advances, the implementation of digital twin technology will demand a thorough cost and benefit analysis by individual farms. Digital twin application will need to overcome challenges and accept limitations that arise. However, regardless of these issues, the potential of digital twins promises to revolutionize livestock farming in the future.

Phthalates are esters of Pthalic acid, compounds mainly used as plasticisers. Phthalates are widely used chemicals which are of significant research interest as their exposure causes various consequences for human health. There are several categories of phthalates extensively used in many commercial productions with diverse uses, physicochemical properties and toxicological effects. This chapter discusses the toxicity of Phthalates and its potential risks to human health.

The present study analyzed the efficiency of a naturally derived fenugreek powder for removal of Congo red dye from the aqueous solution. The flocculation Studies on Congo Red (CR) a hazardous, textile dye onto Fenugreek Powder and its adsorption was analyzed. Fenugreek Powder is Eco-friendly, biodegradable and locally available in the market. The dye adsorption process was performed in different batches at varying pH, dye concentration, adsorbent concentration and contact time to get the best results. The result showed that the maximum removal of dye was 42.4% with 10mg/l of Fenugreek powder at pH 4.

In recent decades, the requirements for implantable medical devices increased, but the risks of implant rejection still exist. These issues are primarily associated with poor osseointegration, leading to biofilm formation on the implant surface. This study focuses on addressing these issues by developing a biomaterial for implant coatings. 45S5 Bioglass has been widely used in tissue engineering due to its ability to form a hydroxyapatite layer, ensuring a strong bond between the hard tissue and the bioglass. In this context, 45S5 bioglasses, modified by the incorporation of different amounts of copper oxide, from 0 to 8 mol%, were synthesized by the melt-quenching technique. The incorporation of Cu ions does not show a significant change in the glass structure. Since the bioglass exhibited the capacity of being polarized, and therefore promoting the osseointegration effectiveness, the electrical properties of the prepared samples were studied using the impedance spectroscopy method, in the frequency range of 102-106 Hz and temperature range of 200-400 K. The effects of CuO on charge transport mobility were investigated. Additionally, the bioactivity of the modified bioglasses was evaluated through immersion tests in simulated body fluid. The results revealed the initiation of a Ca-P-rich layer formation on the surface within 24 h, indicating the potential of the bioglasses to enhance the bone regeneration process.

Intermediate temperature solid oxide fuel cell (SOFC) operation provides numerous advantages such as high combined heat and power (CHP) efficiency, potentially long-term materials stability, and the use of low-cost materials. However, due to the sluggish kinetics of the oxygen reduction reaction at intermediate temperature, the cathode of SOFC requires an efficient and stable catalyst. Significant progress in the development of cathode materials has been made over recent years. In this article, multiple strategies for improving the performance of cathode materials have been extensively reviewed such as A and B site doping of perovskites, infiltration of catalytic active materials, the use of core-shell composites, etc. Emphasis has been given to intrinsic properties such as thermal expansion compatibility, chemical and thermal stability, and oxygen transport number. Furthermore, to avoid any insulating phase formation at the cathode/electrolyte interface, strategies for interfacial layer modifications have also been extensively reviewed and summarized. Based on major technical challenges, future research directions have been proposed for efficient and stable intermediate temperature solid oxide fuel cell (SOFC) operation.

We report two cases of pancytopenia in patients after recovering from a mild COVID-19, now presenting as paroxysmal nocturnal hemoglobinuria (PNH) and aplastic anemia. These cases il-lustrate a common pathway whereby a viral trigger causes clonal expansion of a hematological disorder. Although the association in both cases are temporal, and that COVID-19 may be an in-cidental diagnosis, the growing evidence related to hematological effects of SARS-CoV-2 infec-tion highlights the need for further investigation into the hematological consequences of COVID-19, particularly in the post-pandemic era.

Forest fire is a major ecological disaster, which has economic, social and environmental impacts on humans and also causes the loss of biodiversity. Forest officials issue the warnings to the public on the basis of fire danger index classes. There is no fire danger index for the country India due to the sparsely distributed meteorological stations. Previous studies suggest that Static Fire Danger Index (SFDI) as well as Dynamic Fire Danger Index (DFDI) have been derived from the satellite datasets. In this study, we have made an attempt to integrate both the Static and Dynamic fire danger indices and also used the Near Real Time (NRT) data sets that can be available for download through NASA FTP server after one hour of the satellite overpass. In this study, DFDI has been calculated from the MODIS Terra NRT Land Surface Temperature (MOD11_L2) and MODIS TERRA NRT surface reflectance MOD09. Finally, The Forest Fire Danger Index (FFDI) has been developed from the static and dynamic fire danger indices by the additive model and the overall accuracy was around 81.27%. Thus, the FFDI has been useful to predict the fire danger accurately and can be useful anywhere, where the meteorological stations are un-available.

Ni1-xGdxFe2O4 (x=0.02, 0.04, 0.06 & 0.08) (NGF) nanoparticles were synthesized via hydrothermal method. The X-ray diffraction study confirmed the single-phase cubic spinel structure. The morphology indicated the stones (approximately nanocubes) like structures in the FESEM images. The mass loss, and thermal transition temperatures were evaluated as a function of Gd-content using TG-DTG, and TG-DSC measurements. The M-H loops evidenced the high magnetization values for NGFO as compared to pure nickel ferrite. Cation distribution showed the preferred occupancy of cations in the magnetic spinel system. Frequency dependence of dielectric, conductivity, and impedance parameters was elucidated with increase in Gd-content.

The world tsunami hazard map is developed to understand the pattern of occurrence of tsunami across the globe and for using this database for preparing effective evacuation plan and rescue operations. It has been developed by using Web GIS tools. HTML, CSS, Java Script, JQuery, IIS and Maps API are utilized to develop hazard map. These data can be even used by government authorities as well as by other nodal agencies.

This work presents an innovative application of optimal control theory to the strategic scheduling of battery storage in the day-ahead electricity market, with a focus on enhancing profitability while factoring in battery degradation. The study incorporates in the optimisation framework the cost of battery capacity degradation, the effects of capacity degradation and internal resistance degradation into dynamics. We employ a continuous-time representation of the dynamics, in contrast with many other studies that use a discrete-time approximation with rather coarse intervals. We adopt an equivalent circuit model coupled with empirical degradation parameters to simulate a battery cell’s behaviour and degradation mechanisms with good support from experimental data. Utilising direct collocation methods with mesh refinement allows for precise numerical solutions to the complex, nonlinear dynamics involved. Through a detailed case study of Belgium’s day-ahead electricity market, we determine the optimal charging and discharging schedules under varying objectives: maximizing net revenues, profits considering capacity degradation, and profits considering both capacity degradation and internal resistance increase due to degradation. The results demonstrate the viability of our approach and underscore the significance of integrating degradation costs into the market strategy for battery operators, alongside its effects on the battery’s dynamic behaviour. Our methodology extends previous work by offering a more comprehensive model that empirically captures the intricacies of battery degradation, including a fine and adaptive time domain representation, focusing on the day-ahead market, and utilising accurate direct methods for optimal control. The paper concludes with insights into the potential of optimal control applications in energy markets and suggestions for future research avenues.

Ehretia laevis Roxb. (Boraginaceae) has been extensively used as a traditional remedy for the treatment of a diverse range of ailments related to the respiratory system, the gastrointestinal tract, the reproductive system, and against several infections. This review critically assesses and documents, for the first time, the fragmented information on E. laevis, including its botanical description, folklore uses, bioactive phytometabolites and pharmacological activities. The goal is to explore this plant therapeutically. Ethnomedicinal surveys reveal that E. laevis has been used by tribal communities in Asian countries for the treatment of various disorders. Quantitative and qualitative phytochemical investigations of E. laevis showed the presence of important phytoconstituents such as pentacyclic triterpenoids, phenolic acids, flavonoids, fatty acids, steroids, alkaloids, aliphatic alcohols, hydrocarbons, amino acids, carbohydrates, vitamins and minerals. Fresh plant parts, crude extracts, fractions and isolated compounds have been reported to exhibit broad spectrum of therapeutic activities viz., antioxidant, antiarthritic, antidiabetic, anti-inflammatory, antiulcer, antidiarrheal, antidysenteric, wound healing and anti-infective activities. E. laevis is shown to be an excellent potential source of drugs for the mitigation of jaundice, asthma, dysentery, ulcers, diarrhea, ringworm, eczema, diabetes, fissure, syphilis, cuts and wounds, inflammation, liver problems, venereal and infectious disorders. Although few investigations authenticated its traditional uses but employed uncharacterized crude extracts of the plant, the major concerns raised are reproducibility of therapeutic efficacy and safety of plant material. The outcomes of limited pharmacological screening and reported bioactive compounds of E. laevis suggest that there is an urgent need for in-depth pharmacological investigations of the plant.

(1) Background: This retrospective cohort study aimed to investigate the cephalometric evaluation of facial height ratio (FHR) and growth patterns. (2) Methods: We assessed facial height ratios, Y-axis to SN angle, and growth patterns in 94 participants from Timis county using digital cephalograms. Participants were categorized based on Angle’s classification. Cephalograms were digitally traced and analyzed using WebCeph imaging software. Statistical analysis was conducted using Python version 3.11.9. (3) Results: Significant correlations were observed between FHR and craniofacial development, particularly regarding hypodivergent growth patterns. In a sample of 94, molar classes showed varying patterns: Class I—33.3% hyperdivergent, 61.5% normodivergent, 33.3% hypodivergent. Class II—50% hyperdivergent, 30.8% normodivergent, 52% hypodivergent. Class III—16.7% hyperdivergent, 7.7% normodivergent, 14.7% hypodivergent. Y-axis to SN angle values were within normal limits across groups (Group I: 65.0 (IQR: 61.2-69.0), Group II: 65.0 (IQR: 62.0-69.0), Group III: 67.0 (IQR: 59.7- 69.0). A strong negative correlation was found between Y-axis to SN angle and Jarabak’s ratio (r = -0.72, p &lt; 0.001), highlighting the need for holistic assessment methods in orthodontic practice. (4) Conclusion: Patients from Timis county predominantly exhibit a hypodivergent growth pattern across all malocclusion types. Understanding these patterns is essential for comprehensive orthodontic treatment planning. Further research is needed to explore the implications of these findings on treatment outcomes and patient care.

In this study, I extend the Fama and French five-factor asset pricing model with a sixth factor, namely, carbon risk, to investigate its impact on equity returns. To measure carbon risk, a new factor ‘pollutant minus green,’ is developed using the difference between the weighted average returns of pollutant and green firms across 51 developed and emerging countries across four categories—North America, Europe, Emerging Markets, and the Asia Pacific. The results reveal that North America, Europe, and Asia Pacific markets have a carbon risk premium that gets eliminated in small-cap firms. The carbon risk factor is further tested in left-hand side (LHS) test asset portfolios and found to be more pronounced with size-effect anomaly; specifically, small stock firms report greater declining average returns because of more exposure than the mega-cap stocks to carbon dioxide emissions. Furthermore, size-effect anomaly prevails with profitability and investment factors across firms. Therefore, high profitability, as well as high investment small firms, show a greater decline than the big stock firms in average returns when their carbon dioxide emissions increase. The asset pricing model evaluation is carried out through the Gibbons, Ross, and Shanken test. The six-factor model directed at capturing carbon risk patterns in average equity returns performs better than the three-factor and five-factor models of Fama and French (1993 and 2015) in the majority of categories under 3x3 sorting and compete with both Fama and French model under 2x4x4 sorted LHS portfolios. The finding of this study offers various useful applications for investors, policymakers, brokers, corporations, governmental pollution abatement institutions, and other stakeholders who wish to obtain carbon risk premium.

The pressure on freshwater resources is leading to diminishing flows in some of the critical river systems across the globe and India is no exception and this is mainly because of water withdrawal for irrigation, which is often to the tune of 70% to 80% with some proportion for domestic and industrial use. While graduating from the concept of environmental flows and its assessment methodologies in India, the water-managers, the researchers and the conservationists are now moving towards answering the next question if the rivers are to be revived, then where will the water come from, especially in the case of over-allocated rivers, including the river Ganga. While the logical way is to look at the biggest user of water, i.e. irrigation, it remains to be seen whether the irrigation water savings will actually lead to enhancing flows in a river, complementing the efforts towards maintaining e-flows in rivers, or whether it will lead to more area under agriculture, bring changes in cropping patterns towards more water-intensive crops or result in something else. This is a growing debate across the globe, where India is no exception, and there has been a wide range of opinions in this regard. This paper discusses the process, findings and lessons from a joint initiative involving farmers, the Uttar Pradesh state Irrigation and Water Resources Department, Bijnor District Administration and a conservation organisation to enhance flows in a rivulet, called karula River, which is part of the Ganga river system.

The aim of this work was to describe the physiological limitations induced under drought conditions in young sweet orange trees, including the crop capability to recuperate its physiological status once re-watering, and after a water withholding. The trial was conducted during two consecutive months (July and August) in two-years old sweet orange trees. Three irrigation treatments were arranged: A full irrigation treatment; (FI); a low-frequency deficit-irrigation treatment (LFDI); and a sustained-deficit irrigation (SDI) treatment. The obtained results showed that for Stem values below -1.5 MPa, significant decreases in gas exchange rates, evidencing diffusive limitations to drought stress. Additionally, there was evidence of increased osmolyte synthesis, compatible as a defensive response to prevent oxidative damage. Likewise, the isohydric behaviour observed also resulted in significant decreases in gs even for moderate stress situations. In addition, it was observed that in the LFDI treatment, the stress levels reached caused a significant increase in the levels of Pro and MDA, which were not equal to those detected in FI during the recovery periods, which leads us to assume that potential values below -1.8 MPa would be causing significant oxidative damage with no capacity for subsequent recovery, and probably affecting the final yield.

Efficient triaging and referral assessment are critical in ensuring prompt medical intervention in the Community Health Care (CHC) system. However, the existing triaging systems in many Community Health Services are an intensive, time-consuming process and often lack accuracy, particularly for various symptoms that might represent heart failure or other health-threatening conditions. There is a noticeable limit of research papers describing AI technologies for triaging patients. This paper proposes a novel quantitative data-driven approach using machine learning (ML) modelling to improve the community clinical triaging process. Furthermore, this study aims to employ the feature selection process and machine learning power to reduce the triaging process’s waiting time and increase accuracy in clinical decision-making. The model was trained on medical records of “Heart Failure” patients’ dataset, which included demographics, past medical history, vital signs, medications, and clinical symptoms. A comparative study was conducted using the list of machine learning algorithms where XGBoost demonstrated the best performance among other ML models. The triage levels of 2,35,982 patients achieved an accuracy of 99.94%, precision of 0.9986, recall of 0.9958, and f1-score of 0.9972 within 0.059 seconds. The proposed diagnostic model can be implemented for the CHC decision system and be developed further for other medical conditions.

Immunosuppressed individuals, such as people living with HIV (PLWH), remain vulnerable to severe COVID-19. We analyzed the persistence of specific SARS-CoV-2 humoral and cellular immune responses in a retrospective, cross-sectional study in PLWH on antiretroviral therapy. Among 104 participants, 70.2% had anti-S IgG antibodies and 55.8% had significant neutralizing activity against the Omicron variant in a surrogate virus neutralization test. Only 38.5% were vaccinated (8.76 ± 4.1 months prior), all displaying anti-S IgG, 75% with neutralizing antibodies and anti-S IgA. 29.8% had no SARS-CoV-2 serologic markers and displayed significantly lower CD4 counts and HIV viral load. Severe immunosuppression (present in 12.5% of participants) was linked to lower levels of detectable anti-S IgG (0.0003), anti-S IgA (p &lt; 0.0001) and lack of neutralizing activity against Omicron variant (p &lt; 0.0001). T-cell responses were present in 86.7% of tested participants, even in those lacking serological markers. In PLWH without severe immunosuppression, neutralizing antibodies and T-cell responses persisted for up to 9 months post-infection or vaccination. Advanced immunosuppression led to diminished humoral immunity, but retained cellular immune.

In the foodservice industry, clients judge not only the product standard but also the service quality they receive during their restaurant experience. Therefore, when the importance of the dining experience is strongly regarded by consumers in terms of intellectual and motivational attributes, they are happier to suggest the establishment to others. The main objective of this research is to study the influence of different service quality factors in restaurants and their impact on customer satisfaction. This research is also undertaken to evaluate the identification of important factors for the restaurant industry and the levels of service quality among customers satisfaction with the services offered by restaurants in India. As factor analysis, correlation, and regression research, this study was conducted using a modified IBM SPSS Statistics 24 and AMOS instrument among the 630 randomly selected customers. A weighted mean was used to describe the levels of service quality and customer satisfaction. Multiple regressions were used to determine the predictors of customer satisfaction. The results reveal the main 12 service quality factors, which are: 1. Process-related factors: system for take-away orders, order tracking system, and time for food preparation 2. People-related factors: focus on customers' special requests, peer group influence, and making you feel special. 3. Physical Environment: Walking Space, Architecture of the Restaurant, and Signals for Direction 4. Performance-related factors: food served at an appropriate temperature; food presentation; portion size of food in the restaurant. The researchers have developed one model on service quality factors that is described in the research paper.

We present a normative methodology, for value-pricing B2B services, using a Nash Equilibrium mechanism. Value is fundamental to any service, yet it has defied a definition. The literature is conspicuously silent on how to define value, but abounds in richly descriptive characterizations. To overcome this deficit, we focus on the financial economics of service-value. We formulate an axiomatic definition and a differential equation that embodies the idea of service-value. We specify a set of value axioms and multidisciplinary postulates that coherently form our service-value constructs. Value-pricing is challenging. It is natural that providers desire a high price and customers want a low price. Realistically, both will agree on a win-win price. To uncover this price, we specify an algorithm to reveal the Nash Equilibrium. Once agreed, it validates providers’ and customers’ commitments, and payment obligations. Service value is cocreated by both provider and customer. For a reciprocal process, its treatment is remarkably asymmetric favoring the customer. We argue that one-sided descriptions of value-in-use and value-proposition are limiting mental models. We propose the additional ideas of value-from-use and value-supposition to strengthen the conceptual symmetry of cocreation. Our work reveals a critical gap in service science, metrology. Metrology, the science of measurements, is absent from service-science. Service-value is silent on the questions of quantities, units, scales, measurement principles and instruments. We argue for a call to action for Service Metrology. We sketch a roadmap of actionable suggestions to get started.

Our ability to cure common diseases is still in danger due to the creation and spread of bacteria and fungi that are resistant to drugs and has developed new resistance mechanisms. The increasing global expansion of multi- and pan-resistant bacteria commonly referred to as "superbugs," which cause diseases that cannot be treated with current antimicrobial medications like antibiotics, is particularly concerning. Indirect and direct risks to public health are posed by microorganisms that are resistant to antibiotics in food. Food can get contaminated via cross-contamination, intentionally introducing resistance genes during food preparation, and antibiotic-resistant bacteria in food. Consuming unprocessed or unpreserved food products increases the danger of transferring antimicrobial resistance to people and other animals. Stressed cells in food can have an impact on food quality, safety, and security. They can also contribute to the development of resistance and afterwards spread it to unintended organisms in the food chain. These reviews look at a variety of topics to address the question of how antimicrobial resistance mechanisms work. The various types of bacteria that make up infections are examined, as well as the usage of antimicrobial agents and the requirement for alternative antimicrobial agents that will discourage the use of synthetic antimicrobial agents. Additionally, specific details are provided regarding the uniqueness, interdependence, and phylogenicity of organisms as well as their interactions with various ecological media. Thus, it is clear why it is important to work with food producers and handlers to stop the spread of pathogenic organisms.

Two billion people worldwide suffer from anemia with reproductive-age women being disproportionately affected. Iron plays a crucial role in cellular function and impacts cognition, physical function, and quality of life. Iron deficiency (ID) and iron deficiency anemia (IDA) are associated with adverse effects on pregnancy and fetal development. Oral iron supplementation has been the standard treatment for IDA for decades often producing sub-optimal outcomes. Many babies are still being born with ID and suffer adverse sequelae due to inadequate iron levels in the mothers. Is it time to consider broad scale-up of parenteral iron as a new standard of care?

The invention of nanoscience not only brings a revolutionary change in the field of science but also changed the direction of research. Today the whole world is under the trigger of nano and nanoparticles have multidimensional applications in every aspect of life including environmental point of view. Till today a plethora of nanoparticles have been synthesized and have also been applied for multiple purposes and hence grabbed the attention of researchers all over the world. Among the bunch of NPs discovered to date, we have a particular interest in silver nanoparticles (AgNPs) because of their cost-effectiveness and huge abundance in the earth’s crust. With respect to every passing day, due to various kinds of anthropological activities, the quality of the environment such as air, water and soil is depleting which ultimately hampers the human civilization. To encompass the growing environmental issues, many techniques have been adopted. Among the many strategies, tackling the current issues regarding environment through nanoscience is highly worthy as because of its cost-effectiveness, less time consuming and easy handling process. This article reviews the potential of nanoparticles, particularly silver nanoparticles, for a wide range of environmental applications, such as soil, air, and water remediation.

The emergence of Artificial Intelligence (AI) has transformed the way historical data is interpreted, uncovering previously unseen trends and fundamentally altering modern management practices. This shift has significantly impacted the analysis of business history and leadership studies. However, there is still a notable gap in research regarding the effects of AI-driven reinterpretations of historical events on contemporary business strategies, leadership frameworks, and ethical considerations. This study aims to address this gap by exploring the methodologies and implications of AI in business history analysis, focusing on case studies of Walmart and JPMorgan Chase. Integrating foundational theories such as evolutionary theory, path dependency, transformational, and authentic leadership reveals how AI-driven insights can refine strategic planning, advance leadership development, and promote equitable management practices. It also highlights the ethical challenges in using AI for historical analysis, particularly the risks of perpetuating existing biases. Findings indicate that AI's capacity to analyze large volumes of historical data significantly reshapes our understanding of business evolution and leadership effectiveness. The study concludes with actionable recommendations for adopting ethical AI practices, outlining future research directions, including developing AI models for contextual analysis and exploring AI’s role in promoting diverse leadership models. This research advances academic discourse by providing a theory-driven framework for leveraging AI in business history and leadership studies. It underscores AI's transformative potential and complex implications for future business practices.

Is it possible to measure the dispersion of ex-ante chances (i.e. chances “before the event”) among people, be it gambling, health, or social opportunities? We explore this question and provide some tools, including a statistical test, to evidence the actual dispersion of ex-ante chances in various areas with a focus on chronic diseases. Using the principle of maximum entropy, we derive the distribution of the risk to become ill in the global population as well as in the population of affected people. We find that affected people are either at very low risk like the overwhelming majority of the population but still were unlucky to become ill, or are at extremely high risk and were bound to become ill.

This study evaluated metabolites and lipid composition in the calf muscles of Type 2 Diabetes Mellitus (T2DM) patients and age-matched healthy controls using multi-dimensional MR Spectroscopic Imaging. We also explored the association between muscle metabolites, lipids, and intra-abdominal fat in T2DM. Participants included 12 T2DM patients (60.3 ± 8.6 years), 9 age-matched healthy controls (AMHC) (60.9 ± 7.8 years), and 10 young healthy controls (YHC) (28.3 ± 1.8 years). We acquired 2D MR spectra of calf muscles using an enhanced accelerated 5D echo-planar correlated spectroscopic imaging (EP-COSI) technique, and abdominal MRI with breath-hold 6-point Dixon sequence. In YHC, choline levels were lower in the gastrocnemius (GAS) and soleus (SOL) muscles but higher in the tibialis anterior (TA) compared to AMHC. YHC also showed higher unsaturation index (U.I.) of extramyocellular lipids (EMCL) in TA, intramyocellular lipids (IMCL) in GAS, carnosine in SOL, and taurine and creatine in TA. T2DM patients exhibited higher choline in TA and myo-inositol in SOL than AMHC, while Triglyceride fats (TGFR2) in TA was lower. Correlation analysis indicated associations between IMCL U.I. and various metabolites in muscles with liver, pancreas, and abdominal fat estimates in T2DM. These findings may elucidate the impact of dysfunctional adipose tissues in T2DM.

Gastric cancer is a challenging public health concern worldwide and remains a leading cause of cancer-related mortality. The primary risk factor implicated in gastric cancer development is infection with Helicobacter pylori. H. pylori induces chronic inflammation affecting the gastric epithelium, which can lead to DNA damage and promotion of precancerous lesions. Disease manifestations associated with H. pylori are attributed to virulence factors with multiple activities and its capacity to subvert host immunity. One of the most significant H. pylori virulence determinants is the cagPAI gene cluster, which encodes a type IV secretion system and the CagA toxin. This secretion system allows H. pylori to inject the CagA oncoprotein into host cells, causing multiple cellular perturbations. Despite the high prevalence of H. pylori infection, only a small percentage of affected individuals develop significant clinical outcomes, while most remain asymptomatic. Therefore, understanding how H. pylori triggers carcinogenesis and its immune evasion mechanisms is critical in preventing gastric cancer and miti-gating the burden of this life-threatening disease. This review aims to provide an overview of our current under-standing of H. pylori infection, its association with gastric cancer and other gastric diseases, and how it subverts the host immune system to establish persistent infection.

This paper presents an analysis of precipitation extremes over the East African region. The study employs six extreme precipitation indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI) to evaluate possible climate change. Observed datasets and CMIP6 simulations and projections are employed to assess the changes during the two main rainfall seasons of March to May (MAM) and October to December (OND). The study evaluated the capability of CMIP6 simulations in reproducing the observed extreme events during the period 1995 – 2014. Our results show that the multi-model ensemble (herein referred to as MME) of CMIP6 models can depict the observed spatial distribution of precipitation extremes for both seasons, albeit with some noticeable exceptions in some indices. Overall, MME's assessment yields considerable confidence in CMIP6 to be employed for the projection of extreme events over the study area. Analysis of extreme estimations shows an increase (decrease) in CDD (CWD) during 2081 – 2100 relative to the baseline period in both seasons. Moreover, SDII, R95p, R20mm, and PRCPTOT demonstrate significant OND estimates compared to the MAM season. The spatial variation for extreme incidences shows likely intensification over Uganda and most parts of Kenya, while reduction is observed over the Tanzania region. The increase in projected extremes during two main rainfall seasons poses a significant threat to the sustainability of societal infrastructure and ecosystem wellbeing. The results from these analyses present an opportunity to understand the emergence of extreme events and the capability of model outputs from CMIP6 in estimating the projected changes. More studies are encouraged to examine the underlying physical features modulating the occurrence of extremes incidences projected for relevant policies.

In bilaterians organism, the signaling of pyramidal neurons (PyrNs) is linked to the relative (prevalent) molecular chirality and physiological, perceptual, cognitive, and psychological functions and dysfunctions, providing the coupling of the central nervous system downstream and upstream evolutionary and developmental processes. The most apparent and discriminating morphological specificity of PyrNs is the geometry of the cell body. However, the question "why/how PyrNs soma gains the shape of quasi-tetrahedral symmetry" has never been explicitly articulated. If the basic function of PyrNs is sensory space perception, supporting the orientation, and movement coordination, then the pyramidal shape of soma is the best evolutionary-selected geometry to perform sensory-motor coupling. In biology, the impact of chiral symmetry (handedness) is evident at all levels of biological organization, from the prevalent symmetry of biological molecules to the morphology and function of bilateral organisms. How the tetrahedral symmetry of biomolecules is linked to the morphology and functions of bilateral organisms remains a challenging question. Cell chirality represents an intermediate point connecting two poles of biochirality. In this holistic perspective, examining the PyrNs' morphology-circuitry-function link is crucial to understanding the complex interaction between genetic, epigenetic, and environmental factors in the evolution of the CNS. The predictive power of our hypothesis can be partially expressed by the statement that the most integral and reliable biomarker of the neurodegenerative (including aging) and mental (including all variants of psychiatric illness) disorders would be the detection of the hemispheric asymmetry of D-amino acids (D-AAs) level in pyramidal neurons (PyrNs).

To understand SARS-CoV-2 microevolution, this study explored the genome-wide frequency, gene-wise distribution, and molecular nature of all point-mutations detected across its 71,703 RNA-genomes deposited in the GISAID repository, till 21 August 2020. Globally, nsp1/nsp2/nsp3/ nsp11 and orf7a/orf3a/S were the most mutation-ridden non-structural and structural genes respectively. Phylogeny based on 4,618 spatiotemporally-representative genomes revealed that entities belonging to the early lineages are mostly spread over Asian countries (including India, the biggest hotspot of the pandemic) whereas the recently-derived lineages are more globally distributed. Of the total 16,602 polymorphism-bearing sites in the pan-genome, 11,037 and 4,965 involved transitions and transversions, which in turn were predominated by cytidine-to-uridine and guanosine-to-uridine conversions, respectively. Positive selection of nonsynonymous mutations (dN/dS >1) in most of the structural, but not non-structural, genes indicated that SARS-CoV-2 has already harmonized its replication/transcription machineries with the host’s metabolic system, while it is still redefining virulence/transmissibility strategies at the molecular level.

This paper presents some results from a computational fluid dynamics (CFD) model of a multi-megawatt crosswind kite spinning on a circular path in a straight downwind configuration. The unsteady Reynolds averaged Navier-Stokes equations closed by the k−ω SST turbulence model are solved in the three-dimensional space using ANSYS Fluent. The flow behaviour is examined at the rotation plane, and the overall (or global) induction factor is obtained by getting the weighted average of induction factors on multiple annuli over the swept area. The wake flow behaviour is also discussed in some details using velocity and pressure contour plots. In addition to the CFD model, an analytical model for calculating the average flow velocity and radii of the annular wake downstream of the kite is developed. The model is formulated based on the widely-used Jensen’s model (Technical Report Risø-M; No. 2411, 1983), which was developed for conventional wind turbines, and thus has a simple form. Expressions for the dimensionless wake flow velocity and wake radii are obtained by assuming self-similarity of flow velocity and linear wake expansion. Comparisons are made between numerical results from the analytical model and those from the CFD simulation. The level of agreement was found to be reasonably good. Such computational and analytical models are indispensable for kite farm layout design and optimization, where aerodynamic interactions between kites should be considered.

Hematopoietic stem cells (HSCs) are responsible for the production of blood cells throughout the human life span. Single HSCs can give rise to at least eight distinct blood cell lineages. Together, hematopoiesis, erythropoiesis and angiogenesis coordinate several biological processes, such as cellular interactions in development and proliferation, guided migration, lineage programming and reprogramming by transcription factors. Any dysregulation of these processes may result in hematological disorders and/or malignancies. Several studies of the molecular mechanisms governing HSC maintenance have demonstrated that protein regulation by the ubiquitin proteasomal pathway is crucial for normal HSC function. Recent studies have shown that the reversal of ubiquitination by deubiquitinating enzymes (DUBs) plays an equally important role in hematopoiesis; however, there is only limited additional information regarding the biological function of DUBs. In this review, we focus on recent discoveries that have led to a better understanding of the physiological roles of DUBs in hematopoiesis, erythropoiesis and angiogenesis. In addition, we discuss the DUBs associated with common hematological disorders and malignancies, which may potentially be therapeutic drug targets.

This paper explores how Artificial Intelligence (AI) can help reduce the communication gap and streamline the distribution of resources. It uses an exploratory case study to examine the current state of the communication gap and the potential opportunities AI can bring to close it. The paper will discuss the current state of the communication gap, the current methods of resource distribution, and the potential opportunities AI can bring to reduce the communication gap. It will also explore the potential implications of AI on resource distribution and the communication gap. The paper will review the existing literature on the communication gap, resource distribution, and AI. It will then focus on the opportunities AI can bring to the communication gap. It will analyze the use of machine learning, natural language processing, and other AI-based technologies to reduce the communication gap. It will also examine the potential implications of AI on resource distribution, such as developing more efficient distribution channels, increasing transparency, and reducing costs. Finally, the paper will analyze the impact of AI on the communication gap and resource distribution. It will discuss the potential challenges and benefits of using AI, such as the need for data security, the potential for bias, and the limitations of AI technology. The paper will also explore the potential for AI to improve the distribution of resources and reduce the communication gap. Overall, this paper provides an exploratory case study on the opportunities of how AI can contribute to reducing the communication gap and streamlining the distribution of resources. It discusses the potential implications of AI on the communication gap and resource distribution, and it reviews the potential challenges and benefits of using AI. The paper provides an in-depth analysis of the potential opportunities of using AI to reduce the communication gap and streamline the distribution of resources.

This paper is a further development of our “Proposal of a new double-nozzle technique for in-gas-jet laser resonance ionization spectroscopy” published in journal Atoms earlier in this year. Here, we propose to equip the proposed double-nozzle technique with the RF-only funnel and RF-buncher placed in the gas-jet chamber at 70 mm distance downstream the double-nozzle exit. It allows for highly effective extraction into vacuum heavy ion beams, produced in two-steps laser resonance ionization in the argon supersonic jet. We explored the operation of this new full version of the double-nozzle technique through detailed gas dynamic and then Monte Carlo trajectory simulations, which results are presented and discussed. In particularly, we have shown by calculations that more than 80% of all nobelium-254 neutral atoms, extracted by argon flow from the gas stopping cell through the double-nozzle into the supersonic jet can be extracted into vacuum in a form of pulsed ion beam having low transverse and longitudinal emittance.s dynamic and Monte Carlo simulations

Interpretive Structural Modelling (ISM) is widely used to understand the complex connections between different components. This study presents a bibliometric overview of ISM research with a focus on its linkages to the Sustainable Development Goals (SDG) and the impact of COVID-19. The study analyzed 1988 publications on ISM published between 2012 and 2021, of which 1202 were directly mapped to the SDG and 59 were related to COVID-19. The study identified key authors, institutions, countries, and journals involved in the research and their linkages to the SDG. The results showed that ISM research is strongly linked to SDG 12 (Responsible Consumption and Production) and SDG 9 (Industry, Innovation and Infrastructure). We also identified influential SDG based on centrality measures like betweenness and eigenvector. The top four countries contributing to ISM publications were India, China, the United Kingdom, and the United States. The most frequently cited journals were Benchmarking: An International Journal, Sustainability, Journal of Modelling in Management, and Journal of Cleaner Production. Four main clusters were identified in the ISM research, including (1) integration with AHP & Fuzzy Logic for promoting sustainability alignment, (2) ISM-based strategy development for various stakeholders, (3) ISM-based decision-making in various fields, and (4) ISM-based risk evaluation. For the first time studies that used ISM approach to understand the epidemiological characteristics of COVID-19 were identified and their key findings were discussed. The study also identified several emerging topics for future ISM research, such as blockchain & IoT, environmental management systems, climate change adaptation, smart cities, and humanitarian logistics and their potential linkages to SDG.

Major depressive disorder (MDD) is one of life-threatening disorders that is prevalent worldwide. The evident etiology of this disease is still poorly understood. Therefore, a possible mechanism is used to evaluate the antidepressant effect. Nowadays, herbal medicine is gaining more interest as an alternative antidepressant. Oroxylum indicum, which is used in traditional medicine and contains a potential antidepressive compound, baicalein, has an antidepressive property. In vitro inhibitory effect on monoamine oxidase-A (MAO-A) enzyme was used to preliminarily screen the antidepressant effect of the O. indicum seed (OIS) extract. Mice were subjected to unpredictable chronic mild stress (UCMS) for 6 weeks and daily administration of the OIS extract started from week 4. The mechanisms involved in antidepressive activity were investigated. The OIS extract significantly alleviated anhedonia and despair behaviors in UCMS-induced mouse model via two possible pathways: (i) normalize the HPA-axis function by restoration of negative feedback (decreased FKBP5 and increased GR expressions) and reduction of glucocorticoid-related negative gene (SGK-1), (ii) improve neurogenesis by escalation of BDNF and CREB expression in the hippocampus and the frontal cortex. HPLC analysis of the extract showed the presence of All the results obtained from this study emphasize its potential as an effective and novel alternative treatment for MDD.

The construction enterprise is a essential zone that contributes considerably to the worldwide financial system. but it faces numerous challenges, together with inefficiency in electric and electronics engineering tasks. This inefficiency results in delays, expanded charges, and decreased productivity. Device learning strategies have the capacity to deal with those challenges through optimizing the making plans and execution of electrical and electronics engineering tasks within the construction industry. This study paper targets to discover using machines gaining knowledge of strategies to maximise efficiency in electrical and electronics engineering projects inside the production industry. Mainly, the paper will be conscious of developing and imposing gadget mastering algorithms to optimize project scheduling, fabric procurement, and system utilization. The paper will even look at the ability of using predictive analytics to identify and mitigate dangers related to electric and electronics engineering tasks. The study could be based on a combination of literature review and empirical analysis. The literature evaluation will provide a top-level view of the demanding situations going through the development industry and the capability advantages of the usage of system getting to know strategies to deal with those challenges. The empirical evaluation will involve the improvement and testing of device mastering models on real-world information from electrical and electronics engineering initiatives inside the construction industry. The predicted results of this research are the development of a fixed of sensible recommendations for the use of machine gaining knowledge of strategies to optimize electric and electronics engineering projects within the construction industry. These suggestions will be beneficial for venture managers, engineers, and other stakeholders within the creation enterprise who're inquisitive about maximizing performance and decreasing charges of their tasks. Ordinary, this studies paper aims to contribute to the continued efforts to enhance the efficiency and productiveness of the development enterprise via exploring the ability of system studying techniques to optimize electrical and electronics engineering tasks.

The pharmaceutical industry is making significant strides in enhancing process comprehension through the development of concepts like Quality by Design (QbD) and Process Analytical Technology (PAT). This shift has moved from traditional offline testing methods to real-time estimation of product quality. The dissolution characteristics of pharmaceutical tablets play a crucial role in maximizing the release of medications and their bioavailability. One factor that can affect dissolution is the blending procedure. Inadequate mixing can lead to patches of concentrated active components or excipients within the tablet, resulting in inconsistent dissolution behavior. A study investigated the impact of blending time and speed on the dissolution behavior of Amlodipine tablets using near-infrared (NIR) spectroscopy and multivariate modeling. NIR spectra were collected for Amlodipine tablets produced under various blending conditions using a 2-level central composite design. Dissolving profiles were analyzed using a USP dissolution device. Multivariate analysis techniques, including principal component analysis (PCA), partial least squares (PLS), Support Vector Machine (SVM), and Artificial Neural Networks (ANN), were applied to the collected NIR spectra. The findings demonstrated that blending speed and time had a significant influence on the dissolving properties of Amlodipine tablets. Blending at faster speeds and for shorter durations resulted in excessive shear and insufficient mixing, ultimately reduced drug release. The multivariate models constructed using ANN outperformed SVM and PLS in predicting dissolution profiles based on NIR spectra. This research highlights the effectiveness of NIR spectroscopy and multivariate modeling in optimizing tablet dissolution. These advancements enable continuous manufacturing of high-quality pharmaceutical products.

IKEA is a massive international conglomerate company, and the purpose of this exploratory qualitative research is to investigate how the burgeoning academic field known as "artificial intelligence" (AI) could assist IKEA in resolving some of the challenges that it is currently dealing with in relation to its efforts to source sustainable materials. According to the conclusions of this study, artificial intelligence has the potential to aid in the resolution of at least some of the issues associated with sustainable sourcing, at least to some degree. This was developed by mapping the issues, analyzing the potential for adopting artificial intelligence as a solution, and defining the components that are required for efficient adoption of the technology. A number of prerequisites need to be satisfied by businesses before artificial intelligence may be used in the area of sustainable sourcing. In addition, a number of artificial intelligence's constraints need to be addressed before the technology can be put into practice by the same businesses. In conclusion, the findings of this study offer some management implications and ideas for businesses that have the goal of sourcing in a way that is less harmful to the environment by making use of technological breakthroughs such as artificial intelligence. This can be accomplished through the utilization of innovative technologies.