Surface water distribution extracted from remote sensing data has been used in water resource assessment, coastal management, and environmental change studies. Traditional manual methods for extracting water bodies cannot satisfy the requirements for mass processing of remote sensing data; therefore, accurate automated extraction of such water bodies has remained a challenge. The histogram bimodal method (HBM) is a frequently used objective tool for threshold selection in image segmentation. The threshold is determined by seeking twin peaks, and the valley values between them; however, automatically calculating the threshold is difficult because complex surfaces and image noise which lead to not perfect twin peaks (single or multiple peaks). We developed an operational automated water extraction method, the modified histogram bimodal method (MHBM). The MHBM defines the threshold range of water extraction through mass static data; therefore, it does not require the identification of twin histogram peaks. It then seeks the minimum values in the threshold range to achieve automated threshold. We calibrated the MHBM for many lakes in China using Landsat 8 Operational Land Imager (OLI) images, for which the relative error (RE) and squared correlation coefficient (R2) for threshold accuracy were found to be 2.1% and 0.96, respectively. The RE and root-mean-square error (RMSE) for the area accuracy of MHBM were 0.59% and 7.4 km2. The results show that the MHBM could easily be applied to mass time-series remote sensing data to calculate water thresholds within water index images and successfully extract the spatial distribution of large water bodies automatically.

A large diversity of epigenetic factors, such as microRNAs and histones modifications, are known to be capable of regulating gene expression without altering DNA sequence itself. In particular, miR-1 is considered the first essential microRNA in cardiac development. In this study, miR-1 potential role in early cardiac chamber differentiation is analyzed through specific signaling pathways. For this, we performed in chick embryos functional experiments by means of miR-1 microinjections into the posterior cardiac precursors -of both primitive endocardial tubes- committed to sinoatrial region fates. Subsequently, embryos were subjected to whole mount in situ hybridization, immunohistochemistry and RT-qPCR analysis. As a relevant nov-elty, our results revealed that miR-1 increases Tbx5, Gata4 and AMHC1, while this microRNA diminishes Mef2c expression, during early differentiation of cardiac sinoatrial region. Further-more, we observed in this developmental context that miR-1 upregulates CRABPII and RARß, and downregulates CRABPI, which are three crucial factors in retinoic acid signaling pathway. Interestingly, we also noticed that miR-1 directly interacts with HDAC4 and Calmodulin, as well as with Erk2/MAPK1, which are three key factors actively involved in Mef2c regulation. Our study shows, for the first time, a key role of miR-1 as an epigenetic regulator in early differenti-ation of cardiac sinoatrial region through orchestrating opposite actions between retinoic acid and Mef2c, fundamental to properly assign cardiac cells to their respective heart chambers. A better understanding of those molecular mechanisms modulated by miR-1 will definitely help in fields applied to therapy and cardiac regeneration and repair.

The rapid growth of the global wearable technologies market is projected to reach $61.30 billion by 2030 which reflects a shift towards personalised health monitoring embedded in everyday activities. This paper explores the potential of embroidered interdigitated transducer (IDT)-based sensors for non-invasive, continuous monitoring of human biomarkers, particularly glucose levels, in human sweat. The study employs innovative embroidery techniques to create flexible fabric-based sensors with gold-coated IDTs. Experimental results demonstrate the sensor’s capability to detect water droplets and glucose concentrations, showcasing promising applications for health monitoring. The embroidered IDT-based sensors offer a unique approach for seamless integration into clothing, paving the way for personalised, continuous health data capture.

The goal of the study presented in this article is to investigate interconnection issues of the Intelligent Electrical Networks (IENs) with the existing ones, as well as the deployment of the appropriate load forecasting demand techniques. The concept of IENs has recently emerged as a potential solution to the global energy problem, due to the more flexible dispersed production units that are involved. In the same context, additional issues, such as the reduction of production-transfer-distribution costs as well as power losses are analyzed as well, via the penetration of Renewable Energy Sources (RESs). To this end, it is necessary to use appropriate advanced infrastructure, where in combination with modern telecommunication networks will make possible the full exploitation of IENs. Moreover, to make the electricity system more efficient, to avoid voltage and frequency imbalance issues and to implement optimal production and consumption planning, the Electric Load Forecast (ELF) is a vital process and plays a key role in the planning as well as in the management of electricity systems. Therefore, recent state of the art approaches in ELF methods are discussed as well.

With the increasing concern of environment, the energy-efficiency scheduling of manufacturing industry is becoming urgent and popular. In turning processes, both spindle speed and processing time can affect the final energy consumption and thus the spindle speed and scheduling scheme need to be optimized simultaneously. Since the turning workshop can be regarded as the flexible flow shop, this paper formulates a mixed integer linear programming model for energy-efficient scheduling of flexible flow shop. Accordingly, a new decoding method is developed by considering of the optimization of spindle speed and scheduling scheme simultaneously, and an estimation of distribution algorithm adopting the new decoding method is proposed to solve large-size problems. The parameters of this algorithm are determined by statistical technique with a simplified practical case. In order to validate the proposed method, a case from practical factories is studied, in which the makespan can be shortened by 25.22%, and the consumed energy can be saved by 5.48%. These results demonstrate the effectiveness of the proposed mathematical model and algorithm.

This paper presents a study of the 2D roughness profiles on a flat surface generated on a steel work piece, by ball nose end milling, with linear equidistant tool paths (pick-intervals). The milled surface exploration with a surface roughness tester (on pick and feed directions) produces usually 2D roughness profiles having periodical evolutions. These evolutions can be seen as time-dependent signals, describable as a sum of sinusoidal components (the wavelength of each component being regarded as a period). In order to detect a well approximated description of these components, two appropriate signal processing techniques are used in this work: first technique provides a direct mathematical (analytical) description and is based on computer aided curve (signal) fitting (more accurate), the second technique (providing an indirect and incomplete description) is based on the spectrum generated by fast Fourier transform (less accurate). This study can be seen as a way for a better understanding the interaction between the tool and the work piece or to achieve a mathematical description of the machined surface microgeometry in terms of roughness (e. g. its description being a collection of closely spaced 2D roughness profiles).

Plant leaf area index (LAI) is a key characteristic affecting field canopy microclimate. In addition to traditional professional measuring instruments, smartphone camera sensors have been used to measure plant LAI. However, when smartphone methods were used to measure conifer forest LAI, very different performances were obtained depending on whether the smartphone was held at the zenith angle or at a 57.5° angle. To validate further the potential of smartphone sensors for measuring conifer LAI and to find the limits of this method, this paper reports the results of a comparison of two smartphone methods with an LAI-2000 instrument. It is shown that both methods can be used to reveal the conifer leaf-growing trajectory. However, the method with the phone oriented vertically upwards always produced better consistency in magnitude with LAI-2000. The bias of the LAI between the smartphone method and the LAI-2000 instrument was explained with regard to four aspects that can affect LAI: gap fraction, leaf projection ratio, sensor field of view (FOV), and viewing zenith angle (VZA). It was concluded that large FOV and large VZA cause the 57.5° method to overestimate the gap fraction and hence underestimate conifer LAI, especially when tree height is greater than 2.0 m. For the vertically upward method, the bias caused by the overestimated gap fraction is compensated for by an underestimated leaf projection ratio.

Neurological disorders are the leading cause of cognitive and physical disability worldwide, affecting 15% of the global population. Due to the demographics of aging, the prevalence of neurological disorders, including neurodegenerative diseases, will double over the next two decades. Unfortunately, while available therapies provide symptomatic relief for cognitive and motor impairment, there is an urgent unmet need to develop disease-modifying therapies that slow the rate of pathological progression. In that context, biomarkers could identify at-risk and prodromal patients, monitor disease progression, track response to therapy, and parse causality of molecular events to identify novel targets for further clinical investigation. Thus, identifying biomarkers that discriminate between diseases and reflect specific stages of pathology would catalyze the discovery and development of therapeutic targets. This review will describe the prevalence, known mechanisms, ongoing or recently concluded therapeutic clinical trials, and biomarkers of three of the most prevalent neurodegenerative diseases, including Alzheimer’s disease (AD), amyotrophic lateral sclerosis (ALS), and Parkinson’s disease (PD).

This paper will discuss Data, Information, Knowledge, and Wisdom, which is commonly referred to as DIKW. The DIKW Pyramid Model that is often referenced in both academics and practitioner circles will be discussed and shown to be faulty on several levels, including. a lack of definitional agreement. A new DIKW framework will be proposed that focuses on what the DIKW elements do in the way humans think, not what they are by definition. Information as a replacement for wasted physical resources in goal-oriented tasks will be a central organizing point. The paper will move the DIKW discussion to the computer-based concept of Digital Twins (DTs) and its augmentation of how we can use DIKW to be more effective and efficient. This will especially be the case as we move to Intelligent Digital Twins (IDT) with Artificial Intelligence (AI).

L-Theanine, a unique non-protein amino acid, is an important bioactive component of green tea. Previous studies have shown that L-theanine has many potent health benefits, such as anti-anxiety effect, regulation of immune response, relaxing neural tension, reducing oxidative damage. However, little is known concerning whether L-theanine can improve the clearance of mitochondrial DNA (mtDNA) damage in organisms. Here, we reported that L-theanine treatment increased ATP production and improved mitochondrial morphology to extend the lifespan of UVC-exposed nematodes. Mechanistic investigations showed that L-theanine treatment enhanced the removal of mtDNA damage and extended lifespan by activating autophagy, mitophagy, mitochondrial dynamics, and mitochondrial unfolded protein response (UPRmt) in UVC-exposed nematodes. In addition, L-theanine treatment also up-regulated the expression of genes related to mitochondrial energy metabolism in UVC-exposed nematodes. Our study provided a theoretical basis for the possibility that tea drinking may prevent mitochondrial related diseases.

Bladder cancer (BC) diagnosis is reliant on cystoscopy, an invasive procedure associated with urinary tract infections. This has sparked interest in identifying non-invasive biomarkers in body fluids such as blood and urine. A source of biomarkers in these biofluids are extracellular vesicles (EVs), nanosized vesicles that contain a wide array of molecular cargo, including small non-coding RNA such as microRNA and transfer RNA-derived fragments (tRF). Here we performed small-RNA next-generation sequencing for urine EVs, serum EVs and serum supernatant from 41 patients with non-muscle invasive BC (27 stage Ta, 14 stage T1) and 15 non-cancer patients (NCP) with benign cystoscopy findings. Serum and urine samples were collected before transurethral resection of bladder tumors (TUR-b) and at routine postsurgery check-ups. Expression levels of tRFs in presurgery samples were compared to those at postsurgery check-ups and to NCPs. To strengthen the reliability of our results, samples from 10 patients with stage T1 disease were resequenced. When comparing tRF expression in urine EVs between BC patients with stage T1 disease and NCPs, 14 differentially expressed tRFs (DEtRFs) were identified. In serum supernatant, six DEtRFs were identified among stage T1 patients when comparing presurgery to postsurgery samples, and four DEtRFs were found when comparing presurgery samples to NCPs. By performing a blast search, we found that sequences of DEtRFs aligned with genomic sequences pertaining to processes relevant to cancer development, such as enhancers, regulatory elements and CpG islands. Our findings display a number of tRFs that may hold potential as biomarkers for the diagnosis and recurrence-free survival of BC.

The mineralogical indices, e.g., Quartz Index (QI), Carbonate Index (CI), Mafic Index (MI) for ASTER multispectral thermal infrared (TIR) data have been applied to various geological materials. The indices are proved to be robust in extracting geological information at the local scale. Considering the relatively narrow spatial coverage of each ASTER scene compared to LANDSAT, many scenes are needed when mosaicking the images to be mapped at regional scales. We develop a system to search ASTER data for a target area given the vast and expanding ASTER data archive. The data is then conditioned, prioritized, and the indices are calculated before finally mosaicking the imagery. The maps of the indices covering the very wide region of the central Tibetan Plateau are produced with this system. The characteristic features of the indices relating to their geology in the study area are analyzed and discussed. Many interesting lithological and structural information that are not currently well understood in the central Tibetan Plateau, the highest and most extensive plateau in the world, with an average elevation of over 4,500 meters above sea level, for example, distributions of the mafic-ultramafic rocks along the suture zones, the quarzitic and marine sedimentology accreted to the Eurasian continent and sulfate layers related to the Tethys and neo-Tethys geological setting can be retrieved from the processed ASTER images.

The described data set contains features from the machine control of a five-axis milling machine. The features were recorded during thirteen series productions. Each series production includes a changeover process in which the machine was set up for the production of a different product. In addition to the timestamps and the twenty recorded features derived from Numerical Control (NC) control variables, the data set also contains labels for the different production phases. For this purpose, up to 23 phases were assigned, which are based on a generalized milling process. The data set consists of thirteen .csv files, each representing a series production. The data set was recorded in a production company in the contract manufacturing sector for components with real series orders in ongoing industrial production.

Depressive disorder in childhood and adolescence is a highly prevalent mood disorder that tends to recur throughout life. Untreated mood disorders can adversely impact a patient's quality of life and cause socioeconomic loss. Thus, an accurate diagnosis and appropriate treatment is crucial. However, until now, diagnoses and treatments are conducted according to clinical symptoms. Objective and biological validation are lacking. This may result in a poor outcome for patients with depressive disorder. Research has been conducted to identify the biomarkers that are related to depressive disorder. Cumulative evidence has revealed that certain immunologic biomarkers in-cluding brain-derived neurotrophic factor (BDNF) and cytokines, gastrointestinal biomarkers, hormones, oxidative stress, and certain hypothalamus-pituitary axis biomarkers are associated with depressive disorder. This article reviews the biomarkers related to the diagnosis and treatment of pediatric depressive disorders. To date, clinical biomarker tests are not yet available for diagnosis or for the prediction of treatment prognosis. However, cytokines such as Interleukin-2, interfer-on-gamma, tumor necrosis factor-alpha, and BDNF have shown significant results in previous studies of pediatric depressive disorder. These biomarkers have the potential to be used for diagnosis, prognostic assessment, and group screening for those at high risk.

People with diabetic foot frequently exhibit poor gait and balance. However, there is no review to inform digital biomarkers of poor gait and balance related to diabetic foot, measurable by wearables outside traditional gait laboratories. Such information could assist in designing remote patient monitoring platform to track changes in gait and balance dysfunction among people with diabetic foot for timely referral and intervention. Accordingly, we conducted a web-based review using PubMed. Our search was limited to human subjects and English-written papers published in peer-reviewed journals. We identified 20 papers in this review. We found preliminary evidence of digital biomarkers of gait and balance dysfunction in people with diabetic foot, measured by wearables, such as slow gait speed, large gait variability, unstable gait initiation, and large body sway. However, due to heterogeneities in included papers in terms of study design, movement tasks, and small sample size, more studies are recommended to confirm this preliminary evidence. Additionally, based on our review, we recommend establishing appropriate strategies to successfully implement wearable-based assessment into clinical practice for diabetic foot care.

This paper explores Ghana's two-decade journey in developing a digital land information system (LIS), exemplified by Ghana Enterprise Land Information System (GELIS) and Enterprise Land Information System (ELIS). Despite advancements by Ghana’s Lands Commission, manual processes persist and coexist with digital ones. Our study uses a qualitative approach to assess the effectiveness of the LIS implemented in Accra. The study found that Accra’s LIS is highly aligned with clear institutional mandates, roles, and responsibilities as well as the availability of laws and policies to support analogue to digital conversion. Furthermore, a robust approach for protecting data, operating system and software underpins the system. Subsequently, the integration of a digital LIS has enhanced service delivery and accountability. Nonetheless, Accra’s LIS is still at the beginning of a comprehensive learning and development curve. To sustain the LIS requires furtherance in the implementation plan, funding, law implementation, ICT strategy, divisional integration, work processes, data quality, and communication strategy. These findings will inform the next steps of improvement for Accra's LIS and guide its nationwide scaling, contributing to discussions on technology acceptance dynamics in predominantly analogue environments.

The purpose of this study is to establish whether digital literacy and insurtech adoption influence insurance inclusion in Uganda. Principally, we sought to determine whether insurtech adoption mediates the nexus between digital literacy and insurance inclusion. This study adopted a cross-sectional and quantitative correlational approach. The study’s sample was 391 individuals who had used digital platforms such as mobile phones and computers to access insurance products and services in Uganda. Data were collected using structured survey questionnaires. Partial Least Squares Structural Equation Modelling (PLSEM) was employed to test the hypothesised relationships. The results demonstrate that both digital literacy and insurtech adoption significantly and positively influence insurance inclusion. We also found digital literacy to be a significant and positive determinant of insurtech adoption. Markedly, it was found that insurtech adoption mediates the association between digital literacy and insurance inclusion in Uganda. However, this study was conducted in a developing country with an underdeveloped insurance market and with low technological advancement. This may affect generalisation of the study findings. This study’s novelty lies in establishing how digital literacy and insurtech adoption interplay to influence insurance inclusion in Uganda. This is the first study to examine the effect of digital literacy and insurtech adoption on insurance inclusion.

The conventional well placement was done manually by using a numerical reservoir simulator, and it required a lengthy trial-and-error process. It required great experience and expertise to manipulate the variables and uncertainties that lie on the reservoir to determine the best placement of the well. In addition, the traditional gradient-based methods such as Line-search and Trust-region were not viable in terms of maximum results obtained. Gradient-based methods were too dependent on the surface gradient of the solution and may only converge to local optima instead of global optima. Complex reservoirs have rough surfaces with high uncertainties, which hinders the traditional gradient-based method from converging to global optima. Thus, genetic algorithms were utilized to automate the manual trial-and-error process and to overcome the limitations of the traditional gradient-based method. The objectives of this study were to analyse the effect of different initial well placement distributions, the number of random solution sizes, and the crossover rate on cumulative oil production. A synthetic reservoir model built using CMG Builder was used as the testing platform for the optimization problems. Well-placement parameterization and optimization set-up were carried out using the CMG CMOST optimization tool. The integration of CMG IMEX and CMOST optimized cumulative oil production based on the objectives of the study. The results obtained showed that the higher number of random solutions used resulted in higher cumulative oil production, with more generations needed to reach the optimum solution. It can be concluded that the larger number of random solutions used increased the probability of reaching the optimum solution, but will take more generations.

Wearable devices have become ubiquitous, collecting rich temporal data that offers valuable insights into human activities, health monitoring, and behavior analysis. Leveraging this data, researchers have developed innovative approaches to classify and predict time-based patterns and events in human life. Time-based techniques allow the capture of intricate temporal dependencies, which is the nature of the data coming from wearable devices. This paper focuses on predicting well-being factors, such as stress, anxiety, positive and negative affect, on the Tesserae dataset collected from office workers. We examine the performance of different methodologies, including deep learning architectures, LSTM, ensemble techniques, Random Forest (RF) and XGBoost and compare their performances for time-based and non-time-based versions. In time-based versions, we investigate the effect of previous records of well-being factors on the upcoming ones. The overall results show that time-based LSTM performs the best among conventional (non-time-based) RF, XGBoost, and LSTM. The performance even increases when we consider a more extended previous period, in this case, 3 past-days rather than 1 past-day to predict the next day. Furthermore, we explore the corresponding biomarkers for each well-being factor using feature ranking. The obtained rankings are compatible with the psychological literature. In this work, we validated them based on device measurements rather than subjective survey responses.

Gestational Diabetes Mellitus (GDM) is related with adverse outcomes for both the mother and the offspring. Immune and senescence play a crucial role in GDM progression. This study aims to explore the diagnostic significance of immune-related senescence (IRS) genes in GDM. We used weighted gene co-expression network analysis (WGCNA) and single sample gene set enrichment analysis (ssGSEA) to compute the immune score and aging score in control and GDM samples. Machine learning identified core IRS genes. Then we performed unsupervised cluster analysis in GDM patients to explore immune infiltration. Finally, we validated the core genes in clinical samples. We used the intersection of MEblack module from WGCNA and differentially expressed genes (DEGs) to obtain the potential genes. Next, a univariate logistic regression model including 23 potential genes were constructed, and these genes were filtered by least absolute shrinkage and selection operator (LASSO) algorithm. There were 5 potential genes (COX17, RRAS2, RRAGC, CECR1, and TACC2), which defined as biomarkers, remained eventually, and the efficacy of these biomarkers was evaluated by nomogram. Finally, the results from both qRT-PCR and western blot analyses demonstrated CECR1 was down-regulated, while TACC2 was up-regulated in GDM placental tissue. Our study identified CECR1 and TACC2 as diagnostic biomarkers for GDM associating with immune and senescence, providing a new perspective on GDM progression.

(1) Background: The aim of this study was to investigate the circadian rhythms of tongue features according to the effects of physiological phases over a 24-hour period. (2) Methods: Fifteen healthy participants aged 20 to 69 years were recruited. The participants did not have current chronic diseases or past diseases and had to meet the inclusion and exclusion criteria. The participants stayed at the Gil Hospital for a duration of 2 nights and 3 days. On the first day, at 18:00, they consumed their allocated portions of food and water and then completed a questionnaire. At approximately 21:00, their tongue images were acquired using a computerized tongue image acquisition system, following which they slept for 8 hours, commencing at 23:00. Measurements were taken from 07:00 through 21:00 on the second day, and the final acquisition was taken at 07:00 on the following morning, resulting in a total of eight images. The circadian rhythm was authenticated and quantified utilizing the single cosinor analysis, a technique for periodic regression analysis for fitting a 24-hour cosine curve. (3) Results: Cosinor analysis revealed that all tongue features were significantly related to circadian rhythm. (4) Conclusions: The results of this study may be important for considering the time of day at which the tongue is observed and tongue status is evaluated.

In this comprehensive review, we delve into the realm of intention recognition within the context of digital forensics and cybercrime. The rise of cybercrime has become a major concern for individuals, organizations, and governments worldwide. Digital forensics is a field that deals with the investigation and analysis of digital evidence in order to identify, preserve, and analyze information that can be used as evidence in a court of law. Whereas, Intention recognition is a subfield of artificial intelligence that deals with the identification of agents’ intentions based on their actions and change of states. In the context of cybercrime, intention recognition can be used to identify the intentions of cybercriminals and even to predict their future actions. Employing a meticulous six-step systematic review approach, we curated research articles from reputable journals and categorized them into three distinct modeling approaches: logic-based, classical machine learning-based, and deep learning-based. Notably, intention recognition has transcended its historical confinement to network security, now addressing critical challenges across various subdomains, including social engineering attacks, AI black box vulnerabilities, and physical security. While deep learning emerges as the dominant paradigm, its inherent lack of transparency poses unique challenges in the digital forensics landscape. We advocate for hybrid solutions that blend deep learning’s power with interpretability. Furthermore, we propose the creation of a comprehensive taxonomy to precisely define intention recognition, paving the way for future advancements in this pivotal field.

Diabetic foot ulcers (DFUs) significantly affect the lives of patients and increase the risk of hospital stay and amputation. We suggest a remote-monitoring platform for better DFU care. This system uses digital health metrics (scaled from 0 to 10, where higher scores indicate a greater risk of slow healing) to provide a comprehensive overview through a visual interface. The platform features smart offloading devices that capture behavioral metrics such as offloading adherence, daily steps, and cadence. Coupled with remotely measurable frailty and phenotypic metrics, it offers an in-depth patient profile. Additional demographic data, wound characteristics, and clinical parameters, such as cognitive function, were integrated, contributing to a comprehensive risk factor profile. We evaluated the feasibility of this platform with 124 DFU patients over 12 weeks; 39% experienced unfavorable outcomes such as dropout, adverse events, or non-healing. Digital biomarkers were benchmarked (0-10), categorized into low, medium, and high risk for unfavorable outcomes, and visually represented using color-coded radar plots. The initial results of case reports illustrate the value of this holistic visualization to pinpoint the underlying risk factors for unfavorable outcomes, including a high number of steps, poor adherence, and cognitive impairment. Although future studies are needed to validate the effectiveness of this visualization in personalizing care and improving wound outcomes, early results in identifying risk factors for unfavorable outcomes are promising.

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.

Depression is the commonest mental disorder worldwide. Both antidepressants and psychotherapy are effective in treating depression, but response to these treatments is often incomplete. Yoga-based interventions (YBIs) have emerged as a promising adjunctive treatment for depression. Recent research has attempted to identify the biological mechanisms associated with the antidepressant actions of YBIs. In this scoping review, conducted according to the PRISMA-ScR guidelines, the PubMed and Scopus databases were searched to retrieve research on biomarkers of response to YBIs in patients with depression. Eleven studies were included in the review. Based on these studies, YBIs may improve depression through beneficial effects on systemic inflammation, stress axis regulation, cardiac autonomic functioning, and possibly reductions in oxidative stress-related cellular damage. Increases in brain-derived neurotrophic factor (BDNF) may represent a final common pathway upon which these processes converge. Though these results are theoretically plausible, they should be interpreted with caution due to certain methodological limitations in the existing research. Replication of these results and improvements in study methodology, and the evaluation of other biomarkers relevant to the pathophysiology of depression, could further elucidate the mechanism of action of YBIs in this disorder, and identify which patients would benefit most from this treatment approach.

Urea cycle disorders (UCDs) are common inborn errors of metabolism, with an incidence of one in 30,000 births. They are caused by deficiencies in any of six enzymes and two carrier proteins, the most common being Ornithine Transcarbamylase Deficiency (OTCD). OTCD results in impairment to excrete nitrogen, causing toxic buildup of ammonia with resultant encephalopathy. Hyperammonemia (HA) induces the conversion of glutamate to glutamine in the brain. Excess glutamine in the brain causes osmotic changes cerebral edema, changes in astrocyte morphology, and cell death. Acute symptoms of HA include vomiting, hyperventilation, seizures, and irritability. Long-term neurological changes include deficits in working memory and executive function. To date, there are no predictors of prognosis of infants with neonatal onset OTCD outside of plasma ammonia level at presentation and duration of hyperammonemic coma. We provide a comprehensive analysis of a 16-year-old male with neonatal onset of OTCD as an example of how brain biomarkers may be useful to monitor disease course and outcome. This male presented at 8 days post natal with plasma ammonia and glutamine of 677 and 4024 micromol/L and had a missense mutation in Exon 4 (p.R129H). Treatment included protein restriction, sodium benzoate, and citrulline, arginine, and iron. He suffered recurrent acute hyperammonemic episodes despite compliance, triggered by infections or catabolic stressors. We discuss the long-term effects of the hyperammonemic episodes by following MRI based disease biomarkers.

This paper proposed a three-layer distributed simulation network architecture: 1. Distributed virtual simulation network, 2. Perception and control sub-network, and 3. Collaborative communication service network. This simulation architecture runs on a distributed platform. The platform can provide a unique virtual scenario and several simulation services to verify the individual robot system's basic perception, control, and planning algorithms and the distributed collaborative algorithms of heterogeneous multi-robot systems. Furthermore, we designed a simulation experiment scenario for classical heterogeneous robot systems such as unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs). At last, We gave a rough estimation model of the bandwidth.

Introduction: Substance-induced psychotic disorder (SIPD) is a mental health condition linked to the use of a psychotropic substance whose pathogenesis has not been totally clarified. The present retrospective observational study has the objective to identify differences in clinical features and peripheral biomarkers in SIPD patients according to the main substance of abuse.Methods: A total sample of 218 patients was divided in 3 groups according to the type of consumed substance: alcohol, cannabis and psychostimulants. The 3 groups were compared for continuous variables by one-way analyses of variance (ANOVAs) and for qualitative variables by χ² tests. After removing alcohol-induced psychotic disorder group, the same analyses were repeated. Statistically significant variables from these latter analyses were included in a binary logistic regression model to confirm their reliability as predictors for cannabis- or psychostimulants-induced psychotic disorders. Results: Psychotic cannabis abusers were younger (p<0.01) and had an earlier age at onset (p<0.01). On the contrary, alcohol consumers resulted to have a longer duration of illness (p<0.01), more previous hospitalizations (p=0.04), more frequent medical comorbidity (p<0.01), higher mean Modified Sad Person Scale scores (p<0.01). Finally, psychostimulant abusers had more frequently a lifetime history of poly-substance use disorders (p<0.01). Binary logistic regression revealed that higher mean Brief Psychiatric Rating Scale scores (p<0.01) higher sodium (p=0.012) and haemoglobin(p=0.040) plasma levels were predictors of cannabis misuse in SIPD patients.Conclusions: Different clinical factors and biochemical parameters con be associated to SIPD according to the main substance of abuse, thus requiring a specific management by clinicians.

Lung cancer is the leading cause of cancer related mortality worldwide. In order to improve its overall survival, early diagnosis is required. Since current screening methods still face some pitfalls, such as high false positive rates for low-dose computed tomography, researchers are still looking for early biomarkers to complement existing screening techniques in order to provide a safe, faster and more accurate diagnosis. Biomarkers are biological molecules found in body fluids, such as plasma, that can be used to diagnose a condition or disease. Metabolomics has already shown to be a powerful tool in the search for cancer biomarkers since cancer cells are characterised by impaired metabolism, resulting in an adapted plasma metabolite profile. The metabolite profile can be determined using nuclear magnetic resonance, or NMR. Although metabolomics and NMR metabolite profiling of blood plasma are still under investigation, there is already evidence for its potential for early-stage lung cancer diagnosis, therapy response, and follow-up monitoring. This review highlights some key breakthroughs in this research field, where the most significant biomarkers will be discussed in relation with their metabolic pathways and in light of the altered cancer metabolism.

DNA Topoisomerase IIa (Top2A) is a nuclear enzyme that is a cancer drug target, and there is interest in identifying novel sites on the enzyme to inhibit cancer cells more selectively and to reduce off-target toxicity. The C-terminal domain (CTD) is one potential target, but it is an intrinsically disordered domain, which prevents structural analysis. Therefore, we set out to analyze the sequence of Top2A from 105 species using bioinformatic analysis including the PSICalc algorithm, entropy analysis, and other approaches. Our results demonstrate that large (10th-order) interdependent clusters are found including non-proximal positions across the major domains of Top2A. Further, CTD-specific clusters of 3rd, 4th, and 5th-order were identified that included positions that had been previously analyzed via mutation and biochemical assays. Some of these clusters coincided with positions that when mutated either increased or decreased relaxation activity. Finally, sites of low entropy (i.e., high information content) were identified and mapped as key positions in the CTD. Included in the low entropy sites are phosphorylation sites and charged positions. Together, these results help to build a clearer picture of the critical positions in the CTD and provide potential sites/regions for further analysis.

Lung cancer remains the main cause of global cancer-related deaths. Currently low-dose computed tomography (LD-CT) shows a 20% reduction of mortality. However, its clinical suitability is hindered by cost, radiation, and some false positives. Thus, the discovery non-invasive and less-expensive biomarker is urgent needed. In this scenario, proteins released in biological fluids could be considered interesting biomarkers for their role in cancer development. We conducted a pioneering case-control pilot study aimed at assessing the usefulness of Raf Kinase Inhibitory Protein (RKIP) and its phosphorylated form (pRKIP) as potential early-stage lung cancer biomarkers. Urine and Blood samples of two consecutive and independent cohorts of lung cancer patients and healthy controls were screened by means of a novel experimental ELISA assay designed to evaluate both forms of the biomarker. Although urinary levels of RKIP were not statistically different between the lung cancer group and high-risk healthy group, serum ones instead increased significantly in early stage LC patients and permitted to discriminate LC patients from both low-risk (HS) and high-risk healthy subjects (HR-HS) with an overall 93% accuracy (AUC 0.93) when LC was compared to HS and 74% (AUC 0.73) when it was compared to high-risk group. Of note, the analysis of RKIP/pRKIP ratio also showed a high accuracy (90% - AUC 0.95) in discriminating LC patients from HS and more importantly, it allowed to identify LC patients compared to HR-HS, with a higher accuracy (79% - AUC 0.79) than RKIP alone. We hypothesize that serum RKIP levels may be correlated to the activation of the immune systems against the tumor during the first phases of its development. Our study identified a preliminary, non-invasive, RKIP and pRKIP profile able to discriminate with high specificity and sensitivity the early-stage lung cancer patients from high-risk Healthy Subjects. These results, although preliminary, suggest the utility of measuring RKIP/pRKIP as potential new screening test for lung cancer and provide the basis for future validation studies.

Digital twins are an emerging technology that could be harnessed for the digitalization of the industry. Steel industry systems contain a large number of electro-hydraulic components, as proportional valves. An input-output model for a novel water proportional cartridge valve is derived from physical modelling based on fluid mechanics, dynamics, and electrical principles. The valve is a two-stage valve with two 2/2-way water proportional valves as the pilot stage and a marginally stable poppet-type cartridge valve as the main valve. The orifice equation, which is based on Bernoulli principles, is approximated by a polynomial which makes parameter estimation easier and makes modelling possible without measuring pressure of the varying control volume situated in the pilot stage part of the valve. Data were collected when the valve was operating as a component in a closed-loop system as part of a press-mill machine in a steel manufacturing plant. Parameter estimation was made by convex optimization with physical constraints to overcome problems caused by poor system excitation. For comparison, a simple linear model was derived and the least squares method used for parameter estimation. A thorough estimation of the parameters relative errors is presented. The model contains 5 parameters related to the design parameters of the valve. The modelled position output is in good agreement with experimental data for training and test data. The model can be used for real time monitoring of the valve’s status by the model parameters. One of the model parameters varies linearly with production cycles. Thus, aging of the valve can be monitored.

Genome-wide association studies (GWAS) have successfully revealed numerous susceptibility loci for obesity. PREDATORR study (2014) shows that in Romania, 34,6% of adults aged 20-79 y/o are overweight, 31,4% are obese with a high risk of cardiometabolic complications, a number that puts almost 67% of Romania's population in the abnormal weight group. Our study aims to investigate the current staus of genetic foundation in metabolic disease associated with obesity, applied to a pilot group of patients specifically examining the impact of known polymorphisms and their haplotype of six food intake-regulating genes: leptin (LEP), leptin receptor (LEP-R), serotonin receptor (5HTR2A), ghrelin (GHRL), neuropeptide Y (NPY) and fat-mass and obesity-associated (FTO) with the following polymorphisms: LEP A-2548G, LEPR A-223G, 5HTR2A G-1439A, GHRL G-72T, NPY T-29063C, FTO A-T and body mass index (BMI). A notable link between the LEP-2548 rs7799039 gene's AG genotype and the risk of obesity was observed, particularly pronounced in males aged 40-49, with an approximately sevenfold increased likelihood of obesity. The 5HTR2A rs6311 AA genotype was associated with a higher BMI, not statistically significant. The FTO rs9939609 gene's AA genotype emerged as a significant predictor of obesity risk. Besides these significant findings, no substantial associations were observed with the LEPR, 5HTR2A, GHRL and NPY genes. Haplotype association analysis showed a suggestive indication of GRGMLA (rs7799039, rs1137101, rs6311, rs696217, rs16139, rs9939609 sequence) haplotype with susceptibility effect towards obesity predisposition. Linkage disequilibrium (LD) analysis showed statistically significant associations between LEP and LEPR gene (p = 1e-04), LEP and GHRL gene (p = 0.0047), GHRL and FTO gene (p=0.03). The outcomes of this study, to our knowledge, one of the very few on the Romanian population, aimed to be a starting point for further research on the targeted interventional strategies to reduce cardiometabolic risks.

The changing of banking customer preferences after Covid-19 pandemic encourages banking transformation. Neobanks emerges as innovation in banking that enter the market to compete with the incumbent banks by offering new digital experience for customer. This paper analyzes the neobanks which transformed from traditional bank, specifically its efficiency after they implement digital transformation. Efficiency is measured by using Stochastic Frontier Analysis (SFA). This study also uses Pooled Mean Group (PMG) of Panel ARDL (Autoregressive Distributed Lagged) to investigate digital transformation as one of determinants neobank’s efficiency, as well as to examine the existence of short-term and long-term relationship between digital transformation and efficiency. The SFA results show that the efficiency of the neobanks increase after they carry out digital transformation. Furthermore, from PMG Panel ARDL, it can be concluded that digital transformation is one of determinant of efficiency and there is longevity relationship between digital transformation and efficiency. In the short run, digital transformation has a significant and negative correlation with efficiency and in the long run, it has a significant positive relationship because initially digital transformation cost for operational and risk management innovation decreases the profit efficiency, but afterwards it increases the profit efficiency.

Bridges enable communications and transportation of goods nationally and internationally, underpinning economic and social activities, thus they are pylons of our prosperity and mobility. Bridges worldwide are constantly subjected to physical wear, ageing, deterioration, hazards, environmental influences, and increased loading. Loss of performance and functionality of bridge structures would have a crucial impact on overall infrastructural resilience and would cause significant negative economic and social consequences. Monitoring their behaviour for different loading conditions relies on accurate estimations of the stress-strain state of various critical components and remaining capacities. These activities are of high importance for better planning lifespan prolongation, that is the extension of their service life and prevention of unforeseen collapses, in line with sustainability principles of well-informed maintenance. In many cases, access to the structure is limited or even impossible, which causes the need for the deployment of remote and contactless methods. One such innovative technique, which has recently attracted attention in scientific and practical applications is the digital image correlation (DIC). DIC is a contactless approach applicable for long-term measurements, which enables obtaining the full field of strains and deformations. Although the DIC approach has been widely used in world engineering practice for monitoring bridges and has proved to be a reliable and accurate method, there is a lack of systematic integral review on previous practical applications, revealing limitations and perspectives. This is the main motivation and novelty of this study, which will describe selected case studies, in which DIC was used on real full-scale bridge structures and propose improvements for the method.

The objective of this research is to establish a better understanding of the current landscape of digital learning research and sustainable development by using CiteSpace. First, we retrieved published publications from the Web of Science (2004–2022). Following that, we examined the primary research strengths and important subjects of digital learning from two perspectives: collaboration networks (including collaborative networks across countries, institutions, and authors) and co-citation networks. We examined the co-citation network from three perspectives: cluster analysis, the most active citers, top references. Furthermore, referenced journals, popular themes, and rising trends were examined. These findings indicate the primary study subjects in the field of digital learning, the most intriguing research literature, and each period's emerging research hotspots. Finally, we proposed further study ideas for future paths.

A proportion of osteoarthritis (OA) patients are unsatisfied with post-operative outcomes following total joint replacement surgery (TJR), with insufficient pain relief or poor functional improvement. Predicting those will have poor outcomes would be beneficial for patients and clinicians. The aim of this study was to determine the relationship between baseline anthropometric data and the concentration of pre-operative serum and peri-operative synovial fluid (SF) cytokines with 7-month post-operative outcomes in a cohort of knee and hip OA patients. 160 OA patients were recruited who were scheduled for TJR. The concentration of 24 cytokines were measured in blood and SF by multiplex assay. EQ5Dindex health status was assessed pre-operatively and at 7-months post-operatively. 13% of patients were identified as non-responders based on EQ5Dindex. Compared to responders, non-responders were of higher BMI, had greater waist and hip circumference and had higher levels of SF leptin but lower levels of SF Resistin (p&lt;0.05). Linear regression analysis found a significant relationship between pre-operative body weight and post-operative response (ΔEQ5Dindex; r=0.222, p=0.049). The combination of body weight with SF amphiregulin and SF IL-6 provided an improved predictive model of post-operative response (r=0.470, p=0.035).

Proposed study aims to investigate the digital transformation of Greek small and medium enterprises (SMEs), with a particular focus on their digital maturity and the strategic and organizational factors contributing to digital transformation. The research issue of digital transformation is attracting considerable interest among academics and business practicians since COVID-19 accelerated the procedure of implementing Industry’s 4.0 principles all over global economies. Quantitative research on 147 Greek SMEs revealed most important issues on how these businesses implement digital transformation, factors accelerating or decelerating the process, barriers and expected outcomes. Research enlights how SMEs face the transition to digital business and reveals the factors that can facilitate the whole process. Results can be valuable for stakeholders enabled to digital transformation process from both business and academic point of view, while there exist aspects that can contribute to policy makers / motivation developers in state’s level as well.

Introduction: The repertoire of microRNAs (miRNAs) in thyroid carcinomas starts to be elucidated. Among differentiated thyroid carcinomas (DTC), papillary thyroid carcinoma (PTC) is the most common. The assessment of miRNAs expression may contribute to refine the pre-surgical diagnosis, in order to obtain a personalized and more effective treatment of patients. Aims: This study aims to evaluated 1) miRNAs expression in a series of DTC, and the association with the presence of selected genetic mutations, in order to improve diagnosis and predict biologic behavior of DTC/PTC. 2) the reliability of molecular tests in Ultrasound-guided Fine Needle Aspiration Cytology (US-FNAC) for a more precise preoperative diagnosis. Material and methods: This series includes 176 samples (98 cytology samples and 78 formalin-fixed paraffin embedded (FFPE) tissues) obtained from 106 patients submitted to surgery, being 13 benign lesions (controls) and 93 DTCs (cases). The microRNA expression was assessed for miR-146b, miR-221, miR-222, and miR-15a by quantitative reverse transcriptase-polymerase chain reaction (qRT-PCR) and the results were analyzed using the 2- ΔΔCT method, using miR16 as an endogenous control. The discriminative ability of miRNAs expression regarding PTC diagnosis was evaluated using the area under the Receiver Operating Characteristic Curve (AUC). The association of miRNAs expression, clinicopatological features and genetic alterations (BRAF, RAS and TERTp) was evaluated in PTCs. Results/Discussion: All the analyzed miRNAs showed a tendency to be over expressed in DTCs/PTCs when compared with benign lesions, both in cytology and histology samples. In cytology, miRNAs expression levels were higher in malignant than in benign tumors. In histology, corresponding discriminative abilities regarding PTC diagnosis were: miR-146b (AUC0.94, 95%CI 0.87-1), miR-221 (AUC0.79, 95%CI 0.68-0.9), miR-222 (AUC0.76, 95%CI 0.63-0.89) and miR-15a (AUC0.85, 95%CI 0.74-0.97). miR-146b showed 89% sensitivity (se) and 87% specificity (sp); mi-R221 se=68.4, sp=90; miR222 se=73, sp=70, and mi-R15a se=72, sp=80. MicroRNAs were associated with aggressive clinicopathological features and tumor progression in PTCs (p &lt; 0.05), particularly for miR-222. Our data reveals a significant association between higher expression levels of miR-146b, miR-221 and miR-222, with the presence of BRAF mutation (p&lt;0.001), and miR-146b (p=0.016) and miR-221 (p=0.010) with the presence of RAS mutation, suggesting an interplay of these mutations with miRNAs expression. Although this study has a relatively small sample size, over expression of miRNAs in cytology may contribute to a more precise preoperative diagnosis. The miRNAs showed a good discriminative ability in PTC diagnosis. The association between the miRNAs expression profile and genetic alterations results could be useful for an accurate diagnosis of DTCs/PTCs in FNAC.

Background: Learning arrangements in health care profession education are increasingly taking place in digital environments. VR in nursing education, as one of the digital elements, is the subject of controversial debate. On the one hand it supports the authenticity of case studies by adding realistic perspectives and information. On the other hand, the costs of developing and maintaining software and hardware hinder long-term implementation. Methods: In this paper, we describe our findings and insights from two different research projects focused on the incorporation of digital tools, particularly virtual reality (VR), into nursing education. Based on the German context, our aim is to promote the adoption of innovative digital methods in nursing education and to offer invaluable experiences from the field. Results: Starting with a brief recapitulation of the projects, we then elucidate the pedagogical strategies for embedding VR-driven scenarios in nursing education. Based on our experiences during the projects we identify various positive aspects, such as change of perspective and simulating acute situations. Key findings: Although potential drawbacks remain, we advocate long-term implementation and specific use of VR at the interface between theory and practice. Nevertheless it is crucial to establish regular evaluation observing the value of digitalisation, especially VR; for nursing education.

This article delves into the intersection of generative AI and digital twins within drug discovery, exploring their synergistic potential to revolutionize pharmaceutical research and development. Through various instances and examples, we illuminate how generative AI algorithms, capable of simulating vast chemical spaces and predicting molecular properties, are increasingly integrated with digital twins of biological systems to expedite drug discovery. By harnessing the power of computational models and machine learning, researchers can design novel compounds tailored to specific targets, optimize drug candidates, and simulate their behavior within virtual biological environments. This paradigm shift offers unprecedented opportunities for accelerating drug development, reducing costs, and, ultimately, improving patient outcomes. As we navigate this rapidly evolving landscape, collaboration between interdisciplinary teams and continued innovation will be paramount in realizing the promise of generative AI and digital twins in advancing drug discovery.

Background: Oral cancer (OC) is serious health concerning issue that has a high fatality rate. The oral cavity has seven kinds of OC, including the lip, tongue, and floor of the mouth, as well as the buccal, hard palate, alveolar, retromolar trigone, and soft palate. The goal of this study is to look into new biomarkers and important pathways that might be used as diagnostic biomarkers and therapeutic candidates in OC. Methods: Publicly available repository the Gene Expression Omnibus (GEO) was responsible to collect OC-related datasets. GSE74530, GSE23558, and GSE3524 microarray datasets were collected to apply analysis. Minimum cut-off criteria of |log fold-change (FC)| > 1 and adjusted p < 0.05 were applied to figure out the up-regulated and down-regulated differential expression genes (DEGs) from the three datasets. After that only common DEGs in all three datasets were collected to apply further analysis. Gene ontology (GO) and Pathway analysis were implemented to explore the functional behaviors of DEGs. Then protein-protein interaction (PPI) networks were built to identify the most performed genes, clustering algorithm was also implemented to identify complex parts of PPI. TF-miRNA networks were also constructed to study deeply about OC-associated DEGs. Finally, top gene performers from PPI networks were used to apply drug signature analysis. Results: After applying filtration and cut-off criteria 2508, 3377, and 670 DEGs were found for GSE74530, GSE23558, and GSE3524 respectively, and 166 common DEGs were found in every dataset. The GO annotation remarks that most of the DEGs were associated with the terms of type I interferon signaling pathway. The pathways of KEGG reported that the common DEGs are related to the Cell cycle and Influenza A. The PPI network holds 88 nodes and 492 edges and CDC6 had the highest number of connections. 4 clusters were identified from the PPI. Drug signatures doxorubicin and resveratrol showed high significance according to the hub genes. We anticipate that our bioinformatics research will aid in the definition of the pathophysiology and the development of new therapies for OC.

Mild TBI (mTBI) accounts for most TBI cases, the leading cause of morbidity and mortality worldwide. Despite their high incidence, mTBI pathophysiology remains largely unknown. Recent studies have shown that the inflammatory response is activated early after mTBI and can persist for several weeks or months. However, limited evidence on the utility of inflammatory biomarkers as predictors of clinical outcomes in mTBI was previously provided. Thus, this study aims to provide an overview of the current knowledge on the role of inflammation in the pathogenesis of mTBI and the potential of some inflammatory biomolecules as biomarkers of mTBI. Thus, it was shown that the increase of IL-6, TNF-α, and IL-1β plasma levels could be implicated in the development of the early post-concussion symptoms. On the other hand, the persistence of the increased plasmatic concentrations of IL-10 and IL-8 for as long as six months following the brain injury event could suggest chronic inflammation leading to neuroinflammation and late or persistent symptoms. In this context, our findings showed that the inflammatory biomarkers could be relevant in diagnosing or predicting recovery or long-term outcomes of mTBI.

Floods, recognized for their erratic behavior and devastating effects, are a significant threat as a natural disaster. In Brunei Darussalam, the impact of climate change is becoming increasingly evident through higher temperatures and an expected increase in annual rainfall (5.0mm per year from 2021 to 2050), highlighting the critical need for updated flood risk management techniques. In line with the climate resilience and adaptation objectives of WAWASAN 2035, this paper introduces an innovative approach—a cutting-edge flood risk management framework that utilizes the capabilities of big data analytics, sophisticated machine learning algorithms, and smart Internet of Things (IoT) sensors. Traditional methods, which rely on numerical and physical data modeling, often do not provide precise short-term flood forecasts. Our suggested framework uses machine learning to analyze real-time flood forecasting, sensor data and enhance warning systems, addressing the shortcomings of traditional approaches. The key advantage of this approach is its potential to drastically decrease flood-related damages, protect lives, and boost infrastructure resilience. This proposal is not only in aligns with Brunei's vision for a safe and climate-adaptable future but also marks a significant step forward in adopting innovative and sustainable flood risk management solutions amidst an evolving climate.

Despite increasing research efforts, there is a lack of consensus on defining aging or health. To understand the underlying processes, and to foster the development of targeted interventions towards increasing one’s health, there is an urgent need: (1) to find a broadly acceptable and useful definition of health, based on a list of features (which may or may not be molecular); (2) to operationalize features of health so that it can be measured; (3) to identify predictive biomarkers and (molecular) pathways of health, and (4) to suggest interventions, such as nutrition and exercise, targeted at putative causal pathways and processes. Based on a survey of the literature, we propose to define health as a state of an individual characterized by the core features of (a) physiological function, (b) cognitive function and (c) physical function, amended, specifically in case of humans, by (d) lack of disease, and by (e) reproductive function. Often used concepts such as lack of frailty, allostatic load, or self-reported health (in case of human), and indices such as the Healthy Aging Index can be viewed as projections or surrogates of our definition. We further define aging as the set of all processes in an individual that reduce its “wellbeing”, that is, its health or survival or both. We define biomarkers of health by their attribute of predicting future health better than chronological age. We define healthspan pathways as molecular features of health that relate to each other, specifically by belonging to the same molecular pathway. Our conceptual framework may integrate diverse operationalizations of health and guide precision prevention efforts that are a key to reducing the need for medical and nursing care.

The massive amount of human biological, imaging and clinical data produced by multiple and diverse sources, necessitates integrative modeling approaches able to summarize all this information into answers to specific clinical questions. In this paper, we present a hypermodeling scheme able to combine models of diverse cancer aspects regardless of their underlying method or scale. Describing tissue-scale cancer cell proliferation, biomechanical tumor growth, nutrient transport, genomic-scale aberrant cancer cell metabolism, and cell signaling pathways that regulate the cellular response to therapy, the hypermodel integrates mutation, miRNA expression, imaging, and clinical data. The constituting hypomodels as well as their orchestration and links are described. Two specific cancer types, Wilms tumor (nephroblastoma) and non-small cell lung cancer, are addressed as proof of concept study cases. Personalized simulations over the actual anatomy of a patient have been carried out. The hypermodel has also applied to predict tumor control after radiotherapy and the relationship between tumor proliferative activity and response to neoadjuvant chemotherapy. Our innovative hypermodel holds promise as a digital twin based clinical decision support system and the core of future in silico trial platforms, although additional retrospective adaptation and validation is necessary.

Introduction: A central role for neuroinflammation in epileptogenesis has recently been suggested by several investigations. This systematic review explores the role of inflammatory mediators in epileptogenesis, its association with seizure severity, and its correlation with drug-resistant epilepsy (DRE). Material and Methods: The study analysed articles published in JCR journals from 2019 to 2024, including retrospective, prospective, case-control, or cross-sectional studies. It combined MESH and free terms for "Epileptogenesis" and "Neuroinflammation" and included searches for biomarkers previously reported in narrative reviews. Results: The study analysed 243 articles related to epileptogenesis and neuroinflammation, with 356 from selective searches by biomarker type. After eliminating duplicates, 324 articles were evaluated, with 272 excluded and 52 evaluated by authors. 21 articles were included in the qualitative evaluation, including 18 case-control studies, 2 case series, and 1 prospective study. This systematic review provides acceptable support for five biomarkers: TNF-a and some of its soluble receptors (sTNFr2), HMGB1 and TLR-4, CCL2 and IL-33. Discussions: Certain receptors, cytokines, and chemokines are examples of neuroinflammation-related biomarkers that may be crucial for the early diagnosis of refractory epilepsy or may be connected to the control levels of epileptic patients. Their value will be better defined by future studies.

Though early-stage colorectal cancer has a high 5-year survival rate of 65-92% depending on the specific stage, this probability drops to 13% after the cancer metastasizes. Frontline treatments for colorectal cancer such as chemotherapy and radiation often produce dose-limiting toxicities in patients and acquired resistance in cancer cells. Additional targeted treatments are needed to improve patient outcomes and quality of life. Immunotherapy involves treatment with peptides, cells, antibodies, viruses, or small molecules to engage or train the immune system to kill cancer cells. Preclinical and clinical investigations of immunotherapy for treatment of colorectal cancer including immune checkpoint blockade, adoptive cell therapy, monoclonal antibodies, oncolytic viruses, anti-cancer vaccines, and immune system modulators have been promising, but demonstrate limitations for patients with proficient mismatch repair enzymes. In this review, we discuss preclinical and clinical studies investigating immunotherapy for treatment of colorectal cancer and predictive biomarkers for response to these treatments. We also consider open questions including optimal combination treatments to maximize efficacy, minimize toxicity, and prevent acquired resistance and approaches to sensitize mismatch repair proficient patients to immunotherapy.

Cervical cancer (CC) is the most common cancer in women worldwide and is almost always associated with repeated infections by human papillomavirus (HPV). Screening by traditional tests combined with biomarker identification techniques for low- or high-grade injuries, are becoming increasingly important in diagnosis and prognosis, avoiding countless deaths. This article explores existing literature on the main serum biomarkers and the identification of biomarkers associated with the oncogenesis of HPV expression in the identification of pre-cancerous lesions and of CC for an effective treatment, with reduction of recurrence, as well as we explain strategies for the development of biomarkers based on DNA, proteins and other markers. We also describe how markers of chromosomal instability host DNA, promoting hypo- or hypermethylation of DNA, as well as polymorphisms and epigenetic events in the p53 gene. Finally, we discuss changes in gene expression using cDNA microarray techniques and changes in the expression of proteins and markers identified through mass spectrometry.

It is estimated that pancreatic cancer will be the 2^nd leading cause of cancer-related deaths globally by 2030, highlighting the ongoing lack of effective treatment options in this devastating condition. There is a lack of reliable prognostic or predictive markers in pancreatic cancer to guide management decisions, whether for systemic chemotherapy, molecularly targeted therapies, or immunotherapies. To date, the results for targeted agents and immunotherapies in unselected populations of chemo-refractory pancreatic cancer have not met expectations. The reasons for this lack of efficacy of immunotherapy in pancreatic cancer are incompletely understood. The challenges in pancreatic cancer include the physical barrier created by the dense desmoplastic stroma surrounding the tumor, chemokine-mediated exclusion of T cells, poor antigenicity, paucity of infiltrating T cells within the tumor, ultimately leading to an immunosuppressive microenvironment. A better understanding of the role of inflammation in pancreatic cancer, its tumor microenvironment and individualized patient-related features, be they molecular, clinical or histopathological would enable a more effective tailored approach to the management of pancreatic cancer. In this review, the role of inflammation, the immune tumor microenvironment and potential immune biomarkers in pancreatic cancer are explored.

In recent years, the digital economy has played an increasingly important role in enhancing productivity, fostering growth, and facilitating the emergence of digital banking. With the widespread adoption of digital financial services for financial inclusion, the banking sector is experiencing a demand for digital transformation. It is important to note that despite the many advantages of this change, it also disrupts the familiar banking experience that customers have grown to expect, as well as introducing potential risks to maintaining customer protection at previous levels. Many critics have raised concerns regarding customer protection and potential exposure to various risks that may arise from digital financial services, which may result in both financial and non-financial losses. These risks can impact customer retention as dissatisfied customers may switch to alternative service providers. Additionally, reputation can be affected since merely offering digital financial services may not sufficiently safeguard customers. To remain competitive, financial institutions must deliver a secure experience that aligns with customer expectations while considering economic factors and societal needs. This is why it is essential to comprehend the severity of the risk factors that influence the protection of customers in the realm of digitalized banking services and products. In this study, five factors have been identified that influence customer protection when using digital financial services in Pakistan. Based on the analysis conducted using SmartPLS, it appears that all the factors proposed in this study have a significant and positive impact on the protection of customers. In particular, the information provided remains of paramount importance when it comes to determining the level of protection that a customer is entitled to within digital financial services, followed by responsiveness, privacy, authentication, and encryption mechanisms. Due to these findings, the implementation of enhanced information security management principles becomes imperative for the development and progress of the Pakistani digital banking industry.

Lymphangioleiomyomatosis (LAM) is a cystic pulmonary disorder that has been known to primarily affect women of childbearing age. The disorder can develop with mutations in the tuberous sclerosis (TSC) genes TSC1 & TSC2 but may arise by other irregular cases in the lack of these mutations. LAM can manifest in a wide variety of ways and generally compounded in symptoms of other lung disorders such as bronchitis, emphysema, & asthma. However, due to lack of effective diagnostic methods, biomarkers of LAM leading to a treatment for the disorder are currently unmet. Using integrative dating analytics and bioinformatics approaches; this study used publicly available microarray data to identify biomarkers in LAM. The results showed important differential expressed genes (DEGs) including: CDH2, CXCL6, ANXA10, MFAP5, RPS4Y1, DCBLD2, PAPPA, TFPI2, SERPINE1, LOX, MYH11, RGS1, SEPP1, TMOD1, ID1, SFTPB, CDH1, HTR2B, OGN, and IGLC1. Furthermore, gene – network and gene ontology (GO) analysis were conducted to show the importance of these genes from a molecular understanding. Taken together, the results revealed could serve as diagnostic and prognostic biomarkers with LAM patients.

Abstract Precision medicine is essentially based on reliable and non-invasive biomarkers. Aim of this review has been to describe the newest biomarkers in the field of kidney transplantation and the kidney rejection, one of the commonest and severe complications. Standard of care tools to identify acute rejection are largely full of errors and of drawbacks. In recent years new and reliable biomarkers have been found. They are avoid of risks, non-invasive and able to detect rejection even in those frequent cases in which acute rejection are clinically asymptomatic and not otherwise identifiable. In recent years several biomarkers have been identified. Very recently have been found new relevant biomarkers able to diagnose rejection with high positive predictive value and low negative predictive value. These are the donor derived cell free DNA found in the recipient, the gene expression profile of the donor found in the recipient and the urinary cytokines that express modification in the graft tissue. The aim of this study has been to find the most recent findings in the literature on this topic and to describe the utility and possible limitation of such new biomarkers on kidney rejection.

This paper provides an overview of the current state-of-the-art digital twin and digital thread technology in industrial operations. Both are transformational technologies that have the advantage of improving the efficiency of current design and manufacturing. Digital twin is an important element of the Industry 4.0 digitalization process; however, the huge amount of data that are generated and collected by digital twin offer challenges in handling, processing and storage. The paper aims to report on the development of a new framework that combines the digital twin and digital thread for better data management in order to drive innovation, improve the production process and performance, and to ensure continuity and traceability of information. The digital twin/thread framework incorporated behavior simulation and physical control components, in which these two components rely on the connectivity between the twin and thread for information flow and exchange to drive innovation. The twin/thread framework encompasses specifications that include organizational architecture layout, security, user access, cloud computing set-up, and hardware and software requirements. It is envisaged that the framework will be applicable to enhancing optimization of operational processes and traceability of information in the physical world, especially in Industry Shipyard 4.0.

Based on screening in computational biology and biological in vitro assays, five natural products isolated from extracts of the herbal medicine toad skin, such as cinobufagin (CBFi), bufalin (BFi), arenobufagin (ABFi), telocinobufagin (TBFi), bufotalin (BFTi), were subjected to molecular docking calculations with the use of SARS-CoV-2 main protease (PDB 6LU7 and 7BTF) and top-scoring ligand-receptor complexes were obtained. The results showed that the binding energy of ABFi to the 3CL protein was -17.044kcal/mol, which was higher than CBFi and TBFi. However, the binding energy of ABFi to the RdRp protease was -23.250 kcal/mol, which was much lower than that of CBFi and TBFi, EVEN lower than that of ABFi to the 3CL protein. ABFi also has polar interactions with amino acids such as Glu811, Ser814, Ser681 and Thr680 of RdRp enzyme. The results revealed that ABFi had a moderate inhibitory effect on the cell proliferation of SARS-CoV-2 in vitro, with an inhibition rate of 61.12%, even weaker than Remdesivir. This new discovery provides us with new ideas for in-depth studies on the development of natural products with this class of structural generalizations as inhibitors of SARS-CoV-2, and provides an experimental basis for the next step of mechanistic studies.

A reinforcement learning algorithm is proposed to improve the accuracy of short-term load forecasting (STLF) in this article. The proposed model integrates radial basis function neural network (RBFNN), support vector regression (SVR), and adaptive annealing learning algorithm (AALA). In the proposed methodology, firstly, the initial structure of RBFNN is determined by using SVR. Then, an AALA with time-varying learning rates is used to optimize the initial parameters of SVR-RBFNN (AALA-SVR-RBFNN). In order to overcome the stagnation for searching optimal RBFNN, a particle swarm optimization (PSO) is applied to simultaneously find promising learning rates in AALA. Finally, the short-term load demands are predicted by using the optimal RBFNN. The performance of the proposed methodology is verified on the actual load dataset from Taiwan Power Company (TPC). Simulation results reveal that the proposed AALA-SVR-RBFNN can achieve a better load forecasting precision as compared to various RBFNNs.

The aging populations observed across numerous countries worldwide necessitate a thorough exploration of interventions aimed at promoting healthy aging. As individuals age, they undergo significant physiological changes that impact their functional capacity and often necessitate increased reliance on external support systems. Enhancing the autonomy and well-being of elderly individuals emerges as a critical imperative, serving not only individual interests but also broader societal goals. This review investigates various interventions designed to optimize the health and independence of aging populations, offering insights into effective strategies for promoting healthy aging and mitigating the societal burdens associated with an aging demographic.

MicroRNAs (miRNAs) are single-stranded, noncoding RNA molecules that regulate gene expression post-transcriptionally by binding to messenger RNAs. miRNAs are important regulators of gene expression, and their dysregulation is implicated in many human and dog diseases. Most cancers tested, to date, have been shown to express altered miRNA levels, which indicates their potential importance in the oncogenic process. Based on this evidence, numerous miRNAs have been suggested as potential cancer biomarkers for both diagnosis and prognosis. miRNA-based therapies have also been tested in different cancers and have provided measurable clinical benefits to patients. In addition, understanding miRNA biogenesis and regulatory mechanisms in cancer can provide important knowledge about resistance to chemotherapies, leading to more personalized cancer treatment. In this review, we have comprehensively summarized the importance of miRNA in human and dog cancer research. We have discussed the current state of development and potential for miRNA as both a diagnostic marker and a therapeutic target.

Renal clear cell carcinoma (ccRCC) comprises over 75% of all renal tumors and arises in the epithelial cells of the proximal convoluted tubule. Molecularly ccRCC is characterized by copy number alterations (CNAs) such as the loss of chromosome 3p and VHL inactivation. Additional driver mutations (SETD2, PBRM1, BAP1, and others) promote genomic instability and tumor cell metastasis through the dysregulation of various metabolic and immune-response pathways. Many researchers identified mutation, gene expression, and proteomic signatures for early diagnosis and prognostics for ccRCC. Despite a tremendous influx of data regarding DNA alterations, gene expression, and protein expression, the incorporation of these analyses for diagnosis and prognosis of RCC into the clinical application has not been implemented yet. In this review, we focused on the molecular changes associated with ccRCC development, along with gene expression and protein signatures, to emphasize the utilization of these molecular profiles in clinical practice. These findings, in the context of machine learning and precision medicine, may help to overcome some of the barriers encountered for implementing molecular profiles of tumors into the diagnosis and treatment of ccRCC.

Sepsis is a serious disease that can lead to mortality. The etiology of the disease is connected to how the body as a host reacts toward microbes such as bacteria, viruses, and fungi invasion. Current research shows that during sepsis, the reaction toward microbe attacks takes two main phases. The first is known as a cytokine storm, while the second is a vicious cycle of pro-inflammatory responses followed by a strict anti-inflammatory response led by CD4+ Tregs. Various immunomodulatory therapies have been proposed to break the cycle of pro- and anti-inflammatory reactions to sepsis. However, clinical trials did not show promising results, indicating further research into the mechanism that Treg uses to inhibit proinflammatory reactions. We used next-generation sequencing (NGS) analysis of bulk RNA-seq and single-cell RNA-seq data to examine the exact molecular pathways used by Treg to inhibit the immune response in sepsis. Our approach identified ACOD1 (Aconitate Decarboxylase 1) as a primary mediator of Treg suppression of immune cells. We identified and validated CD36 as a downstream target of ACOD1. CD36 is a known metabolite regulator in Tregs. Taken together, our results indicate that targeting ACOD1 could prove valuable in regulating Treg function during sepsis.

There is no doubt that the COVID-19 pandemic has greatly affected the higher education sector in South Africa. However, academics and students’ preparedness and competencies have been considered a major conundrum concerning the integration of online learning in the classroom. The purpose of this paper is to examine the academics’ perspectives on a blend of a metaverse in the higher education sector in the post-COVID-19 era at one of the South African rural-based universities. A survey was administered in 2022 to ten (10) academics and the reports were consulted from four (4) E-learning practitioners to solicit their perceptions regarding the use of online learning and contact classes in the post-pandemic era. A qualitative research approach was employed to comprehend the challenges encountered in a historically disadvantaged rural-based university in Limpopo Province. The findings of this study show that there are major issues and challenges impeding the establishment of effective e-learning education. Consequently, technological continuity as a new normal in the post-COVID-19 era appears to be unattainable due to the challenges encountered by the students and lecturers. The use of Learning Management Systems among academics seems to have deteriorated due to the adjustments in COVID-19 rules and regulations. Furthermore, the findings revealed that most academics believed that contact learning remains irreplaceable throughout generations and ages as some are technophilic. This paper recommends that the incompetence and technological resistance and reluctance in adopting transformative pedagogies may not be used as a stumbling block to consider e-learning as a viable option to discontinue online education.

The construction industry is a resource and energy intensive industry and thus, has been criti-cized due to rising environmental concerns. As a result of that it has gained heightened interest in the concept of Circular Economy (CE) over the last decade to its ability to promote the slowing, reducing, and closing production and consumption cycles of materials and products used in the construction practices. Digital technologies hold potential to enhance the construction industry's ability to integrate the concept of CE in its practices. However, a clear understanding of digital technology (DT) related barriers that hinder practical implementation of CE, appears to be lacking within the sector. In light of this, this study aims to identify the barriers to adopting DTs to im-plement CE practices in the construction industry. To achieve this aim, a systematic literature review was conducted by reviewing twenty-three (23) relevant papers published until January 2024 in Scopus database. The VOS viewer software was used to perform a co-occurrence analysis of keywords to identify new and popular study areas in the field and content analysis was used to analyse the major barriers to adapt DTs to implement CE in the construction industry. The study identified thirty-four (34) barriers to use DTs to implement CE and these were categorized into nine areas such as: infrastructure, organizational, regulatory, standardization, investment, nature of the construction industry, technological, stakeholder and data related barriers. These findings will be beneficial for construction practitioners and policy makers to adopt DTs to integrate CE practices in the construction industry.

Aluminum, a contentious trace element found in the environment, has been demonstrated to have harmful effects on both humans and animals. In contrast, Sempervivum tectorum, an evergreen plant, has been found to offer numerous beneficial effects for both humans and animals. Therefore, this study aims to assess the protective effect of S. tectorum on certain reproductive biomarkers in male rats exposed to aluminum. Thirty-five Wistar rats were randomly divided into five groups: NTC (no-treatment control) received distilled water; NC (negative control) received drinking water containing 1 mg/L aluminum sulfate (AS); E1 received 1 mg/L AS along with an 8% S. tectorum extract; PC (positive control) received only 8% S. tectorum extract; E2 received 1 mg/L AS for three months followed by 8% extract for one month. The study analyzed testosterone, LH, FSH, body weight, and the histological structure of the testis, epididymis, and prostate, as well as the levels of zinc, manganese, copper, and iron in these organs. Significant decreases in body weight, testis, and epididymis size were observed in the aluminum-exposed groups compared to the control, whereas these decreases were not significant in the S. tectorum-treated groups compared to the control. Aluminum exposure led to significant decreases in testosterone and LH levels, with FSH levels showing a non-significant decrease in males, which were mitigated significantly by the administration of the plant extract. Histological analysis revealed alterations in the testis, epididymis, and prostate of the AS-exposed groups, including necrosis of seminiferous tubule epithelium and Leydig cells in the testis, and basal epithelial necrosis in the epididymis and prostate. Aluminum levels increased in all organs studied, while levels of zinc, copper, iron, and manganese decreased, showing a negative and significant correlation with aluminum levels. The aqueous extract of S. tectorum demonstrated a protective effect on certain studied biomarkers in male rats affected by aluminum exposure.

As technology and multimedia production have advanced, there has been a significant rise in attacks on digital media, resulting in duplicated, fraudulent, and altered data and the infringement of copyright laws. This paper presents a robust and secure digital image watermarking technique that has been implemented in the spatial domain and exploits the erratic and chaotic behaviour of the powerful elementary cellular automata rule-30. The crucial characteristics of the watermarking system, i.e., imperceptibility, capacity, and robustness, have been perfectly balanced by the suggested blind watermarking technique. In this approach, prior to embedding, the grayscale watermark image is downsized to its two Most Significant Bits (MSBs). Then, the 2-MSBs watermark is encrypted using an ECA rule-30 so as to level up the security attribute of the system. Then, the host image is scrambled using ECA rule-30 to distribute the watermark pixels throughout the host image and thus achieve the highest robustness against geometrical attacks. Finally, the encrypted watermark data is embedded into the scrambled host image using the ECA rule-30-based embedding key. The proposed method performs better in terms of imperceptibility, capacity, and robustness when compared to several systems with similar competencies. The simulation's findings demonstrate strong imperceptibility as evaluated by the Peak Signal-to-Noise Ratio (PSNR), which has an average value of 58.3735 dB and a high payload. The experimental outcomes, observed across a diverse range of standardized attack scenarios, unequivocally establish the ascendancy of the proposed algorithm over competing methodologies in the realm of image watermarking.

Brain tumors still remain a deadly cancer that, in most cases, is difficult to diagnose at an earlier stage. Therefore, it is necessary to develop rapid, sensitive methods for detecting cancer biomarkers. Such a biomarker may be a microRNA which expression level in body fluids is strongly correlated with cancer. Numerous studies have demonstrated changes in the expression of microRNAs in cerebrospinal fluid and blood samples collected from patients with brain tumors. These new biomarkers have the potential to help diagnose brain cancers at an early stage, avoiding the most invasive procedures. This review discusses the functional role of microRNAs and the prospects for their use as diagnostic biomarkers in patients with brain tumors.

Multiple Sclerosis (MS) is a complex disease of the central nervous system (CNS) that involves the intricate interplay of different immune cells going awry leading to inflammation, demyelination, and neurodegeneration. Its diagnosis is quite arduous because of the baffling number of symptoms it elicits and the varied clinical manifestation it presents. The simplified criteria (in form of Macdonald’s Criteria) which have got modified several times is now the single most important criteria accepted by neurology bodies for diagnosing MS. Biomarkers from time to time have been explored to simplify the diagnosis and prognosticate MS along with anecessity to monitor treatment outcome. In recent years, research on biomarkers has advanced rapidly due to their ability to be easily and rapidly measured, their specificity, safety, and their ability to yield precise results. Biomarkers are classified into various categories including predictive, diagnostic, prognostic, related to disease activity, and monitoring treatment outcome. Each representative of the disease activity category reflects a variety of pathological processes of MS such as neuroaxonal loss, gliosis, demyelination, disability progression, remyelination, etc. This review discusses several promising serum and cerebrospinal fluid biomarkers and imaging biomarkers used in clinical practice. Myelin oligodendrocyte glycoprotein antibody disease which is recently recognized as a definite disease will also be discussed. Furthermore, it sheds light on the criteria and the challenges a biomarker faces to be considered as a standard one.

Based on the data of A-share listed companies in Shanghai and Shenzhen of China from 2011 to 2018 and the digital inclusive finance index of Peking University (2011-2018), this paper empirically tests the impact of digital finance development on corporate social responsibility in various provinces of China and its impact mechanism. The results show that: (1) the development of digital finance helps to promote the fulfillment of corporate social responsibility; (2) the influence mechanism of the development of digital finance to promote the fulfillment of corporate social responsibility lies in that it reduces the cost of debt financing and leads to the improvement of corporate social responsibility.(3) Further research shows that the positive relationship between digital finance and corporate social responsibility is more significant in private enterprises. At the same time, the impact is more significant in areas with poor market environment. The above research shows that the development of digital finance has a significant positive effect, which will improve the level of corporate social responsibility.

There are clear signs that the COVID-19 pandemic has had a straining impact on the higher education sector in South Africa and the far reaches of the world. This chapter examines (Science, Technology, Engineering, Mathematics) STEM lecturers’ experiences with digital assessments and perspectives on the continuity of digital transformation in the higher education sector in the post-COVID-19 era at one of South Africa’s rural universities. In 2022, a survey of ten (10) lecturers was conducted and the reports were also consulted by four (4) e-learning practitioners to obtain their views on the use of online learning and contact courses in the post-pandemic period. A qualitative research approach was used to understand the puzzling issues faced by STEM lecturers at a previously disadvantaged institution in Limpopo Province. The results of this study show that there is a puzzling conundrum that hinders the integration of effective online learning. Furthermore, online education as the new normal in the post-COVID-19 era seems unattainable due to extremely complex and difficult-to-understand mathematical symbols. Software and tools proved to be limitations in terms of establishing an interactive learning environment for students and lecturers on online learning platforms. Lecturers’ use of learning and teaching platforms deteriorated due to numerous unknown challenges, but also the tightening of COVID-19 restrictions. Furthermore, the results showed that science and mathematics lecturers believe that contact learning remains irreplaceable across generations and eras as some are not technology enthusiasts. This paper recommends that the lack of technological systems as well as technological resistance and reluctance to adopt transformative pedagogy and innovative practices, should not be viewed as a thorny issue to end the dominance of online education in STEM courses. Keywords: connectivism, Learning Management Systems, affordances, technological resistance, Moodle, transformative pedagogy

In an era driven by technology and connectivity, where the sphere of learning goes beyond conventional classrooms, e-Service-Learning (e-SL) merges the possibilities of technology with the principles of experiential education in a powerful blend [1]. Service-learning (SL) is an educational proposal that has been cited by UNESCO [2] as an innovative educational tool through which to address the complex challenges of our society. The Sustainable Development Goals [3] provide a framework around which to structure the SL projects and thus develop the skills needed to act on the full range of goals. Embedding digital means open a debate on the intricate relationship between technology, education, and societal impact [4]. By analyzing two focus groups involving faculty, students and community partners, this contribution aims to explore the potential of combining SL and digital empowerment (DE). The focus group participants discussed the opportunities for innovation, the expected impact on them and the society at large, their needs and the pre-conditions needed to make SL and DE successful, and limitations. The results are presented according to the six principles of sustainability education in the university environment, highlighting the transformative potential of combining these principles within SL and DE.

The most popular schemes of digital potentiometers (DP) and digital-to-analog converters (DAC) are considered. DP and DAC use how elements of parameter adjustment of active RC-filters. The circuits of the DAC on the Kelvin-Varley divider, the Kelvin divider based on the R-2R matrix, and the architecture of the DAC with a segmented circuit are shown. The internal structure of DP an Analog Devices and Intersil company is shown, and also the DP switching circuits in a tunable low-pass filter and a band-pass filter, a programmable inverting amplifier and a voltage regulator are presented.

(1) Background: The purpose of this study is to evaluate the full arch scan accuracy (precision and trueness) of nine digital intra-oral scanners and four lab scanners. Previous studies have compared the accuracy of some intra-oral scanners, but as this is a field of quickly developing technologies, a more up-to-date study was needed to assess the capabilities of currently available models.; (2) Methods: The present in vitro study compared nine different intraoral scanners (Omnicam 4.6; Omnicam 5.1; Primescan; CS 3600; Trios 3; Trios 4; Runyes; i500 and DL206) as well as four lab light scanners (Einscan SE; 300e; E2 and Ineos X5) to investigate the accuracy of each scanner by examining the overall trueness and precision. Ten aligned and cut scans from each of the intra-oral and lab scanners in the in vitro study were brought into CloudCompare. A comparison was made with the master STL using the CloudCompare 3D analysis best-fit algorithm. The results were recorded along with individual standard deviation and a colorimetric map of the deviation across the surface of the STL mesh; a comparison was made to the master STL, quantified at specific points. ; (3) Results: In the present study, the Primescan had the best overall trueness (17.3 ± 4.9). Followed by (in order of increasing deviation) the Trios 4 (20.8 ± 6.2), i500 (25.2 ± 7.3), CS3600 (26.9 ± 15.9), Trios 3 (27.7 ± 6.8), Runyes (47.2 ± 5.4), Omnicam 5.1 (55.1 ± 9.5), Omnicam 4.6 (57.5 ± 3.2) and Launca DL206 (58.5 ± 22.0). Regarding the lab light scanners, the Ineos X5 had the best overall trueness with (0.0 ± 1.9). Followed by (in order of increasing deviation) the 3Shape E2 (3.6 ± 2.2), Up3D 300E (12.8 ± 2.7), and Einscan SE (14.9 ± 9.5); (4) Conclusions: This study confirms that all current generations of intra-oral digital scanners can capture a reliable, reproducible full arch scan in dentate patients. Out of the intra-oral scanners tested, no scanner produced results significantly similar in trueness to the Ineos X5. However, the Primescan was the only one to be statistically of a similar level of trueness to the 3Shape E2 lab scanner. All scanners in the study had mean trueness of under 60-micron deviation. While this study can compare the scanning accuracy of this sample in a dentate arch, the scanning of a fully edentulous arch is more challenging. The accuracy of these scanners in edentulous cases should be examined in further studies.

Abstract-Digital Phase Plane is presented in this paper. For constant input second order digital systems with arbitrary initial conditions for arbitrary quantizaters, this digital phase plane shows, in plot form, the state trajectory and the asymptotic stability, including the existence and the position of arbitrary period limit cycles.

Blood biomarkers represent a promising future for studying cognitive impairment, particularly in Alzheimer's Disease (AD), as they offer a non-invasive alternative to cerebrospinal fluid tests and have potential as population screening tools. However, the relationships between these biomarkers and specific cognitive functions, as well as their utility in predicting longitudinal cognitive decline, are not yet fully understood. This descriptive review surveys the literature from 2018 to 2023, focusing on the associations between Amyloid-β, Total Tau, Phosphorylated Tau (p-tau), Neurofilament Light (Nfl), and Glial Fibrillary Acidic Protein (GFAP) with cognition. The reviewed studies are heterogeneous, varying in design and population (mixed, cognitively impaired, or unimpaired), and show results that are sometimes conflicting. Generally, cognition positively correlates with Aβ levels, especially when evaluated through the Aβ42/Aβ40 ratio. In contrast, Tau, Nfl, and GFAP levels typically show a negative correlation with cognitive performance. While p-tau measures tend to show stronger associations with cognitive functions than other biomarkers, no single blood marker has emerged as predominantly associated with a specific cognitive domain. These findings add to our understanding of the complex relationship between blood biomarkers and cognitive performance and underscore their potential utility in clinical evaluations of cognition.

Acute kidney injury (AKI) is a clinical syndrome where a rapid decrease in kidney function and/or urine output is observed, which may result in the imbalance of water, electrolyte and acid base, and is associated with poor prognosis and prolonged hospitalization. Therefore, early diagnosis and treatment to avoid the severe AKI stage is important. While several biomarkers, such as urinary L-FABP and NGAL, can be clinically useful, there is still no gold standard for early detection of AKI and there are limited therapeutic options against AKI. miRNAs are non-coding and single-stranded RNAs that silence their target genes in the post-transcriptional process and are involved in a wide range of biological processes. Recent accumulated evidence has revealed that miRNAs may be potential biomarkers and therapeutic targets for AKI. In this review article, we summarize the current knowledge about miRNAs as promising biomarkers and potential therapeutic targets for AKI, as well as the challenges in their clinical use.

nutrition, macro and micronutrients, bone health, exercise, exogenous and endogenous microRNA, epigenetics

Epidemiological data highlight prostate cancer as a significant global health issue, with high incidence and substantial impact on patients' quality of life. The prevalence of this disease is associated with various factors, including age, heredity, and race. Recent research in prostate cancer genetics has identified several genetic variants that may be associated with an increased risk of developing the disease. However, despite the significance of these findings, genetic markers for prostate cancer are not currently utilized in clinical practice as reliable indicators of the disease. In addition to genetics, epigenetic alterations also play a crucial role in prostate cancer development. Aberrant DNA methylation, changes in chromatin structure, and microRNA (miRNA) expression are major epigenetic events that influence oncogenesis. Existing markers for prostate cancer, such as prostate-specific antigen (PSA), have limitations in terms of sensitivity and specificity. The cost of testing, follow-up procedures, and treatment for false-positive results and overdiagnosis contributes to the overall healthcare expenditure. Improving the effectiveness of prostate cancer diagnosis and prognosis requires either narrowing the risk group by identifying new genetic factors or enhancing the sensitivity and specificity of existing markers. Immunological biomarkers (both circulating and intra-tumoral), including markers of immune response and immune dysfunction, represent a potentially useful area of research for enhancing the diagnosis and prognosis of prostate cancer. Our review emphasizes the need for developing novel immunological biomarkers to improve the diagnosis, prognosis, and management of prostate cancer. We highlight the most recent achievements in the identification of biomarkers provided by circulating monocytes and tumor-associated macrophages (TAMs). We highlight that monocytes-derived and TAMs-derived biomarkers can enable to establish the missing links between genetic predisposition, hormonal metabolism and immune responses in prostate cancer.

Ulcerative colitis (UC) is one of the two disorders known as inflammatory bowel diseases (IBD) along with Crohn’s disease (CD), with complex pathogenesis, requiring costly invasive investigations. Objective: to examine the most recent biomarkers proposed for UC diagnosis; to establish the strategy used to make the differential diagnosis between UC and CD relying on these biomarkers, also adding the benefit of finding new non-invasive tools in managing this condition. The search was performed in a single database (Web of Science) using the specific keywords „ulcerative colitis”, „biomarkers” and „diagnosis” for the last five years. Study eligibility criteria: clinical trials on adults and pediatric patients with ulcerative colitis compared with Crohn’s disease. Results: We selected 57 studies, randomized controlled trials (RCTs) and clinical case series (CCS), summarizing the latest most specific biomarkers in diagnosis of UC. Limitations: we considered RCTs and CCS from one database, limited to the search topics. Our findings indicate a important number of potential biomarkers with diagnostic value, which bring the advantage of a non-invasive method to approach this challenging disorder.

Objective: The aim of study is to find key genes and enriched pathways associated with lung cancer. Participants and Methods: Differentially expressed genes (DEGs) data of 54674 genes based on stage, tumor and status of lung cancer was taken from 66 patients of African American (AAs) origin. 2392 DEGs were found based on stage, 13502 DEGs were found based on tumor, 2927 DEGs were found based on status having p value (p<0.05). Results: Total 33 common DEGs were found from stage, tumor and status of lung cancer. Gene ontology (GO) and KEGG pathway enrichment analysis was performed and 49 significant pathways were obtained, out of which 10 pathways were found to be exclusively involved in lung cancer development. Protein-protein interaction (PPI) network analysis found 69 nodes and 324 edges and identified 10 hub genes based on their highest degrees. Module analysis of PPI found that ‘Viral carcinogenesis’, ‘pathways in cancer’, ‘notch signaling pathway’, ‘AMPK signaling pathways’ had a close association with lung cancer. Conclusion: These identified DEGs regulate other genes which play important role in growth of lung cancer. The key genes and enriched pathways identified can thus help in better identification and prediction of lung cancer.

This manuscript explores the significance of continuous improvement in software development and digital product management, highlighting its role in addressing challenges and achieving superior results in today's dynamic business landscape. Drawing on Lean Thinking and Agile principles, the paper emphasizes the importance of customer focus, multidisciplinary collaboration, respect for people, inspection and adaptation, experimentation, incremental delivery, and continuous feedback in driving continuous improvement. It identifies key problems associated with the lack of continuous improvement, including quality issues, stagnation, inefficiency, lack of learning, lack of participation and collaboration, and lack of adaptability. To overcome these challenges, the paper proposes a solution that leverages agile events and interactions, such as sprint retrospectives and daily scrum meetings, and introduces a "kaizen board" as a visual tool to manage and monitor the continuous improvement process. The manuscript highlights the benefits of continuous improvement in terms of quality enhancement, efficiency gains, improved customer satisfaction, and adaptability to change. It stresses the need for fostering a culture of learning and constant adaptation, and provides insights on applying continuous improvement across different dimensions: product, process, team, and culture. The paper concludes by emphasizing the significance of commitment and active participation from all team members for achieving sustainable progress and success in digital transformation efforts.

The quality of photogrammetric-based derived products like orthophotos, digital terrain models (DTMs) and digital line maps as well as the global digital elevation models (DEM) are rigorously dependent on the accuracy of image orientation. This paper evaluates the vertical accuracy of aerial photogrammetric Digital Terrain Model (DTM), Shuttle Radar Topography Mission (SRTM), Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER), and TerraSAR-X's twin satellite of TanDEM-X (TDX) datasets against in-situ orthometric heights computed from ellipsoidal heights and the 2008 Earth Gravitational Model (EGM2008) derived geoid heights in Ethiopia. The quality of the four global digital elevation models was also validated against the aerial photogrammetric DTM measurements. Besides, the accuracies of the photogrammetric DTM and the four DEM products were checked for their compliance to the American Society for Photogrammetry and Remote Sensing (ASPRS) standards as well as the Ethiopian national vertical data evaluation standards. The study showed that the photogrammetric DTM is in a good agreement with the reference orthometric heights compared to SRTM, ASTER and TDX datasets. More precisely, the result has an absolute accuracy of 1.67 m at Linear Error (LE) 95% confidence level, while the absolute accuracy of SRTM3 arc seconds (SRTM3) at LE 90% (11.91 m) is better than its product specification (16 m). The absolute accuracy of SRTM1 arc second (SRTM1) (7.70 m at LE 90%) surpasses that of SRTM3, whereas the absolute accuracy of ASTER DEM is somehow below its product specification. TDX also has the same vertical accuracy (10.29 m at LE 90%) compared to its product specification (10 m). Furthermore, the vertical accuracy of the photogrammetric DTM meets the100 cm vertical accuracy of the 2015 ASPRS standard. However, it does not meet the Ethiopian national vertical data accuracy requirement standard, i.e., RMSEz of ± 0.45 m. In general, the photogrammetric DTM, SRTM1, and TDX have been proven a superior product over the SRTM3 and ASTER DEMs, and better to use these products for high-level precision and accuracy required applications.

During the last 25 years the scientific community has coexisted with the most fascinating protocol due to Quantum Physics: quantum teleportation (QTele), which would have been impossible if quantum entanglement, so questioned by Einstein, did not exist. In this work, a complete architecture for the teleportation of Computational Basis States (CBS) is presented. Such CBS will represent each of the possible 24 classical bits commonly used to encode every pixel of a 3-color-channel-image (red-green-blue, or cyan-yellow-magenta). For this purpose, a couple of interfaces: classical-to-quantum (Cl2Qu) and quantum-to-classical (Qu2Cl) are presented with two versions of the teleportation protocol: standard and simplified.

In the paper, we explore the notion of global digital citizenship, particularly with respect to museums and cultural heritage organizations. In this perspectives study, we explore current examples of how museums can adapt to the tenets of global digital citizenship necessary to navigate and participate in increasingly interconnected digital worlds and culture, and to collectively address global challenges.

Postpartum depression (PPD) is a disorder that impairs the formation of the relationship between mother and child, and reduces the quality of life for affected women to a functionally significant degree. Studying markers associated with PPD can help in early detection, prevention, or monitoring treatment. The purpose of this paper is to review biomarkers linked to PPD and to present selected theories on the pathogenesis of the disease based on data from biomarker studies. The complex etiology of the disorder reduces the specificity and sensitivity of markers, but they remain a valuable source of information to help clinicians. The biggest challenge of the future will be to translate high-tech methods for detecting markers associated with postpartum depression into more readily available and less costly ones. Population-based studies are needed to test the utility of potential PPD markers.

Cutaneous T cell lymphomas (CTCLs) are caused by malignant clonal proliferation of skin-tropic T cells. Most patients have an indolent disease course managed with skin-directed therapies, while some entities, or in advanced stages of disease, have aggressive progression and poor survival. To efficiently treat CTCL consistent entity markers are needed to prevent the delay in diagnosis and to provide disease specific treatment to patients. Recently, the introduction of high throughput parallel sequencing methods resulted in identification of numerous genetic and ep-igenetic alterations affecting the transcriptome of CTCL cells. In this review, most relevant genes, including TOX, CADM1, PLS3, DNM3, CXCL13, PD-1, BCL11B and TMEM244 are reported that are specifically expressed in CTCL. Upon verification their specificity and sensitivity in larger studies, their altered expression will be able to be recognized as novel biomarkers, that can im-prove the early diagnosis of CTCL.

The development of biosensors with high sensitivity and low-detection limits provides a new direction for medical and personal care. Graphene and graphene derivatives have been used to prepare various types of biosensors due to their excellent sensing performance (e.g. high specific surface area, extraordinary electronic properties, electron transport capabilities and ultrahigh flexibility). This perspective review focuses on graphene-based biosensors for quantitative detection of cancer related biomarkers such as DNA, miRNA, small molecules and proteins by integrating with different signal outputting approaches including fluorescent, electrochemistry, surface plasmon resonance, surface enhanced Raman scattering etc. The article also discussed their challenges and potential solutions along with future prospects.

Immune Checkpoint Inhibitors are monoclonal antibodies that are used to treat over one in three cancer patients. While they have changed the natural history of disease, prolonging life and preserving quality of life, they are highly active in less than 40% of patients, even in the most responsive malignancies such as melanoma, and cause significant autoimmune side effects. Licenced biomarkers include tumour Programmed Death Ligand 1 expression by immunohistochemistry, microsatellite instability, and Tumour Mutational Burden, none of which are particularly sensitive or specific. Emerging tumour and immune tissue biomarkers such as novel immunohistochemistry scores, tumour, stromal and immune cell gene expression profiling, and liquid biomarkers such as systemic inflammatory markers, kynurenine/tryptophan ratio, circulating immune cells, cytokines and DNA are discussed in this review. We also examine the influence of the faecal microbiome on treatment outcome and its use as a biomarker of response and toxicity.

This paper exposes the initial part of a research consisting in a need analysis carried out by the Romanian partner in the Erasmus+ "Media Education for Aware Adults" project, regarding digital competences and media education of adult educators or trainers. After a short introduction on the subject, in the paper are presented the main findings of the desk research, and two focus group sessions, first one for identifying the necessary digital competences of adult educators, and second one for identifying the elements that educators need in order to adequately support adults in their learning process, especially in relation to the digital environment. The results of the initial research will lead to the development of the main intellectual outputs, namely the digital competences framework, handbook, online platform and training material on digital and media education. In their development will be taken into account, on the one hand, the needs identified in the initial research, and, on the other hand, the need for a sustainable approach to all activities, resources and outputs produced, as well as education itself, this goal being assumed through the project.

In the coming years, digital applications and services that continue to use the country's native cloud systems will be huge. By 2023, that will exceed 500 million, according to IDC. This corresponds to the sum of all applications developed in the last 40 years. If you are the one you answered, yes! This article is for you!

This study aims to explore significant present and future trends in accounting and corporate sustainability by examining publications from the Web of Science database spanning from 2010 to 2023. With a particular focus on research related to the digital business environment, the study employs bibliometric analysis grounded in the resource-based view theory to investigate the evolution of auditing and digital accounting research. Utilizing the VOSviewer tool, the study analyzes keyword relationships and their geographical distribution based on a comprehensive examination of 700 publications from the Web of Science. The findings highlight the transformative impact of auditing and digital accounting on business operations in response to rapid digitization. Key clusters identified shed light on digital transformation and the intersection between information technology and corporate management, revealing the evolving research landscape. The study discusses both practical and theoretical implications arising from these findings.

The paper analyses the national DK-model Hydrological Information and Prediction (HIP) system and HIP portal viewed as a ‘Digital Twin’ and how the introduction of real-time dynamic updating of the DK-model HIP simulations can give room for plug-in sub-models with real-time boundary conditions made available from a HIP portal. The possible feedback to a national real-time risk knowledge base during extreme events (flooding and drought) is also discussed. Under climate change conditions, Denmark is likely to experience more rain in winter, more evapotranspiration in summer, intensified cloudbursts, drought, and sea level rise. These challenges have been addressed as part of the Joint Governmental Digitalization Strategy 2016-2020 for better use and sharing of public data about the terrain, water, and climate to support climate adaptation, water management, and disaster risk reduction. This initiative included the development of a new web-based data portal (HIP portal) developed by the Danish Agency for Data Supply and Infrastructure (SDFI). GEUS delivered 5 terra-byte of hydrological model data to the portal with robust calibration methods and hybrid Machine Learning (ML) being key parts of the deliverables. The paper discusses the challenges and potentials of further developing the HIP Digital Twin with ‘plug-in Digital Twins’ for local river basins including feedback to the national level.

Febrile infections are a common cause of presentation to the emergency department (ED) in chil-dren. While viral infections are usually self-limiting, sometimes bacterial illnesses may lead to sepsis and severe complications. Inflammatory biomarkers such as C reactive protein (CRP) and procalcitonin are usually the first blood exams performed in the ED to differentiate bacterial and viral infections; the better understanding of immunochemical pathways has nowadays led to the discovery of new and more specific biomarkers that could play a role in the emergency setting. The aim of this narrative review is to provide the most recent evidence on biomarkers and predictor models combining them for serious bacterial infections (SBI) diagnosis in febrile children. Litera-ture analysis showed that inflammatory response is a complex mechanism in which many bio-chemical and immunological factors contribute to host response in SBI. CRP and procalcitonin still represent the most used biomarkers in the pediatric ED for the diagnosis of SBI. Their sensibility and sensitivity increase when combined and for this reason it is reasonable to take them both into consideration in the evaluation of a febrile children. The potential of machine learning tools, who represent a real novelty in medical practice, in conjunction with routine clinical and biological in-formation, may improve accuracy of diagnosis and target therapeutic options in SBI. However, studies on this matter are not yet validated in younger populations, making their relevance in pe-diatric precision medicine still uncertain. More data from further researches are needed to improve clinical practice and decision making using these new technologies.

Abstract: Background: COVID-19 is a respiratory disease that eventually became pandemic with 300 million people infected around the world. Alongside the improvement in COVID-19 management and vaccine development, identifying biomarkers for COVID-19 has recently reported to help in early prediction and managing severe cases which might improve outcome. The Aim of our study was to find out if there is any correlation between clinical severity and elevated hematological and biochemical markers in Covid-19 patients and its effect on the outcome Methods: We have collected data retrospectively of socio-demographic, medical history, biomarkers, and diseases outcome from five hospitals and health institutions in the Kingdom of Saudi Arabia. Results: Pneumonia was the most common presentation of COVID 19 in our cohort. All biomarkers (D-Dimer, CRP, troponin, LDH and ferritin) except for the mean lowest white blood cells were found to have significant correlation (p<0.05) with worse COVID-19 disease outcomes. There was a sig-nificant association between the inflammatory biomarkers and the disease severity of COVID 19 patients (p<0.001). Patients with evidence of severe respiratory disease particularly who required mechanical ventilation (MV) had higher biomarkers, when compared to those with stable respiratory condition (p<0.001). Conclusion: Identifying biomarkers predicts outcome for COVID-19 patients and may significantly help in the management.

Epilepsy is one of the most common disorders of the central nervous system, impacting nearly 50 million people around the world. Heterogeneous in nature, epilepsy presents in children and adults alike. Currently, surgery is the only treatment that can cure epilepsy. However, not all individuals are eligible or have successful outcomes. Difficulty in accessing samples of human brain tissue along with advances in sequencing technology have driven researchers to investigate sampling liquid biopsies in blood, serum, plasma, and cerebrospinal fluid within the context of epilepsy. Liquid biopsies provide minimal or non-invasive sample collection approaches and can be assayed relatively easily across multiple time points, unlike tissue-based sampling. Various efforts have investigated circulating nucleic acids from these samples including microRNAs, cell-free DNA, transfer RNAs, and long non-coding RNAs. Here, we review nucleic acid-based liquid biopsies in epilepsy to improve understanding of etiology, diagnosis, prediction, and therapeutic monitoring.

Background: Identification of cancer biomarkers that are differentially expressed (DE) under two biological conditions is an important task in many microarray studies. There exist several methods in the literature in this regards and most of these methods designed especially for unpaired samples, which does not satisfy the requirements of paired samples where the gene expressions are taken from the same patients before and after treatment. Furthermore, the traditional biomarker identification methods based on either p-values or fold change (FC) values. However, sometimes, p-value based results do not comply with FC based results due to the smaller variance of gene expressions. There are some methods that combine both p-values and FC values to solve this problem. But, these methods also show weak performance for small-sample case in presence of outlying expressions. To overcome this problem, in this paper an attempt is made to develop a hybrid robust SAM-FC approach by combining rank of FC values and rank of p-values based on SAM statistic using minimum β-divergence method, which is designed for paired samples. This method introduces a weight function known as β-weight function. This weight function produces larger weights corresponding to usual/normal expressions and smaller weights for unusual/outlying expressions. The β-weight function plays the significant role on the performance of the proposed method. Results: The proposed method uses β-weight function as a measure of outlier detection by setting β=0.2. We unify both classical and robust estimates using β-weight function such that maximum likelihood estimators (MLEs) are used in absence of outliers and minimum β-divergence estimators are used in presence of outliers to obtain reasonable p-values and FC values in the proposed method. We examined the performance of proposed method in a comparison of some popular methods (t-test, SAM, LIMMA, Wilcoxon, WAD, RP and FCROS) using both simulated and real gene expression profiles for both small-and large-sample cases. From the simulation and a real spike in data analysis results we observed that the proposed method outperforms other methods for small-sample case in presence of outliers and it keeps almost equal performance with other robust methods (Wilcoxon, RP and FCROS) otherwise. From a head-and-neck cancer (HNC) dataset the proposed method identified 2 genes (CYP3A4, NOVA1) that are significantly enriched in linoleic acid metabolism, drug metabolism, steroid hormone biosynthesis and metabolic pathways. The survival analysis through Kaplan-Meier curve revealed that combined effect of these 2 genes has prognostic capability and they might be promising biomarker of HNC. Moreover, we retrieved the 12 candidate drugs based on gene interaction from glad4u and drug bank databases. Conclusion The identified drugs showed statistical significance and critical role of the proteins indicate that these proteins might be therapeutic target in cancer. Thus, elucidating the associations between the drugs identified in the present study require further investigations.

Background: The management of gestational diabetes mellitus (GDM) focuses on dietary interventions, physical activity, and pharmacological therapy. Strategies that enhance adherence to the diet could play a pivotal role in managing this population. Objective: To compare the effectiveness of outpatient nutritional guidance supplemented by digital media with exclusively standard outpatient nutritional guidance in pregnant women with GDM. Methods: This was a randomized, patient-blinded clinical trial conducted at the Diabetes and Pregnancy outpatient clinic from February 2021 to January 2023. Pregnant women with GDM were randomly allocated into two groups: the Control Group (CG) received exclusively outpatient nutritional guidance, and the Intervention Group (IG) received outpatient nutritional guidance along with reminders via WhatsApp. Dietary intake (calories, carbohydrates, lipids, proteins, and fibers) was evaluated using 24-hour dietary recalls (24HR). Glycemic control and the need for pharmacological treatment were also assessed. Mann-Whitney tests were applied to numerical variables, and Fisher's exact test or Chi-square tests were used for categorical variables. Results: 81 women were included, 34 allocated to the CG, and 47 to the IG. Patients were followed for a period of 4 to 8 weeks. Few differences were observed in glycemic control between the two groups over the follow-up period. There were no statistical differences in pharmacological therapy (p=0.498); 188 24HR were conducted in the CG and 290 in the IG. A statistically significant increase in lipid intake was observed in the IG over the follow-up period compared to the CG (p

Nowadays, the transactions carried out with digital currencies are increasing. Citizens are asked to respond to growing challenges related to the sustainable management of digital currencies in their daily lives. However, due to the lack of digital skills of users, the sustainable management of digital currencies hides risks. The methodology of this study was based on the Digital Competence Framework for Citizens (DigComp) and is applied through a questionnaire completed by 443 respondents. The main objective was to evaluate their digital skills from the perspective of digital currency use. The analysis of the responses was carried out by using Structural Equation Modeling (SEM). The most important result from this research reveals that users of digital currencies are significantly capable of developing sustainable communication to solve everyday problems. At the same time, users of digital currencies mostly detect digital threats and effectively manage fake news without being affected by them. However, users of digital currencies consider that security issues are important, but only for transactions and not for their supporting functions. The study concludes with suggestions for improving the experience of digital currency users through individual actions, thus having a positive impact on the state and banking institutions.

Music cultural heritage, as an important component of cultural heritage, possesses significant cultural value and inheritance significance. With the development of society and the passage of time, these precious traditional music cultural heritages inevitably face the dilemma of gradual depletion or even disappearance. In the digital age, effectively protecting and inheriting these music cultural heritages has become an urgent problem to be addressed. Therefore, this paper proposes an application method based on digital twin technology, exploring how to protect and inherit music cultural heritages through digital twin technology. By leveraging digital twin technology, a virtual museum dedicated to showcasing the richness and historical connotations of music cultures is created, preserving and simulating the soundscapes of historical music eras. Through the integration of audio archives, 3D modeling, and interactive displays, users can immerse themselves in the experience of historical music in the digital space. This paper evaluates the feasibility and cultural preservation value of this digital music history museum through the creation of music digital twin technology instances and user survey feedback, and discusses the prospects of digital twins in the field of music cultural heritage.

Diabetes mellitus – type 2 (DM2) is a hypercoaguable state with enhanced platelets (PLTs) activation and increased clotting factor production. Simultaneously, the fibrinolytic cell system is inhibited due to formation of clots with high resistance to fibrinolysis. The stages of PLTs "activation" have been well-characterized microscopically, morphometrically and nanomechanically (by light microscope, transmisiion electron microscope - TEM, scanning electron microscope - SEM and atomic force microscope - AFM). Thrombocytes in "acivated" (pro-coagulant) state play central role in two main biological processes: hemostasis and repair of vascular vessels. Pro-coagulant PLTs participate in generation of thrombin and in development of fibrin fibers in the clot matrix. Enhanced PLTs reactivity in dibetic- (diabetes mellitus - DM2) patients is considered as a "pro-thrombotic" state. In the retrospective- and prospective- studies on the topic, the higher are the PLTs' hematometrical indices: PLTs (count), MPV (mean platelet volume), PDW (platelet distribution width), PCR (plateletcrit), as well as PLTs/Ly ratio. The PLТs parameters/indices are usefull biomarkers in early diagnosis and prognosis of DM2. The precise studies of PLTs' state of activation during DM2 might be useful for creation of new diabetes (DM2) treatment strategies and effective medicines. Using the amount of blood glucose, attached to hemoglobin (HbA1c values) as markers of glycemic control in diabetic patients, researchers have observed association between MPV and medications as Insulin, Metformin, sulphonyl ureas. Computational modellng of PLTs' activation in DM2 is also a controlling factor for thrombocyte distribution and margination in bood vessels, associated with micro- and macrovascular disease in DM2. PLTs-derived miRNAs are novel molecular biomarkers for diagnosis and prognosis of DM2, insulin resistance and diabetes complications. We have proposed (2010) oleic acid (OA) as a substance suppressing "PLT hyperactivation" and inhibiting “thrombocyte aggregation”. Antiplatelet agents, natural plant products including, could be effective in the prevention and the secondary treatment of micro- and macrovascular complications in the type 2 Diabetes mellitus. Our proposal on following up of a combination of hematological-, hemorheological- and hemostatic parameters (indices) as a new way in diagnosis, treatment, predicting and management of DM2 and its related vascular complications, probably could be more explored in future studies.

Exosomes are nano-sized extracellular vesicles excreted by mammalian cells that circulate freely in the bloodstream of living organisms. Exosomes have a lipid bilayer that encloses genetic material used in intracellular communication (e.g., double-stranded DNA, micro-RNAs, and messenger RNA). Recent evidence suggests that dysregulation of this genetic content within exosomes has a major role in tumor progression and in the surrounding microenvironment. Motivated by this discovery, we focused here on using exosomal biomarkers as a diagnostic and prognostic tool for cancer. In this review, we discuss recently discovered exosome-derived proteomic and genetic biomarkers used in cancer diagnosis and prognosis. Although several genetic biomarkers have been validated for their diagnostic values, proteomic biomarkers are still being actively pursued. We discuss both commercial technologies and emerging technologies for exosome isolation and analysis.

Digital Evidence is considered as an important type of evidence in many legal cases. Many legislations have dedicated laws to the collection, handling and admissibility of digital evidence. New technologies and new devices are rapidly being developed, which creates new sources of digital evidence. This presents a challenge to law enforcement agencies and digital investigators to stay up to date with the rapid development in the digital field. This paper discusses a relatively new source of digital evidence which is the evidence extracted from Wearable devices. A Fitbit fitness tracker is one of the most common wearable devices used by many people today. This paper presents a case study whereby data extracted from a Fitbit was used as a digital evidence. The admissibility and the challenges of using Wearables as digital evidence is also discussed.