Existing research indicates that mineralization of Carlin-type gold deposit is related to geological structures, but current studies mostly focus on the qualitative association, and lack quantitative analysis of the relationship between mineralization and structural features, which limits the potential for predictive mineral exploration. In this study, the Nibao Carlin-type gold deposit was selected as the research area to conduct three-dimensional geological modeling, spatial analysis, and three-dimensional prospectivity modeling. Spatial analysis revealed that the majority of gold ore bodies are situated near the main fault, with gold mineralization primarily occurring within a spatial range of approximately -80 meters to 20 meters from the main fault plane. This spatial distribution predominantly aligns in a SW-NE direction. Spatial characteristic extraction of fault slope indicators suggests that the slope angles are conducive to the migration and accumulation of ore-forming hydrothermal fluids, with ore body metal content rapidly increasing within an angle range of approximately 20° to 60°. The shape analysis of the mainfault indicates that the principal spaces of mineralization enrichment are within -10m to 20m of the fault plane undulations, where gold resources are concentrated in a relatively flat area. However, the degree of mineralization is more pronounced in the protruding sections than in the depressed areas. The areas with significant dip changes in faults display higher variations in orebody attitudes, with mineralization noticeably weaker in areas where the fault steepness changes markedly. Ore-bearing units exhibit significantly higher grades within the -17° to 3° interval of fault steepness transition. This interval also sees a rapid accumulation of gold resources. The Nibao gold mine's mineralization is derived from deep, ore-bearing hydrothermal fluids ascending along major fractures, precipitating, and accumulating in favorable ore-hosting spaces (faults, anticline cores) and lithological combinations (sedimentary tuffs, tuffs). Three deep potential gold targets were identified, highlighting the deep western edge of the F1 fault and the deep southeast-trending lower plate as prospective areas for future exploration. This study underscores the effectiveness of 3D prospective modeling as an indispensable tool for elucidating the spatial distribution of fault-controlled Carlin-type gold deposits and their tectonic determinants.

Water quality assessment in pastoral of Tibetan Plateau, which is water sources for about 40% of world's population and the runoff-yield area of Yellow rivers, is very important. In this paper, Xiahe county which belongs to Tibetan Plateau pastoral was investigated. Six parameters(via, chloride, COD, ammonia nitrogen, nitrate, fluoride, sulfate) were selected to assess the water quality and health degree by using fuzzy comprehensive evaluation methods. The hydrochemical type in surface water was of HCO3--Mg2+-Ca2+ type. The cations and anions in surface water were mainly from weathering and dissolution of carbonate rock. Results showed that the water quality in all 69 sampling sites was all of class Ⅰ. The integrated health degree reached more than 0.85 and the health rate were 100%. Although ammonia nitrogen was regarded as the main contamination factor, but it had a little effect on the entire body of water. Overall, the surface water qualities of most samples in Xiahe County was good condition.

Background: The validation of laser usage during laparoscopic procedures, notably by Camran Nezhat in the late 1980s, has been significant. Lasers offer precision and depth control in tissue vaporization without bleeding. Surgical intervention remains central in managing endometrio-sis-associated pain and infertility, especially for patients unresponsive to hormonal therapy. Methods: This retrospective cohort study included 200 patients with SPE who underwent laparo-scopic laser vaporization. Surgery was performed using a CO2 laser, and histological confirma-tion of endometriosis was obtained for all cases. Pain scores and SF-36 questionnaire domains were assessed preoperatively and postoperatively. Fertility outcomes were evaluated among pa-tients desiring pregnancy. Results: Significant improvements in pain score and SF-36 questionnaire domains were ob-served postoperatively (p-value

OBJECTIVES: To assess the predictive value of serum trypsin and Trypsinogen Activation Peptide (TAP) for the severity of AP by means of a single center cohort study as well as a systematic review of the current literature. METHODS: The literature search was conducted using Medline (PubMed), EMBASE and the Cochrane Central Register. A total of 142 patients with acute pancreatitis (AP) were included in the cohort study and parameters of the revised Atlanta criteria of 2012 and the APACHE II were assessed. RESULTS: The review showed promising results for the predictive value of serum trypsinogen-2, but conflicting results for serum TAP and trypsin. In the cohort study, patients were observed for 4 days after diagnosis of AP: 9 patients had severe AP, 35 patients had moderate AP and 81 patients had mild AP. The ratio of the geometric mean of severe vs mild AP for trypsin was 0.72 (95% CI: 0.51-1.00), p=0.053 and for TAP 0.74 (95% CI: 0.54-1.01), p=0.055 respectively. CONCLUSION: The cohort study showed an inverse correlation of serum levels of TAP and trypsin with severity of AP. Serum TAP and trypsin have an inferior predictive value of severity of AP compared to the clinical APACHE II score.

Prostate cancer is the second most frequent malignancy (after lung cancer) in men worldwide. Prostate tissue biopsies are usually graded using scores according to the Gleason grading system. This Gleason score is the most popular prognostic marker that reveals the potential aggressiveness of the disease. However, inter-observer variability in rating by human assessors is a major limiting factor. Such variability could lead to missing a severe case or suggesting unnecessary treatments. This study explores the discriminative ability of artificial intelligence (deep learning models) for Gleason score assessment. The study was designed to use whole-slide images of digitized H&E-stained biopsies of prostate tissues to automate the grading process and provide a remotely accessible clinical decision support system. Custom convolutional neural network architectures were trained on 10,616 images of prostate tissues. Gaussian filters were applied to pre-process the images and improve model performance. Transfer learning was applied to train eight machine learning architectures namely: Xception, VGG16 & 19, ResNet101, MobileNet, DenseNet121, EfficientNetB5 & B7. Efficient NetB7 had the best performance 85.2% compared with ground-truth classification by experts. Performance improves as more data is available. The model was deployed and hosted as a web application API on Google cloud service to ensure remote access. Tissue biopsy images can be uploaded and the corresponding Gleason score recovered immediately. This system reduces diagnostics turnaround time, increase throughput, and compensate for limited skills, especially in low resource settings.

Endometriosis is a common finding among women with infertility and women who are diag-nosed with endometriosis are almost twice as likely to experience infertility. Mechanisms by which endometriosis causes infertility remain poorly understood. In this review we evaluate the current literature on the impact of endometriosis on oocyte and embryo quality. The presence of endometriosis evidently reduces ovarian reserve, oocyte quality and embryo quality, however this does not appear to translate to a clear clinical impact. Analysis of data from large assisted reproduction technology registries has shown that women with endometriosis have a lower oo-cyte yield but no reduction in reproductive outcomes. There is a need for future studies in the form of well-designed randomised controlled trials to further evaluate the role of surgical and medi-cal treatment options in women with endometriosis undergoing assisted conception.

Peritoneal fluid is an ovarian exudate from the growing follicle or corpus luteum. Therefore, the free concentrations of estrogens and progesterone are always much higher than in plasma, especially after ovulation. The peritoneum is not vascularised, and peritoneal implants and the superficial portion of deep endometriosis are likely influenced mainly by peritoneal fluid. Follicular phase progesterone concentrations are as high as during the luteal phase in plasma. This explains that most peritoneal implants are rarely proliferative and out of phase with the endometrium. From some 5 mm of depth onwards, endometriosis is rather influenced by plasma hormone concentra-tions, and the deeper parts can be proliferative as the endometrium. Because of these progesterone concentrations, progesterone resistance has to be postulated, permitting endometriosis lesions to initiate and develop. This opens the discussion of whether endometriosis is similar to the basal endometrium. These peritoneal fluid concentrations are important to understand the natural history and medical therapy of endometriosis. Most medical therapies decrease ovarian function and affect peritoneal fluid steroid hormone concentrations. It is suggested that the efficacy of medical therapy for endometriosis-associated pain is a consequence of the decreased estrogen concentrations in the peritoneal cavity and that the local effect of progestins may not be determinant for pain relief. If this is true, a history of endometriosis should not be considered a contraindication for hormone re-placement therapy.

Small interfering RNA (siRNA) holds significant therapeutic potential by silencing target genes through RNA interference. Current clinical applications of siRNA have been primarily limited to liver diseases, while achievements in delivery methods are expanding its applications to various organs, including the lungs. Cholesterol-conjugated siRNA emerges as a promising delivery approach due to its low toxicity and high efficiency. This study focuses on developing a cholesterol-conjugated anti-Il6 siRNA and the evaluation of its potency for the potential treatment of inflammatory diseases using the example of acute lung injury (ALI). The biological activities of different Il6-targeted siRNAs containing chemical modifications were evaluated in J774 cells in vitro. The lead cholesterol-conjugated anti-Il6 siRNA after intranasal instillation demonstrated dose-dependent therapeutic effects in a mouse model of ALI induced by lipopolysaccharide (LPS). The treatment significantly reduced Il6 mRNA levels, inflammatory cell infiltration, and the severity of lung inflammation. IL6 silencing by cholesterol-conjugated siRNA proves to be a promising strategy for treating inflammatory diseases, with potential applications beyond the lungs.

Endometriosis is a disease that affects women of reproductive age and has a significant impact on their well-being. The main symptoms are dysmenorrhea, chronic pelvic pain and infertility. The diagnostic process in many cases is very long and can take up to 8-12 years. Laparoscopy, which is an invasive method, is still necessary to confirm final identification. Therefore, the development of diagnostic markers seems to be crucial for the diagnosis and proper treatment of women affected by endometriosis as soon as possible. Still the most frequently studied and used marker is Cancer Antigen 125 (CA-125). Other glycoproteins, growth factors and immune markers seem to play an important role. However, the search for the ideal endometriosis marker is still ongoing. Developing researches on endometriosis pathogenesis help to identify potential biomarkers or sets of biomarkers in order to improve and speed up the diagnostic process in a non-invasive way.

Conclusions: Our findings suggest that the NMS-E represents a valuable preoperative diagnostic tool for endometriosis, effectively correlating with the disease's severity and surgical outcomes. Incorporating the NMS-E into clinical practice could significantly enhance the management of endometriosis by addressing current diagnostic limitations and guiding surgical planning.

This paper analyses the application of deep learning techniques for predicting wave overtopping events in port environments using sea state and weather forecasts as inputs. The study was conducted in the outer port of Punta Langosteira, A Coruña, Spain. A video recording infrastructure was installed to monitor overtopping events from 2015 to 2022, identifying 3709 overtopping events. The data collected was merged with actual and predicted data for the sea state and weather conditions during the overtopping events, creating three datasets. We used these datasets to create several machine learning models to predict whether an overtopping event would occur based on sea state and weather conditions. The final models achieved a high accuracy level during the training and testing stages: 0.81, 0.73, and 0.84 average accuracy during training and 0.67, 0.48, and 0.86 average accuracy during testing, respectively. The results of this study have significant implications for port safety and efficiency, as wave overtopping events can cause disruptions and potential damage. Using deep learning techniques for overtopping prediction can help port managers take preventative measures and optimize operations, ultimately improving safety and helping to minimize the economic impact that overtopping events have on the port's activities.

Endometriosis is an oestrogen-dependant reproductive disease, with genetic, vascular, neural, inflammatory and auto-immune characteristics. There are many theories about the etiology of endometriosis, however, all of these theories have limitations and do not explain all the locations that endometriosis is found or types of patients with endometriosis. The objective of this paper is to postulate the hypothesis that endometriosis is caused by Maternal Microchimerism, the presence of maternal cells in the fetus. A literature review was conducted, analysing the characteristics, current etiological theories of endometriosis, theory limitations and relationship of maternal microchimerism and endometriosis. At time of writing, there was no literature on maternal microchimerism and endometriosis. These results suggest that Maternal Microchimerism could be a cause of endometriosis. This could account for the genetic and auto-immune characteristics seen in people with endometriosis, inducing a micro-environment for vascular, neural and epigenetic changes. This could also account for account for endometriosis seen in non-menstruating patients, such as men, fetuses and post-menopausal women and endometriosis found in non-peritoneal locations. If the hypothesis of Maternal Microchimerism is correct, endometriosis could be considered a pregnancy-related disease that could affect all humans, changing the accepted demographics of patients and potentially new diagnostic techniques and treatment options for patients with endometriosis. Further studies are needed to test this hypothesis.

Day-ahead forecasting of electricity prices is important in deregulated electricity markets for all the stakeholders: energy wholesalers, traders, retailers, and consumers. Electricity price forecasting is an inherently difficult problem due to its special characteristic of dynamicity and non-stationarity. In this paper, we present a robust price forecasting mechanism that shows resilience towards aggregate demand response effect and provides highly accurate forecasted electricity prices to the stakeholders in a dynamic environment. We employ an ensemble prediction model in which a group of different algorithms participate in predicting the price for each hour of a day. We propose two different strategies, namely, Fixed Weight Method (FWM) and Varying Weight Method (VWM), for selecting each hour's expert algorithm from the set of participating algorithms. In addition, we utilize a carefully engineered set of features selected from a pool of features derived from information such as past electricity price data, weather data, and calendar data. The proposed ensemble model offers better results than both the Pattern Sequence-based Forecasting (PSF) method and our own previous work using Artificial Neural Networks (ANN) alone do on the datasets for New York, Australian, and Spanish electricity markets.

According to cancer death rates for women worldwide, this form of cancer ranks fourth after breast, bronchopulmonary, and colorectal cancer, affecting around 570,000 women annually. About 270,000 women each year pass away from this illness, 85% of them are from underdeveloped nations where cervical cancer claims more lives than any other kind of cancer. In Romania, cervical cancer ranks fourth in terms of death, behind breast, bronchopulmonary, and colorectal cancers, and second in terms of incidence, behind breast cancer. Endometriosis in the cervical region is quite uncommon. Only a tiny fraction of women had the condition upon diagnosis. It might be challenging to detect cervical endometriosis since it rarely manifests any symptoms or markers. Women who are asymptomatic can manage the condition expectantly; however, if the symptoms return often, surgery may be required. As many patients have concurrent diseases such fibroids, adenomyosis, ovarian cysts, and pelvic endometriosis, cervical endometriosis therapy appears to be straightforward. There ought to be surgical intervention. Symptomatic cervical endometriosis, such as irregular or post-coital bleeding, may be treated surgically using the LLETZ surgery or another cervical ablation approach. Background and Objectives: (1) To assess one's degree of knowledge on the signs, causes, and methods of preventing cervical cancer or cervical endometriosis, as well as screening for these conditions. (2) To ascertain how the responder behaves about cervical cancer screening and prevention, as well as endometriosis in the cervical region. Materials and Methods: A case report about cervical cancer or cervical endometriosis was conducted among a patient from Romania, Timisoara. Results: In this study, we report the patient with cervical endometriosis, although he was initially diagnosed with cervical cancer. Conclusions: Cervical cancer, a condition that can have serious repercussions on the patient's health, even if there is no clear picture of paraclinical investigations, to raise the suspicion of cervical endometriosis and not cervical cancer.

Feeling pain means feeling oneself, as the body feels discomfort as a kind of "disturbance experi-ence". Endometriosis is a disease in which the predominant symptom is pain felt by women pri-marily within the pelvis, but also outside of the body. The clinical biomarker used in the diagnosis of this disease is microRNAs. Treatment of endometriosis is complex and is based mainly on phar-macotherapy. Physiotherapy can complement pharmacological treatment in reducing pain using non-invasive treatments for the body. Therefore, databases have been searched to find the most effective forms of physiotherapy for the treatment of endometriosis. The literature review conducted here suggests that it is necessary to perform multicentre studies in order to establish which forms of physical therapy may be most effective in reducing pain and improving the quality of life of women suffering from endometriosis. Certainly, this type of research in conjunction with the assessment of pain and endometriosis markers would be of great clinical value in order to develop an interdisci-plinary treatment model.

A PLS-based quality prediction method is proposed for batch processes using two-dimensional extended windows. To realize the adoption of information in the direction of sampling time and batch, a new definition region of support (ROS), k-i-back-extended region of support (KIBROS), is proposed to establish the extended window, which including the data of the previous sampling times in the same batch, the data of the same sampling time in the historical batches, the data of the previous sampling times in the historical batches and the data of the later sampling times in the historical batches. According to these regions of support, extended windows are established, and different models are proposed based on the extended windows for batch processes quality prediction. Further, using a typical batch process as an example, the injection molding process, the proposed quality prediction method is experimentally verified, proving that the proposed methods have higher prediction accuracy than the traditional method and the prediction stability is also improved.

Endometriosis occurs when endometrial-like tissue grows outside the uterine cavity, leading to pelvic pain, infertility, and increased risk of ovarian cancer. The present study describes the optimization and characterization of cellular spheroids as building blocks for Kenzan scaffold-free method biofabrication and proof-of-concept models of endometriosis and the endometriotic microenvironment. The spheroid building blocks must be a specific diameter (~500 m), compact, round, and smooth to withstand Kenzan biofabrication. Under optimized spheroid conditions for biofabrication, the endometriotic epithelial-like cell line, 12Z, expressed high levels of estrogen-related genes and secreted high amounts of endometriotic inflammatory factors that were independent of TNF stimulation. Heterotypic spheroids, composed of 12Z and T-HESC, an immortalized endometrial stromal cell line, self-assembled into a biologically relevant pattern, consisting of epithelial cells on the outside of the spheroids and stromal cells in the core. 12Z spheroids were biofabricated into large three-dimensional constructs alone, with HEYA8 spheroids, or as heterotypic spheroids with T-HESC. These three-dimensional biofabricated constructs containing multiple monotypic or heterotypic spheroids represent the first scaffold-free biofabricated in vitro models of endometriosis and the endometriotic microenvironment. These efficient and innovative models will allow us to study the complex interactions of multiple cell types within a biologically relevant microenvironment.

Abstract We aim to describe the diagnosis and surgical management of urinary tract endometriosis (UTE). We detail current diagnostic tools including advanced transvaginal ultrasound, magnetic resonance imaging, and surgical diagnostic tools such as cystourethroscopy. While discussing surgical treatment options, we emphasize the importance of an interdisciplinary team, for complex cases that involve the urinary tract. While bladder DE is more straightforward in its surgical treatment, ureteral DE requires a high level of surgical skill. Specialists should be aware of the important entity of UTE due to the serious health implications for women. When UTE exists, it is important to work within a interdisciplinary radiological and surgical team.

Background: Asthma is a widespread chronic respiratory disease that poses a significant public health challenge. The current study investigated the associations between air pollution and asthma severity among individuals residing near the Sohar industrial port (SIP) in Oman. Despite the presence of multiple major industrial complexes in Oman, limited knowledge regarding their impact on respiratory health is accredited. Hence, the primary objective of this study is to offer valuable insights into the respiratory health consequences of industrial air pollution in Al Batinah North. Methods: The state health clinics' records for patient visits related to asthma were collected for the timeframe spanning 2014 to 2022. Exposure was defined as the distance from SIP, Majan Industerial Area (MIA) and Sohar Industerial Zone (SIZ) to determine high, intermediate, and low exposure zones (12 km, respectively). Exposure effect modifications by age, gender, and smoking status were also examined. Results: The conducted cross-sectional study of 410 patients (46.1% males and 53.9% females) living in over 17 areas around SIP revealed that 73.2% of asthmatics were under 50 years old, with severity significantly associated with closeness to the port. Risk ratios were estimated to be (RR:2.42; CI95%: 1.01–5.78), (RR:1.91; CI95%: 1.01–3.6), and (RR:1.68; CI95%: 0.92–3.09) for SIP, MIP and SIZ areas, respectively, compared to the control area. Falaj Al Qabail (6.4 km) and Majees (6 km) had the highest number of asthma patients (N. 69 and N. 72), and highest percentages of severe asthma cases among these patients (28% and 24%) with significant risk ratios (RR:2.97; CI95%: 1.19–7.45 and RR:2.55; CI95%: 1.00–6.48), correspondingly. Moreover, severe asthma prevalence peaked in the 25-50 age group (RR:2.05; CI95%: 1.26–3.33), and this linkage between asthma and age was much more pronounced in males than females. Smoking and exposure to certain contaminant (dust and smoke) increased also the risk of severe asthma symptoms, but their effects were less important in the high risk zone, suggesting much more important risk factor. A neural network model accurately predicted asthma risk (94.8% accuracy), with proximity to Sohar Industrial Port as the most influential predictor. Conclusion: The study highlights the High asthma burden near Sohar Industrial Port, linked to port proximity and smoking and wind direction as major risk factors. These findings inform vital public health policies to reduce air pollution and improve respiratory health in the region, prompting national policy review.

In the dynamic landscape of the telecommunications industry, understanding and mitigating customer churn is paramount for maintaining competitive advantage and sustaining revenue streams. This comprehensive literature review delves into the intricacies of customer churn prediction, a crucial aspect in fortifying the bond between telecom businesses and their customers. The review methodically analyzes 11 articles published between 2000 and March 2024, spotlighting the strategic role of Customer Relationship Management and the prevalence of high churn rates that necessitate a shift from customer acquisition to retention strategies. Furthermore, the review navigates through the challenges inherent in churn prediction, including the complexities of managing large, imbalanced, and noisy datasets and the intricacies involved in model selection and optimization. Despite these challenges, the paper highlights the superior capability of machine learning techniques in handling complex datasets to provide accurate predictions, albeit acknowledging their practical complexities. The main aim of the study is to provide an overview, serving as a knowledge base for researchers interested in machine learning techniques within the realm of telecommunications.

Background: The modified Duke index derived from coronary computed tomography angiography (CCTA) was designed to predict cardiovascular outcomes based on the severity of coronary ste-nosis. However, it does not take into consideration the presence or severity of peri-coronary in-flammation. The peri-coronary fat attenuation index (FAI) is a novel imaging marker determined by CCTA which reflects the degree of inflammation in the coronary tree in patients with coronary artery disease. Purpose: To assess the association between the modified Duke index assessed by CCTA, cardiovascular risk factors and peri-coronary inflammation in the coronary arteries of pa-tients with coronary artery disease. Methods: One hundred seventy-two patients who underwent CCTA for typical angina were assigned into two groups based on the modified Duke index: group 1 – patients with low index ≤ 3 (n=107) and group 2 – patients with high index > 3 (n=65). Demo-graphic, clinical and CCTA data were collected for all patients, and FAI analysis of coronary in-flammation was performed. Results: Patients with increased values of the modified Duke index were significantly older compared to those with a low index (61.83 ± 9.89 vs. 64.78 ± 8.9; p = 0.002). No differences were found between the two groups in terms of gender distribution, hypertension, hypercholesterolemia or smoking history (all p > 0.5). The FAI score was significantly higher in patients from group 2, who presented a significantly higher score of inflammation compared to the patients in group 1, especially at the level of the right coronary artery (FAI score 20.85 ± 15.80 vs. 14.61 ± 16.66; p = 0.01 for the right coronary artery, 13.85 ± 8.04 vs. 10.91 ± 6.5; p = 0.01 for the circumflex artery, and 13.26 ±10.18 vs. 11.37 ± 8.84; p = 0.2 for the left anterior descending artery). CaRi-Heart® analysis identified a significantly higher risk of future events among patients with a high modified Duke index (34.84 % ± 25.86 % versus 16.87 % ± 15.80 %; p

Herein, we examined the effects of weather parameters and individual clinicodemographic risk factors on hemorrhagic stroke (HS) incidence, severity on admission, and disability at discharge in a harsh desert climate. In a retrospective design we studied patients admitted to a stroke unit in Arab Emirates in 2016-2019. With a distributed lag nonlinear model we explored immediate and delayed effects of weather on stroke incidence. Supervised machine learning was used to build models predictive of scores in NIHSS and mRS scales. We assessed model performance by calculating ROC AUC, F1 scores, specificity, and sensitivity. HS risk increased after a significant change in any weather parameter. The risk was reduced below baseline after 1-week adjustment to environmental changes. Climate input from the previous 3 days yielded reliable classification models. Humidex was a better predictor of severity than air temperature, whereas body mass index, age, and time of day at stroke onset were the strongest clinical predictors. Weather parameters before admission were stronger predictors of disease severity than individual clinical factors. We addressed limitations of previous studies by analyzing a full set of climate parameters: ambient temperature, relative humidity, humidex, atmospheric pressure, and wind speed. Predictive models may help to optimize patient management and develop preventative strategies.

By repeated unloading and loading of the excavation dynamic and static loads, repeated train dynamic and static loads of multi-factors, the underpass tunnel, cross tunnel engineering influence area rock layer, the existing tunnel structure of the three constitutes a multifaceted structure, multi-factor interaction system. Existing tunnels into the prediction theory model of the new tunnel excavation process of dynamic and static loads, dynamic and static loads of existing tunnels are mostly analyzed individually, the results of synergistic analysis needs to be improved. Based on the theory of the current layer method, the paper comprehensively considers the stiffness of the existing tunnel, the stiffness of the new tunnel, the excavation method load and the existing tunnel load, coupled with Peck's empirical formula, the theoretical model of deformation prediction for excavation of a two-lane highway tunnel under the tunnel through the existing railroad is proposed, and verified and optimized by numerical calculations and monitoring data, which proves the applicability and reliability of the theoretical model in the similar strata and the project promotion.

The focus of the presented work was to isolate and characterize circulating endometrial cells (CECs) enriched from peripheral blood (PB) of patients with diagnosed endometriosis to support endometriosis diagnosis and treatment. Material and Methods Blood samples (n=423) were tested for CECs presence. Subsequently, gene expression analysis (GEA) was carried out for CECs. In parallel, the presence and character of CECs was tested during phases of menstrual cycle (MC) (n= 11). CECs were isolated by size-based separation from 2x 8ml PB. Results CECs were detected in 78.4% of blood samples. In line with the revised American Fertility Society (rAFS) classification CECs presence was confirmed in all the acknowledged endometriosis stages: minimal, mild, moderate and severe. Surprisingly, the highest CEC negativity rate was reported for severe disease (21.1%). The highest CEC numbers were detected in the mid-secretory periods of MC, which corresponds with uterine lining decidualization. The cytomorphology of CECs captured during MC is changing between the epithelial, stromal and stem cell-like. CECs captured in mid-secretory period expressed KRT18, NANOG and VIM in higher amounts when compared to the genes in the proliferative phase of MC when KRT19 and ESR1 were mostly observed. GEA of the super-positive CECs samples (1000 CECs/8mL PB) revealed high expression of KRT18, VIM, NANOG and FLT1. The expression of these genes was elevated in the endometriosis tissue samples and endometrioma, too. Conclusion The panel of the identified CECs - genes could be tested in a prospective manner to confirm the predictive value of CECs in endometriosis diagnostics.

This paper describes a system that is used to gather data from Smart Home sensors measuring energy consumption, analyze and predict future consumption by using machine learning algorithm for prediction. It focuses on the architecture of the system as well as the need for such system to exist in practice – why the consumption should be measured at all and how that measurement can be put to good use for achieving energy efficiency.

The Mara River basin (MRB) has a world-famous ecosystem, but the vegetation has been damaged due to economic development in recent years, and there is little known about the area that will experience severe vegetation damage in the future. Based on the vegetation vulnerability system, principal component analysis, and three CMIP6 scenarios (SSP1-2.6, SSP2-4.5, SSP5-8.5), vegetation vulnerability was calculated in the base period (2010-2019), near-term period (2020-2059) and long-term period (2060-2099) in the MRB. The spatial cluster of vegetation vulnerability was revealed by spatial correlation analysis, and the transition of vegetation vulnerability of different periods was analyzed by stochastic matrix. The results showed that in all periods, the vulnerability showed a high-high cluster in the east, and a low-low cluster midstream and downstream. From the stochastic matrix, the area of high vulnerability increased the least under the SSP1-2.6, while it increased the most under the SSP5-8.5. The vegetation vulnerability upstream increased the most from the base period to the near-term period and long-term period in the MRB. By comparing the vegetation vulnerability under different scenarios, and pointing out the areas with the highest vulnerability increase, this study can better provide comprehensive decision-making for vegetation protection in the MRB.

Obsessive–compulsive disorder (OCD) is believed to follow a waxing and waning course, often according to environmental stressors. During the COVID-19 pandemic, pre-existing OCD symptoms were reported to increase and to change from checking to washing behaviors, while new-onset symptoms were predominantly of the hoarding type. In the present study, we followed the evolution of OCD symptoms, anxiety, depression, and insight of illness in forty-six OCD patients throughout the pandemic. Clinical measures were collected at four different time points before and during the COVID-19 pandemic in Italy. Within-subject comparisons were used to compare clinical scale scores across time, and correlations were examined between patients' baseline characteristics and changes in clinical scores. We found that all clinical measures increased during the first Italian lockdown, with respect to the pre-pandemic values. Anxiety decreased during the temporary elimination of restriction provisions, whereas severity of OCD symptoms and insight returned to pre-pandemic values during the second mandatory lockdown. These results were observed only in two sub-groups of patients: those taking benzodiazepines and those with shorter illness duration. Our findings suggest the need of additional clinical attention to these specific sub-groups of OCD patients in case of particularly distressing circumstances, while pointing to a possible adaptive role of their OCD symptoms when the environment requires a higher care of hygiene and extraordinary supply of essential resources.

There is a growing interest in the endocannabinoid system and the gut microbiota. Both areas have been suggested to play a role in endometriosis, however this is yet to be studied comprehensively. The purpose of this narrative review is to identify the gaps in endometriosis research in the context of these two areas. It underscores the limited knowledge regarding the involvement of the endocannabinoid system and gut microbiota in endometriosis. Further research in these areas is essential to unlock the therapeutic potential of cannabis and enhance the understanding of endometriosis pathogenesis.

This paper explores the capability of various machine learning algorithms, including Random Forests and advanced gradient boosting techniques such as XGBoost, LightGBM, and CatBoost, to predict customer churn in the telecommunications sector. For this analysis, a dataset available to the public was employed. The performance of these algorithms was assessed using recognized metrics, including Accuracy, Precision, Recall, F1-score, and the Receiver Operating Characteristic Area Under Curve (ROC AUC). These metrics were evaluated at different phases: subsequent to data preprocessing and feature selection; following the application of SMOTE and ADASYN sampling methods; and after conducting hyperparameter tuning on the data that had been adjusted by SMOTE and ADASYN. The outcomes underscore the notable efficiency of upsampling techniques such as SMOTE and ADASYN in addressing the imbalance inherent in customer churn prediction. Notably, the application of random grid search for hyperparameter optimization did not significantly alter the results, which remained comparatively unchanged. The algorithms' performance post- ADASYN application marginally surpassed that observed after SMOTE application. Remarkably, LightGBM achieved an exceptional F1-score of 89% and an ROC AUC of 95% subsequent to the ADASYN sampling technique. This underlines the effectiveness of advanced boosting algorithms and upsampling methods like SMOTE and ADASYN in navigating the complexities of imbalanced datasets and intricate feature interdependencies.

This study investigated the influence of single nucleotide polymorphisms (SNPs) in genes asso-ciated with the interferon pathway (IFNAR2 rs2236757), antiviral response (OAS1 rs10774671, OAS3 rs10735079), and viral entry (ACE2 rs2074192) on COVID-19 severity and their association with non-alcoholic fatty liver disease (MAFLD). We did not observe a significant association between the investigated SNPs and COVID-19 severity. While the IFNAR2 rs2236757 A allele correlated with higher creatinine levels upon admission and the G allele with lower band neu-trophils upon discharge, these findings require further investigation. The distribution of OAS gene polymorphisms (rs10774671, rs10735079 did not differ between MAFLD and non-MAFLD patients. Our study population's distribution of ACE2 rs2074192 genotypes and alleles differed from the European reference population. Overall, our findings suggest that these specific SNPs may not be major contributors to COVID-19 severity in our patient population, highlighting the potential role of other genetic factors and environmental influences.

Rett Syndrome (RTT) is a severe neurodevelopmental disorder predominately diagnosed in females and primarily caused by pathogenic variants in the X-linked gene Methyl-CpG Binding Protein 2 (MECP2). Most often, the disease causing MECP2 allele resides on the paternal X chromosome while a healthy copy is maintained on the maternal X chromosome with inactivation (XCI) resulting in mosaic expression of one allele in each cell. Preferential inactivation of the paternal X chromosome is theorized to result in reduced disease severity; however, establishing such a correlation is complicated by known MECP2 genotype effects and an age dependent increase in severity. To mitigate these confounding factors, we developed an age- and genotype-normalized measure of RTT severity by modeling longitudinal data collected in the US Rett Syndrome Natural History Study. This model accurately reflected individual increase in severity with age and preserved group-level genotype specific differences in severity allowing for the creation of a normalized clinical severity score. Applying this normalized score to a RTT XCI dataset revealed that XCI influence on disease severity depends on MECP2 genotype with a correlation between XCI and severity observed only in individuals with MECP2 variants associated with increased clinical severity. This normalized measure of RTT severity provides the opportunity for future discovery of additional factors contributing to disease severity that may be masked by age and genotype effects.

Although the water absorption feature (WAF) at 970 nm is not very well-defined, it may be used alongside other indices to estimate the canopy water content. The individual performance of a number of existing vegetation water content (VWC) indices against the WAF is assessed using linear regression model. We developed a new Combined Vegetation Water Index (CVWI) by merging indices to boost the weak absorption feature. CVWI showed a promise in assessing the vegetation water status derived from the 970 nm absorption wavelength. CVWI was able to differentiate two groups of dataset when regressed against the absorption feature. CVWI could be seen as an easy and robust method for vegetation water content studies using hyperspectral field data.

Accurate diagnosis for the 400 million people with rare diseases is critical for healthcare decisions, prognosis, understanding disease mechanisms, and identification of treatments. Despite advances in genome sequencing, barriers such as high interpretation costs, diagnostic expertise, throughput associated delays, and uncertain variant classifications persist, with demand exceeding capacity. Many variant classification methods focus narrowly on specific consequences, leading to the use of complex integrative pipelines that often overlook transcript variability and lack prediction transparency. To overcome these limitations, we introduce DITTO. This transparent, transcript-aware machine-learning method demonstrates superior overall performance in accuracy, recall, and precision when compared to existing tools. DITTO is publicly available at https://github.com/uab-cgds-worthey/DITTO

Background/Objectives: Endometriosis represents substantial direct and indirect healthcare costs impacted by absent accurate, non-invasive uniformly diagnostic tools. We endeavored to demonstrate gastrointestinal myoelectrical activity (GIMA) biomarkers unique to endometriosis allow noninvasive, uniformly accurate diagnosis or exclusion of endometriosis. Methods: Prospective open-label comparative study of 154 patients, age >18, with or without diagnosed endometriosis. Population included 62 non-endometriosis controls (Cohort 1), 43 subjects with surgically histologically-confirmed endometriosis (Cohort 2), and 49 subjects with abdominal pain and negative imaging (Cohort 3). Electroviscerography (EVG) recorded GIMA biomarkers from three abdominal electrodes before and 30-minutes post water-load protocol. Cohort 2 had postoperative EVG and Cohort 3 had preoperative EVG. Calculated specificity, sensitivity, NPV, PPV and predictive probability or C-Statistic used univariate, multivariate, linear and logistical regression analyses of the area under the curve (AUC) at all frequency and time points, including age and pain covariants. Results: Non-endometriosis cohort differed significantly from endometriosis cohorts (p<0.001) for median (IQR), and AUC percent frequency distribution of power at baseline, 10-minute, 20-minute, and 30-minute post water-load at all frequency ranges 15-20cpm, 30-40cpm and 40-50cpm. Endometriosis cohorts were statistically similar (p>0.05). GIMA biomarker threshold scoring demonstrated 95%/91% sensitivity and PPV, 96%/95% specificity and NPV and C-statistic of >99%/98% respectively for age subsets. GIMA biomarkers in Cohort 3 predicted 47/49 subjects positive and 2/49 negative for endometriosis, confirmed surgically. Hormonal therapy, surgical stage, nor pain score affected diagnostic accuracy. Conclusions: Non-invasive electroviscerography with GIMA biomarker detection distinguished participants with and without endometriosis based upon endometriosis specific GIMA biomarkers threshold scoring.

Background: Endometriosis affects between 1% to 10% of women worldwide; it is associated with a significant burden on the woman, her relationships, productivity, mental health, family and society. Aim:To contribute to the current understanding of the impact of endometriosis on women’s lives by integrating qualitative research findings to explore the illness experiences of women living with endometriosis. Study design:Synthesis of qualitative data using Britten’s meta-ethnography.Methods:Six bibliographic databases (Medline, Embase, CINAHL, Web of Science, Scopus and PsycINFO) and Google Scholar were searched for peer-reviewed papers published in English language from inception of until August 6th 2021. Results: Fifteen studies were included in the data synthesis. The review included a total of 354 women from fourteen countries (including South Africa, Iran, England, Australia and United States of America), of age range 16-78 years. Subjects represented diverse ethnicities, cultures and native languages distributed across socioeconomic classes. Representation of all stages of endometriosis was included.Findings incorporate the following nine categories into a conceptual model: disease symptoms; health services experience; isolation and limited social participation; limited physical functioning; a coterie of emotions; sex and intimate relationships; infertility; work life and education; coping strategies and support. Diagnostic delays, persistent symptoms, healthcare costs and inadequate education to patients about the disease; all intersect into a web of struggles and incoherence for patients. Conclusion:Women with endometriosis living in various countries report similar illness experiences; although gender roles, culture and socio-economic status may act as mediating factors that shape an individual’s illness experience.

Plasma microRNAs are considered to be potential diagnostic biomarkers for endometriosis. Increasing evidence has shown that a huge amount of miRNAs are abnormally expressed in endometriosis plasma and play irreplaceable roles in diagnosis. The aim of the our study was to identify the differential expression of circular miRNA by reviewing the PubMed, ScienceDirect, and Cochrane databases between normal women and women with endometriosis and analyzing the miRNA data downloaded from the GEO database. Because of the differential miRNA expression in this review, we evaluated the diagnostic values of the differentially expressed miRNAs, particularly during the menstrual phases. According to the cut-off criteria with |log 2 FC|>1.0 and P < 0.05, 36 differentially expressed miRNAs were identified, including 13 upregulated miRNAs and 23 downregulated miRNAs. We developed miR-155, miR-574, miR-23a, and miR-520d via a Venn diagram. Functional enrichment analysis considered that the target miRNAs might be involved in various pathways related to endometriosis, including neurotrophin, Hippo, oocyte meiosis, ubiquitin mediated proteolysis, HTLV-Infection, FoxO, and Rap1 signaling pathways. CTNNB1, MYC, and ES R1 of transcription factors were related to the differentially expressed miRNAs. In summary, our study suggested that a four-miRNA could be included as a prognostic marker in endometriosis.

Heavy rains have created significant threats to human health and life. Floods and other natural disasters, which have a global impact annually, can be attributed to extended durations of intense precipitation. Accurate rainfall predictions are crucial in nations such as Bangladesh, where agriculture is the predominant occupation. The efficiency of machine learning (ML) methods is enhanced by the non-linearity of rainfall, surpassing the effectiveness of other alternative ways. Machine learning techniques show that individual classifiers exhibit worse accuracy than ensemble learning (EL) methodologies. Ensemble Learning techniques are utilized for rainfall prediction and estimating rainfall quantity and daily average temperature to enhance comprehension of the diverse Machine Learning algorithms. This research implements the machine learning techniques and ensemble-based classifier to predict the rainfall occurrence, along with the machine learning regressor models and ensemble-based regressor for the rainfall amount prediction and daily average temperature prediction, using Bangladesh Weather Dataset. The results of the machine learning and ensemble-based models are compared using the Accuracy and F1 score for rainfall occurrence prediction. In contrast, MAE and RMSE evaluation metrics are used for ensemble regressor and regression algorithms for the rainfall amount and daily average temperature prediction. With an accuracy of 83.41% and a recall of 78.17%, the Ensemble Classifier is the best at predicting when it will rain, but its precision of 51.16% stands in for the lowest. The Ensemble Regression model outperforms Linear Regression, Random Forest, and SVR in rainfall amount prediction, with the lowest MAE of 0.36 and RMSE of 0.90. The Ensemble Regression provides the most precise results for daily average temperature prediction with the lowest MAE 0.42 and RMSE 0.54 highlighting its superiority over the other regression models in forecasting temperature with less error. Ensemble approaches consistently lead task performance metrics.

Endometriosis is a prevalent chronic inflammatory disease characterized by a considerable delay between initial symptoms and diagnosis through surgery. The pressing need for a timely, non-invasive diagnostic solution underscores the focus of current research efforts. This study examines the diagnostic potential of the menstrual blood lipidome. The lipid profile of 39 samples (23 women with endometriosis and 16 patient of control group) was acquired using reverse-phase high-performance liquid chromatography-mass spectrometry with LipidMatch processing and identification. Profiles were normalized based on total ion counts. Significant differences in lipids were determined using the Mann-Whitney test. Lipids for the diagnostic model, based on logistic regression, were selected using a combination of variance importance projection filters and Akaike information criteria. Levels of ceramides, sphingomyelins, cardiolipins, triacylglycerols, acyl- and alkenyl-phosphatidylethanolamines, and alkenyl-phosphatidylcholines increased, while acyl- and alkyl-phosphatidylcholines decreased in cases of endometriosis. PE P-16:0/18:1 and CL 16:0_18:0_22:5_22:6 serve as marker lipids in the diagnostic model, exhibiting a sensitivity of 81% and specificity of 85%. The diagnostic approach based on dried spots of menstrual blood holds promise as an alternative to traditional non-invasive methods for endometriosis screening.

PurposeThe purpose of this research was the development and evaluation of an intelligent assistive classification system for the recognition of endometriosis and the improvement of the accuracy of laparoscopic imaging in diagnosis. The research was developed with the use of Deep Learning approaches. Methods Data from 4448 laparoscopy images were used in a retrospective chart analysis. The data were divided into two folders, healthy and pathological including 2157 healthy and 2291 pathological images. Based on simple clinical and imaging information and criteria such as the diagnosis of endometriosis (included in an open-source dataset GLENDA of Kaggle repository), data mining algorithms were used to improve laparoscopic imaging accuracy. Results The final developed computer system based on the ResNet50 algorithm predicted the best outcome for all participants who had laparoscopic surgical therapy. The Keras tool was used and the generated code was implemented in Python programming language providing a mean accuracy of > 95 %. Conclusion The intelligent approach revealed better performance than the commonly used imaging criteria in predicting endometriosis improving the time and the total accuracy of diagnostic approaches.

Myxoid endometriosis is a rare entity that is part of the histological changes that can occur in endometriosis. It is extremely important that the pathologist has knowledge of the histological guidelines for the morphological recognition of this entity, as well as the histochemical and immunohistochemical techniques that will support the diagnosis. Define the morphological characteristics of myxoid endometriosis. In the present work we propose diagnostic approach guidelines, as well as the main differential diagnoses with the use of special histochemical techniques and immunohistochemical reactions to recognize this entity.

Background and Objectives: ARNE score was developed for the prediction of difficult airway for both general and ENT surgery with a universal cut-off value. We tested the accuracy of this score in the case of laryngeal surgery and provided an insight of its combination with flexible laryngoscopy. Materials and Methods: This prospective pilot clinical study included 100 patients who were being scheduled for microscopic laryngeal surgery. We have calculated ARNE score for every patient and flexible laryngoscopy was provided preoperative. Difficult intubation was assessed according to intubation difficulty score (IDS). Results: A total of 33% patients had difficult intubation according to IDS. ARNE score showed to be accurate for prediction of difficult intubation in laryngology with P&lt;0.0001 and AUC 0.784. Flexible laryngoscopy showed to be an independent parameter with P&lt;0.0001 and AUC 0.766. We have defined a new cut-off value of 15.50 for laryngology, according to AUC curve. After the patients were divided in two groups, according to the new cut-off value and provided cut-off value, AUC improved to 0.707 from 0.619 respectively. Flexible laryngoscopy improved the prediction model of ARNE score to AUC 0.882 and of new cut-off value to AUC 0.833. Conclusions: It is recommended to use flexible laryngoscopy together with ARNE score in difficult airway prediction in patients with laryngeal pathology. Also, universally recommended cut-off value of 11 can not be effectively used in laryngology and a new cut-off value of 15.50 is recommended.

Abstract Objective: This study aims to examine CA-125 and miRNAs as biomarkers in endometriosis pathophysiology and their potential roles in clinical diagnostics and prognostics. Methods:We reviewed a variety of studies, including cross-sectional, case-control, and prospective designs, examining a broad spectrum of patient demographics, clinical features, and biomarker (CA-125 and miRNA) expression levels. Results: CA-125 has been reaffirmed as a valuable biomarker in diagnosing and monitoring endometriosis, with its utility enhanced when used alongside other markers. MiRNAs have emerged as promising molecular regulators, offering new avenues for non-invasive diagnostics and a deeper understanding of the disease's pathophysiology. The integration of these biomarkers presents a potential shift towards more personalized and effective diagnostic and therapeutic strategies. Conclusion: Endometriosis presents a significant challenge in women's health, requiring innovative approaches for its management. The convergence of CA-125 and miRNA research represents a promising advance, potentially leading to more accurate diagnostics and personalized treatment. Future research should focus on standardizing methodologies, expanding study cohorts, and integrating findings into clinical practice to fully harness the potential of these biomarkers.

Evidence-based data for endometriosis management are limited. Experiments are excluded without adequate animal models. Data are limited to symptomatic women and occasional observations. Hormonal medical therapy cannot be blinded if recognised by the patient. Randomised controlled trials are not realistic for surgery since a variable disease with low numbers. Each diagnosis and treatment is an experiment with an outcome and experience is Bayesian updating from the past. If the experience of many is similar, this has more value than an opinion. The combined experience of a group of endometriosis surgeons was used to discuss problems in the management of endometriosis. Considering endometriosis as several genetically/epigenetically different diseases is important for medical therapy. Imaging cannot exclude endometriosis and diagnostic accuracy is limited for superficial lesions, deep lesions, and cystic corpora lutea. Surgery should not be avoided for emotional reasons. Shifting infertility treatment to IVF without considering fertility surgery is questionable. The concept of complete excision should be reconsidered. Surgeons should introduce quality control and teaching should move to explain why it is done. The perception of information has a personal bias. These are the major problems in the management of endometriosis identified by the combined experience of endometriosis surgeons.

Background: Effectiveness of Anastrozole and levonorgestrel-releasing intrauterine device (LNG-IUD, Mirena®) in the treatment of endometriosis. Methods: Randomized clinical trial. Elegibility criteria: Endometriomas >3×4 cm, CA-125>35 U/mL and symptoms suggestive of endometriosis. Thirty-one women were randomized to anastrozole+Mirena®+Conservative Surgery(CS) (n=8), anastrozole+Mirena®+transvaginal ultrasound-guided puncture-aspiration(TUGPA) (n=7), Mirena®+CS (n=9), or Mirena®+TUGPA (n=7). Interventions: Anastrozole 1 mg/day and/or only Mirena® for 6 months. CS or TUGPA one month after starting medical treatment. Results: A significant improvement in symptoms during the treatment (difference of 43%, 95% CI 29.9-56.2) occurred, which was maintained at 1 and 2 years. It was more significant in patients treated with anastrozole. For CA-125, the most significant decrease was observed without anastrozole. After CS for endometriosis, a reduction of findings of endometriomas and long-term recurrences occurred, with or without anastrozole, although anastrozole seems to delay recurrences. At 4,2±1,7 years, 88% of the patients who underwent CS were asymptomatic, compared to only 21% if TUGPA was performed, with or without anastrozole (p=0.019). Conclusion: Dosing anastrozole for 6 months, starting one month before CS of endometriosis, reduces more significantly the painful symptoms and delays recurrences, but has no other significant advantages over the single insertion of LNG-IUD (Mirena®) during the same time.

As of 26 March 2020, Pakistan had 1179 cases of COVID-19, with most 421 cases from Sindh, 394 cases, 131 cases, 123 cases, 84 cases, 25 cases and 01 cases from Punjab, Balochistan, Khyber Pakhtunkhwa, Gilgit-Baltistan, Islamabad Capital Territory, and Azad Jammu and Kashmir respectively. Travel-related cases were the main source of SARS-CoV-2 infection during the early phase of the pandemic in Pakistan. Nevertheless, cases of local virus transmission are increasing day by day. As of 26 March 2020, nine deaths have been reported from COVID-19. The case fatality rate is 0.8%, which is less compare to China, Italy, USA, and Iran. The SIR (Susceptible-Infected-Recovered) model of epidemiological analysis predicts that almost 90 million population will be infected in the coming days with 5% critical cases that need health care facilities. However, the Pakistan health care system cannot provide services to this much population. Hence, we need to act timely to reduce this number by restricting local transmission of the disease. This can be done by mass testing, quarantine, isolation and social distancing of the active coronavirus cases in Pakistan. Moreover, better communication between the authorities is very much required to control disease transmission.

Taiwan developing offshore wind power to promote green energy and self-electricity production. In this study, a Light Detection and Ranging (Lidar) was set up at Chang-Hua development zone one on the sea and 10km away from the seashore. At Lidar location, WRF (3.33km & 2km grid lengths) model and WAsP were used to simulate the wind speed at various elevations. Three days mean wind speed of simulated results were compared with Lidar data. From the four wind data sets, developed five different comparisons to find an error% and R-Squared values. Comparison between WAsP and Floating Lidar was shown good consistency. Lukang meteorological station 10 years wind observations at 5m height were used for wind farm energy predictions. The yearly variation of energy predictions of traditional and TGC wind farm layouts are compared under purely neutral and stable condition. The one-year cycle average surface heat flux over the Taiwan Strait is negative (-72.5 (W/m2) and 157.13 STD), which represents stable condition. At stable condition TGC (92.39%) and 600(92.44%), wind farms were shown higher efficiency. The Fuhai met mast wind data was used to estimate roughness length and power law exponent. The average roughness lengths are very small and unstable atmosphere.

The intense motion of a ship can greatly impacts the comfort of crew members and the safety of the vessel. Therefore, accurately estimating and predicting ship attitudes has become an important issue. This paper introduces the latest development in functional deep learning model called DeepOnet. It takes wave height as input and ship motion as output, using a cause-to-result prediction approach. The modeling data used in this study is sourced from publicly available experimental data from the Iowa Institute of Hydraulic Research. Firstly, parameters system tuning was conducted for the neural network's hyperparameters to determine the appropriate combination of parameters. Secondly, the DeepOnet model for wave height and multi-degree-of-freedom motion was established, and the influence of increasing time steps on prediction accuracy was examined. Finally, a comparison was made between the DeepOnet model and the classical time series model LSTM. It was found that the DeepOnet model had a 10-fold improvement in accuracy for roll and heave attitudes. Moreover, as the forecast duration increased, the advantage of DeepOnet showed a trend of strengthening. As a functional prediction model, DeepOnet provides a new promising tool for very short-term prediction of ship motion.

Background/Objectives Bowel endometriosis (BE) is defined as the presence of endometrial-like tissue in the muscular layer of the bowel wall. When BE does not cause severe obstruction to fecal transit, not leading to (sub)occlusive symptoms, the decision to operate becomes challenging as intestinal surgery is associated with higher complication rates and long-term bowel dysfunction. The aim of this cross-sectional study is to evaluate the quality of life (QoL) in patients with BE undergoing medical therapy or expectant management, investigating the impact of characteristics of BE nodules on QoL and intestinal function. Methods A retrospective cross-sectional analysis was conducted on 580 patients with BE who did not undergo surgery but were treated conservatively by medical therapy or expectant management between January 2017 and August 2022. The diagnosis of BE was done by transvaginal ultrasound and confirmed at double contrast barium enema. After at least one year of follow-up, QoL and intestinal function of patients were evaluated by the Endometriosis Health Profile-5 (EHP-5) questionnaire and Bowel Endometriosis Symptom score (BENS), whereas pain symptoms were assessed by the Visual Analogic Scale (VAS 0-10). Statistical analysis was performed to investigate possible associations between QoL and characteristics of bowel nodules (dimension, location, and evidence of stenosis) as well as type and length of eventual medical therapy. Results Patients with BE had a satisfying overall QoL with a mean EHP-5 score of 105.42 ± 99.98 points and VAS lower than 3 for all pain domains; additionally, they did not show a significant impartment of bowel function with mean BENS score of 4.89 ± 5.28 points. Notably, patients under medical therapy had a better QoL than patients without therapy (p

An observational cohort study of patients diagnosed with endometrial cancer (EC) stage IA G1, or atypical endometrial hyperplasia (AHE), undergoing organ-preserving treatment, was conducted. Objective of the study: to determine CDO1, PITX2, and CDH13 gene methylation levels in early endometrial cancer and atypical hyperplasia specimens obtained before organ-preserving treatment in the patients with adequate response and with insufficient response to hormonal treatment. Materials and methods: 41 endometrial specimens obtained during diagnostic uterine curettage in women with EC (n=28) and AEH (n=13), willing to preserve reproductive function, were studied; 18 specimens of endometrial cancer IA stage G1 from peri- and early postmenopausal women (comparison group) were included in the study. The control group included 18 endometrial specimens from healthy women obtained by diagnostic curettage for missed abortion and/or intrauterine adhesions. Methylation levels were analyzed using the modified MS-HRM method. Results: All 13 women with AEH had complete response (CR) to medical treatment. In the group undergoing organ-preserving treatment for uterine cancer IA stage G1 (n=28), 14 patients had complete response (EC CR group) and 14 had not (non-CR). It was found that all groups had statistically significant differences in CDO1 gene methylation levels with the control group (p&lt;0.001) except for the EC CR group (p=0.21). The p-value of difference between EC CR and EC non-CR groups was &lt;0.001. The differences in PITX2 gene methylation levels between the control and study groups were also significantly different (p&lt;0.001), except for AEH group (p=0.21). For the difference between EC CR and EC non-CR groups, the p-value was 0.43. For CDH13 gene methylation levels, statistically significant differences were found between the control and EC non-CR groups (p&lt;0.001), and the control and EC comparison groups (p=0.005). When comparing the EC CR group with EC non-CR group, the p-value for this gene was &lt;0.001. Simultaneous assessment of CDO1 and CDH13 genes methylation allowed to accurately distinguish between EC CR and EC non-CR groups (AUC=0.96). Conclusion: Assessment of CDO1 and CDH13 gene methylation in endometrial specimens from patients with endometrial cancer (IA stage G1), planned for medical treatment, can predict the treatment outcome.

Background: Endometriosis is a female chronic inflammatory disease in which endometrial tissue develops outside the uterine cavity. It is a complex pathology, that significantly contributes to morbidity in premenopausal women, leading to chronic pain, infertility, and subfertility negatively impacting the physical, and emotional well-being and overall quality of life. The public health burden of endometriosis remains elusive and challenging to determine, and this uncertainty can lead to inadequate healthcare services and treatments. Objective: To estimate the incidence and prevalence of endometriosis in Italy using the hospital discharge records database. Population-based retrospective study. Nationwide between 2011 and 2020. Methods: From the National Hospital Discharge Database were selected all admissions with a diagnosis of endometriosis (ICD-9-CM, codes 617.x), supported by the presence of a procedure code of laparoscopy or any other surgical procedure allowing direct visualisation of the lesions. Main outcomes measures Incidence and prevalence of endometriosis were estimated for the entire 2010-2020 period and by individual year, analysing the time trend and variability in different geographical areas of Italy. Results: A total of 134,667,646 women aged 15–50 years with one or more hospitalisations for endometriosis in all Italian hospitals. The incidence of endometriosis in Italy during this period was 0.839 per 1000 women (CI95% 0.834-0.844), exhibiting a statistically significant decreasing trend over the years. A discernible north-south gradient was observed, with higher rates documented in the northern regions. The prevalence rate stood at 14.0 per 1000 during the same period, and a similar north-south geographical gradient was identifiable in the prevalence rates as well. Conclusions: The utilization of national-level hospital data enables the generation of incidence and prevalence data for endometriosis without variations in methods and definitions, facilitating the evaluation of temporal trends and regional comparisons. Understanding and quantifying this phenomenon is essential for appropriate healthcare planning in various Italian regions.

(1) Background: To investigate the effect of phosphohistone H3 (PHH3) and vitamin D receptor (VDR) in the risk assessment of endometriosis, in two cohorts of women with this pathology, those receiving hormonal treatment, and those without treatment; (2) Methods: In 60 cases of women with endometriosis, the paraffin-embedded endometriosis tissue samples, retrieved after surgery, were immunohistochemically (IHC) analyzed aiming to determine the expression status of PHH3 and VDR; (3) Results: The IHC positive index of PHH3 and VDR of these two groups were compared. PHH3 levels were positively correlated with vitamin D levels, both being increased in patients without treatment. Within the group without treatment, PHH3 and VDR expressions were positively associated; (4) Conclusions: IHC analysis of PHH3 and VDR can be used as an additional tool for risk stratification and prognostic assessment in patients with endometriosis.

Given the importance of menstrual blood in the pathogenesis of endometriosis and the multifunctional roles of menstrual mesenchymal stem cells (MenSCs) in regenerative medicine, this issue has gained prominence in the scientific community. Moreover, recent reviews highlight how robust the integrated assessment of omics data is for endometriosis. To our knowledge, no study has applied the multi-omics approaches to endometriosis MenSCs. It is a case-control study at a university-affiliated hospital. MenSCs transcriptome and proteome data were obtained by RNA-seq and UHPLC-MS/MS detection. Among the differentially expressed proteins and genes, we emphasize ATF3, ID1, ID3, FOSB, SNAI1, NR4A1, EGR1, LAMC3, and ZFP36 genes and MT2A, TYMP, COL1A1, COL6A2, and NID2 proteins that were already reported in the endometriosis. Our functional enrichment analysis reveals integrated modulating signaling pathways such as epithelial-mesenchymal transition (↑) and PI3K signaling via AKT to mTORC1 (↓in proteome), mTORC1 signaling, TGF beta signaling, TNFA signaling via NFkB, and response to hypoxia via HIF1A targets (↑in transcriptome). Our findings highlight primary changes in the endometriosis MenSCs, suggesting that the chronic inflammatory endometrial microenvironment can modulate these cells, providing opportunities for endometriosis etiopathogenesis. Moreover, they identify challenges for future research leveraging knowledge for regenerative and precision medicine in endometriosis.

High-throughput screening technologies continues to produce large amounts of multiomics data from different populations and cell types for various diseases, such as cancer. However, analysis of such data encounters difficulties due to cancer heterogeneity, further exacerbated by human biological complexity and genomic variability. There is a need to redefine the drug discovery development pipeline, bringing an Artificial Intelligence (AI)-powered informational view that integrates relevant biological information and explores new ways to develop effective anticancer approaches. Here, we show SynPred, an interdisciplinary approach that leverages specifically designed ensembles of AI-algorithms, links omics and biophysical traits to predict synergistic anticancer drug synergy. SynPred exhibits state-of-the-art performance metrics: accuracy – 0.85, precision – 0.77, recall – 0.75, AUROC – 0.82, and F1-score - 0.76 in an independent test set. Moreover, data interpretability was achieved by deploying the most current and robust feature importance approaches. A simple web-based application available online at http://www.moreiralab.com/resources/synpred/ was constructed to predict synergistic anticancer drug combinations requiring only the upload of the two drug SMILES to be tested, allowing easy access by non-expert researchers.

Keywords: COVID-19, severity, influenza, serum antibodies, blood tests

Coronavirus disease (COVID-19) and its outcomes remain one of the most challenging problems today. COVID-19 in children could be asymptomatic but can result in a fatal outcome, therefore, prediction of the disease severity is important. The goal was to investigate the human genetic factors that could be associated with COVID-19 severity in children. Single nucleotide polymor-phisms of the following genes were studied: ACE2 (rs2074192), IFNAR2 (rs2236757), TYK2 (rs2304256), OAS1 (rs10774671), OAS3 (rs10735079), CD40 (rs4813003), FCGR2A (rs1801274) and CASP3 (rs113420705). In the case-control study were 30 children with mild or moderate course of the disease; 30 with severe COVID-19 symptoms and multisystem inflammatory syndrome in children (MIS -C) and 15 healthy who did not have COVID-19 infection (PCR negative, Ig G negative). The study revealed that ACE2 rs2074192 (allele T), IFNAR2 rs2236757 (allele A), OAS1 rs10774671 (allele A), CD40 rs4813003 (allele C), CASP3 rs113420705 (allele C) and male sex contribute to severe course COVID-19 infection, and MIS-C in 85.6% of cases. The World Health Organization reported new COVID-19 variants may cause previously unseen symptoms in chil-dren. Although the study has limitation due to cohort size, the findings can help provide a better understanding of SARS-CoV-2 infection and proactive pediatric patient management.

Obesity is a significant public health concern with higher morbidity. Obesity patients are at risk of severe COVID-19 infection and obesity is a higher risk factor for intensive Care Unit admission in COVID-19 infection. Obesity status affects lung volumes, cardiac structure and hemodynamics. Obesity is associated with a low inflammation state, endothelial dysfunction, hyperinsulinaemia and metabolic disorders. The authors review cardio-respiratory pathophysiological aspects involved in obesity and propose clinical management in obese patients infected by COVD-19.

Severe acute respiratory syndrome coronavirus 2 (SARS coronavirus 2 or SARS-CoV-2) is the cause of the respiratory infection known as COVID-19. From an immunopathological standpoint, coronaviruses such as SARS-CoV-2 induce an increase in a variety of T-helper 1 (Th1) and inflammatory cytokines and chemokines including interleukins IL-1, IL-6, CCL2 protein and CXCL10 protein. In the absence of proven antiviral agents or an effective vaccine, substances with immunomodulatory activity may be able to inhibit inflammatory and Th1 cytokines and/or yield an anti-inflammatory and/or Th2 immune response to counteract COVID-19 symptoms and severity. This report briefly describes four unconventional but commercially accessible immunomodulatory agents that could be employed in clinical trials to evaluate their effectiveness at alleviating disease symptoms and severity: Low-dose oral interferon-alpha, microdose DNA, low-dose thimerosal and phytocannabinoids.

Excretion of β2-microglobulin (β2M) above 300 µg/g creatinine, termed tubulopathy, was regarded as the critical effect of chronic exposure to the metal pollutant cadmium (Cd). However, current evidence suggests that Cd may induce nephron atrophy, resulting in a reduction in the estimated glomerular filtration rate (eGFR) below 60 mL/min/1.73 m2. Herein, these pathologies were investigated in relation to Cd exposure, smoking, diabetes and hypertension. Data were from 448 residents of Cd-polluted and non-polluted regions of Thailand. The body burden of Cd, indicated by the mean Cd excretion (ECd) normalized to creatinine clearance (Ccr) as (ECd/Ccr) ×100 in women and men did not differ (3.21 vs. 3.12 µg/L filtrate). The prevalence odds ratio (POR) for tubulopathy and a reduced eGFR were increased by 1.9-fold and 3.2-fold for every 10-fold rise in Cd body burden. In women only, a dose-effect relationship was seen between β2M excretion (Eβ2M/Ccr) and ECd/Ccr (F = 3.431, η2 0.021). In men, Eβ2M/Ccr was associated with diabetes (β = 0.279). In both genders, eGFR was inversely associated with Eβ2M/Ccr. The respective covariate- adjusted mean eGFR values were 16.5 and 12.3 mL/min/1.73 m2 lower in women and men who had severe tubulopathy; (Eβ2M/Ccr) ×100 ≥ 1000 µg/L filtrate. These findings indicate that women were particularly susceptible to the nephrotoxicity of Cd, and that the increment of Eβ2M/Ccr could be attributable mostly to Cd-induced impairment in tubular reabsorption of the protein together with Cd-induced nephron loss, evident from an inverse relationship between Eβ2M/Ccr and the eGFR.

The novel COVID-19 disease is a contagious acute respiratory infectious disease whose causative agent has been demonstrated to be a new virus of the coronavirus family, SARS- CoV-2. Multiple studies have already reported that risk factors for severe disease include older age and the presence of at least one of several underlying health conditions. However, a recent physiopathological report and the French COVID-19 scientific council have postulated a protective effect of tobacco smoking. Thanks to a meta-analysis, we have been able to demonstrate the statistical significance in this regard of twelve series from China, France and in the US, reporting three different smoking status (current smoker,former smoker, with a smoking history) as well as disease severity (with respectively odds-ratio of 1.78 [1.08-3.10], 4.60 [3.13-7.17], 2.74 [0.63-5.89]). Subsequently and using a Bayesian approach we have established that past, and present smoking is associated with more severe COVID-19 outcomes. Finally, we refute claims linking general population smoking status (N=O(10^8) or O(10^9)) to much smaller disease course series (N=O(10^4)). The latter point in particular is presented to stimulate academic discussion, and must be further investigated by well-designed studies.

Artificial intelligence (AI) is experiencing advances and integration in all medical specializations, and this creates excitement but also concerns. This narrative review aims at a critical assessment of the state of the art of AI in the field of endometriosis and adenomyosis. By enabling automation, AI may speed up some routine tasks, decreasing gynecologists’ risk of burnout, as well as enabling them to spend more time interacting with their patients, increasing their efficiency and patients’ perception of being taken care of. Surgery may also benefit from AI, especially through its integration with robotic surgery systems. This may improve the detection of anatomical structures and enhance surgical outcomes by combining intra-operative findings with pre-operative imaging. Not only, AI promises to improve the quality of care by facilitating clinical research and through the introduction of decision support tools that can enhance diagnostic assessment and predict treatment effectiveness and side effects, as well as reproductive prognosis and cancer risk. However, concerns exist regarding the fact that good quality data used in tool development and compliance with data sharing guidelines are crucial. Also, professionals are worried AI may render certain specialists obsolete. This said, AI is more likely to become a well-liked team member rather than a usurper.

In the pathogenesis of endometriosis, the differences between the eutopic and ectopic endometrium as well as between the eutopic endometrium with and without endometriosis are repeatedly pointed out. Various mechanisms have been suggested to explain these changes among them epithelial-mesenchymal transition (EMT). Recently, we suggested based on immunohistochemical data that most of the changes occur after and not before implantation of endometrial cells into ectopic locations. Furthermore, the subtle changes between eutopic endometrium with and without endometriosis and maintenance of epithelial cell-to cell contacts only suggest a partial EMT. In this study, we have re-analyzed the mRNA expression array data of eutopic and ectopic endometrium with respect to expression changes and EMT. Especially, we found that the similarity between eutopic endometrium with and without endometriosis is extremely high (~99.1%). In contrast, eutopic endometrium compared to ectopic endometrium only shows an overall similarity of ~95.3%. Analysis of some EMT-associated genes revealed small differences in the mRNA expression levels of some members of the claudin family. The array data suggest that the changes in eutopic endometrium at the beginning of the disease are quite subtle and that the majority of differences occur after implantation into ectopic locations.

Background: given the various clinical manifestations that characterize coronavirus disease 2019 (COVID-19), the scientific community is constantly searching for biomarkers with prognostic value. Surfactant protein A (SP-A) and D (SP-D) play a crucial role in ensuring proper alveolar function and an alteration of their serum levels have been reported in several pulmonary diseases characterized by acute respiratory distress syndrome (ARDS) and pulmonary fibrosis. Consid-ering that such clinical manifestations can also occur during severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, we wondered if these collectins could act as prognostic markers. Methods: in this regard, serum levels of SP-A and SP-D were measured by enzyme immunoassay in patients with SARS-CoV-2 infection (n=51) at admission (T0) and after 7 days (T1) and compared with healthy donors (n=11). Results: SP-D increased in COVID-19 patients com-pared to healthy controls during the early phases of infection, while a significant reduction was observed at T1. Stratifying SARS-CoV-2 patients according to disease severity, increased serum SP-D levels were observed in severe compared to mild patients. Conclusions: In the light of these results SP-D, but not SP-A, seems to be an eligible marker of COVID-19 pneumonia and the early detection of SP-D serum levels could be crucial for a preventive clinical management.

This research delves into the intricate challenges confronting the agricultural sector, with a specialized focus on mitigating infections in tomato crops, particularly powdery mildew induced by the Leveillula Taurica pathogen. Tomatoes, renowned for their nutritional richness, are vital to global food security. However, conventional methodologies for disease detection exhibit both laborious processes and limited accuracy. In response to these challenges, this study advocates for an innovative fusion of hyperspectral imaging and deep learning methodologies to detect crop disease and its severity. The systematic workflow commenced with the curation of a dataset, involving the acquisition of live images through OpenCV, followed by conversion to RGB format and subsequent feature extraction utilizing a pre-trained visual geometry group (VGG-16) model for enhanced analysis. Sequentially, RGB images were transformed into simulated hyperspectral images (SHSI) leveraging a Neural Network generator model, offering a distinctive viewpoint on spectral information. This novel approach transcends conventional constraints by delivering a three-dimensional perspective, seamlessly integrating spatial and spectral dimensions for holistic data acquisition. The SHSI is further transmuted into a 3D visualization cube comprehensive grasp of spatial and spectral aspects encompassing spectral, spatial, and Haralick features. The research concludes with severity detection, categorized as low, moderate, or high, employing a Gaussian Mixture Model (GMM) and K-means for visualization.

Currently available data are consistent with increased severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) replication at temperatures encountered in the upper airways (25–33°C when breathing room temperature air, 25°C) compared to those in the lower airways (37°C). One factor that may contribute to more rapid viral growth in the upper airways is the exponential increase in SARS-CoV-2 stability that occurs with reductions in temperature, as measured in vitro. Because SARS-CoV-2 frequently initiates infection in the upper airways before spreading through the body, increased upper airway viral growth early in the disease course may result in more rapid progression of disease and potentially contribute to more severe outcomes. Similarly, higher SARS-CoV-2 viral titer in the upper airways likely supports more efficient transmission. Conversely, the possible significance of air temperature to upper airway viral growth suggests that prolonged delivery of heated air might represent a preventative measure and prophylactic treatment for coronavirus disease 2019.

Coronavirus disease (COVID-19) is an infectious disease caused by a new virus which causes respiratory illness. Older adults and people who have previous chronic medical conditions are at higher risk for more serious complications from COVID-19.Hypovitaminosis D is attributed to the increased risk of lung injury and acute respiratory distress syndrome (ARDS) as well as diabetes, Cardiovascular event and associated comorbidities, which are the main causes of severe clinical problem in COVID-19 patients. Considering the protective role of vitamin D through modulating the innate and adaptive immune system as well as inhibition of Renin Angiotensin System (RAS), vitamin D supplementation might boost the immune system of COVID-19 patients and reduce severity of the disease in vitamin D deficient individuals.

The statistical analysis of impact of the top 50 solar flares of X-class (1997-2023) on the global seismic activity, as well as on the earthquake preparation zones located in illuminated part of the globe and in the area of 5000 km radius around the subsolar point was carried out. It is shown by a method of epoch superposition that for all cases the increase of seismicity is observed, especially in the region around the subsolar point (up to 38%) during 10 days after the solar flare in comparison with preceding 10 days. The case study of aftershock sequence of strong M=9.1 earthquake (Su-matra-Andaman Islands, 26.12.2004) after the solar flare of X7.2 class (20.01.2005) demonstrated that the number of aftershocks with magnitude M≥2.5 increases more than 20 times after the solar flare with a delay of 7 days. For the case of the Darfield earthquake (M=7.1, 04.09.2010, New Zealand) it was shown that strong solar flares of class X and M probably triggered two strong aftershocks (M>6) with the same delay of 6 days on the Port Hills fault, which is the most sensitive to external electromagnetic impact from point of view of the fault electrical conductivity and orientation. Based on the obtained results the possible application of natural electromagnetic triggering of earthquakes is discussed for a short-term earthquake prediction using confidently recorded strong external electromagnetic triggering impacts on the specific earthquake preparation zones, as well as ionospheric perturbations due to aerosol emission from the earthquake sources recorded by satellites.

The static formation temperature (SFT) is required to determine the thermophysical properties and production parameters in geothermal and oil reservoirs. However, the SFT is not easy to be obtained by both experimental and physical methods. In this paper, a mathematical approach to predicting SFT based on a new model describing the relationship between bottom hole temperature (BHT) and shut-in time was proposed. The unknown coefficients of the model were derived from least squares fit by Particle Swarm Optimization (PSO) algorithm. Besides, the ability to predict SFT based on a few BHT data (such as first 3, 4, or 5 ones of a data set) was evaluated. The accuracy of the proposed method to predict SFT was testified with a deviation percentage less than ±4% and high values of regression coefficient R² (>0.98). The proposed method could be used as a practical tool to predict SFT in both geothermal and oil wells.

ACE2 impact on the severity of COVID-19 is widely discussed but still controversial. To estimate its role in aspects of the main risk factors and comorbidities, we involved post-COVID-19 patients in Ternopil region (Ukraine). Recruitment period was July 2020 to December 2021. Medical records, treatment modalities and outcomes were recorded and analyzed. Serum human ACE2 protein was measured with Cusabio ELISA kits (Houston, TX, USA). Statistical analysis was performed with SPSS21.0 software (SPSS Inc., Chicago, IL, USA). The level of ACE2 serum protein was significantly higher (p < 0.001) in patients with mild symptoms compared to more severe course of disease, and inversely had changed from 1 to 90 days after recovery. In patients with mild COVID-19, ACE2 level significantly decreased over time, while among critical patients, it increased by 34.1percent. Such results could be explained by ACE2 shedding from tissues into circulation. Loss of the membrane-bound form of the enzyme decreases the virus entry into cells. Our studies did not identify any sex-related ACE2 serum levels correlation. The most common comorbidities were hypertension, cardiovascular diseases, respiratory diseases and diabetes mellitus. All comorbidities except respiratory diseases contribute to the severity of disease and correlate with ACE2 blood serum level.

The current COVID-19 pandemic is one of the most devastating events in recent history. The virus causes relatively minor damage to young, healthy populations, imposing life-threatening danger to the elderly and people with diseases of chronic inflammation. Therefore, if we could reduce the risk for vulnerable populations, it would make the COVID-19 pandemic more similar to other typical outbreaks. Children don’t suffer from COVID-19 as much as their grandparents and have a much higher melatonin level. Bats are nocturnal animals possessing high levels of melatonin, which may contribute to their high anti-viral resistance. Viruses induce an explosion of inflammatory cytokines and reactive oxygen species, and melatonin is the best natural antioxidant that is lost with age. The programmed cell death coronaviruses cause, which can result in significant lung damage, is also inhibited by melatonin. Coronavirus causes inflammation in the lungs which requires inflammasome activity. Melatonin blocks these inflammasomes. General immunity is impaired by anxiety and sleep deprivation. Melatonin improves sleep habits, reduces anxiety and stimulates immunity. Fibrosis may be the most dangerous complication after COVID-19. Melatonin is known to prevent fibrosis. Mechanical ventilation may be necessary but yet imposes risks due to oxidative stress, which can be reduced by melatonin. Thus, by using the safe over-the-counter drug melatonin, we may be immediately able to prevent the development of severe disease symptoms in coronavirus patients, reduce the severity of their symptoms, and/or reduce the immuno-pathology of coronavirus infection on patients’ health after the active phase of the infection is over.

Purpose: The objective of this paper is to explore the association between insufficient lycopene intake and risk of periodontitis among older adults. Methods: Data analysis was conducted using data from the National Health and Nutritional Examination Survey 2009-2014. Overall, 1227 adults aged 65- to 79-years-old were investigated. Results: Overall, 48.7% of older adults in this study were classified with different levels of periodontitis and 49.1% of individuals with a form of periodontitis had insufficient dietary lycopene intake. Sufficient lycopene intake was found to be associated with a lower likelihood of severe periodontitis (OR: .33; 95% CI: 0.17-.65; p=0.002). Non-Hispanic Black (NHB) older adults were more likely to develop severe forms of periodontal disease in comparison to Non-Hispanic White (NHW) older adults (OR: 2.82, 95% CI: 1.46-5.45, p=0.003). Gender was also found to play a role in periodontitis status, with women being less likely to have severe periodontitis status, compared to men (OR: 0.27; 95% CI: 0.14-0.55; p=0.0007,). However, only NHW females were less likely to have severe periodontitis compared to NHW males (OR: 0.26; 95% CI: 0.12-0.56; p= 0.001). Conclusion: Dietary intake of lycopene associated with periodontitis disease for individuals over the age of 65; those who consume a sufficient amount of lycopene are less likely to develop severe periodontitis. In general, more men suffered from periodontitis in comparison to women. Such association also has been found between NHB with severe periodontitis. In addition, reduced risk of moderate periodontitis is associated with sufficient lycopene intake is explored in NHW, future targeted interventions using lycopene in dietary intake as a preventative measure to delay or prevent the onset of periodontal disease needs to be race and gender specific.

Skin image analysis using artificial intelligence (AI) has recently attracted significant research interest, particularly for analyzing skin images captured by mobile devices. Acne is one of the most common skin conditions with profound effects in severe cases. In this study, we developed an AI system called AcneDet for automatic acne object detection and acne severity grading using facial images captured by smartphones. AcneDet includes two models for conducting two tasks: (1) a Faster R-CNN-based deep learning model for the detection of acne lesion objects of four types including blackheads/whiteheads, papules/pustules, nodules/cysts, and acne scars; and (2) a LightGBM machine learning model for grading acne severity using the Investigator’s Global Assessment (IGA) scale. The output of the Faster R-CNN model, i.e., the counts of each acne type, were used as input for the LightGBM model for acne severity grading. A dataset consisting of 1,572 labeled facial images captured by both iOS and Android smartphones was used for training. The results show that the Faster R-CNN model achieves a mAP of 0.54 for acne object detection. The mean accuracy of acne severity grading by the LightGBM model is 0.85. With this study, we hope to contribute to the development of artificial intelligent systems that are able to help acne patients understand more about their conditions and support doctors in acne diagnosis.

Endometriosis is a chronic neuro-inflammatory disorder the defining feature of which is the growth of tissue (lesions) that resembles the endometrium in sites outside the uterus. Estimates of prevalence typically quote rates of ~10% of women of reproductive age, equating to ~190 million women world-wide. Three subtypes of endometriosis are usually considered when discussing the aetiology of the disorder - superficial peritoneal, ovarian (endometrioma cysts), and deep (infiltrating). Genetic, hormonal and immunological factors have all been proposed as contributing to risk factors associated with the development of lesions. Twin studies report the heritable component of endometriosis as ~50%. Genome wide association studies (GWAS) have been conducted allowing unbiased scanning of the genome for single nucleotide polymorphisms (SNPs) in many thousands of individuals. These studies have identified SNPs that appear over-represented in patients with endometriosis, particularly those with more extensive disease (stage III/IV). Amongst the larger scale GWAS there has been replication of SNPs near genes involved in oestrogen and other signalling pathways including ESR1 (oestrogen receptor alpha), GREB1, HOXA10, WNT4 and MAPK kinase signalling. The results from patients with endometriosis have also provided an opportunity to make comparisons with GWAS conducted on other patient cohorts including those with reproductive traits (age at menarche) and disorders (fibroids, endometrial and ovarian cancer) and conditions that are reported by women with endometriosis (migraine, depression). These comparative studies have highlighted some shared genetically-controlled biological mechanisms, including hormone-regulated pathways which might explain the co-occurrence of endometriosis with these disorders. In summary, unbiased genetic analysis has provided new insights into the genetic factors that may contribute to increased risk of developing endometriosis. New studies are needed to broaden the range of patients contributing to these datasets and to improve integration with non-genomic and tissue expression data before their full potential for diagnosis and improvements in patient care can be fully realised.

Aiming at the development of the application of massive and heterogeneous LEO constellations, the influence of typical perturbation forces on the orbit prediction of LEO satellites at different altitudes is analyzed. Firstly, the main perturbation force model of LEO satellites is introduced. Then experiments for analyzing the influence of orbit prediction on LEO satellites at different altitudes are designed. Finally, the influence of typical perturbation forces on the orbit prediction under different prediction durations by numerical analysis is assessed systematically, and the orbit prediction of LEO satellites with different non-spherical gravitational field orders, ocean tide orders and atmospheric density models were compared and calculated. The research results show that different orders of non-spherical gravitational fields should be considered when the altitude of satellites is different, the reasonable gravitational field orders for LEO satellites at 500km, 1000km, 1500km and 2000km under 1-hour forecast time are 140x140, 80x80, 60x60 and 40x40 orders, the reasonable orders of ocean tide perturbation for 1500km and 2000km satellites are 20x20 orders. Under the 24-hour forecast time, atmospheric resistance has a great influence on the precision of LEO satellites at orbit altitude below 1,000km. The forecast deviation of the LEO satellites orbit at 500km reaches 8751.576m. The difference of orbit precision between different atmospheric density models is in the meter level. The orbit deviation by the Standard Atmosphere 1976 and NRLMSISE 2000 atmospheric models respectively reaches 25.494m. Relevant conclusions provide support for the optimization and selection of dynamical models in high precision orbit determination and fast orbit prediction of LEO satellites.

Studies report variable effects of sex hormones on serious diseases. Severe disease and mortality rates in COVID-19 show marked gender differences that may be related to sex hormones. Sex hormones regulate the expression of the viral receptors ACE2 and TMPRSS2, which affect the extent of viral infection and consequently cause variable outcomes. In addition, sex hormones have complex regulatory mechanisms that affect the immune response to viruses. These hormones also affect metabolism through obesity, and severe disease can result from complications such as thrombosis. This review presents the latest research on the regulatory functions of hormones in viral receptors, immune responses, complications as well as their role in COVID-19 progression. It also discusses the therapeutical possibilities of these hormones by reviewing the recent findings of clinical and assay studies.

Objective: To determine if higher weight-to-length z-scores after 32 weeks of gestation are associated with higher Pulmonary Scores (PS) in preterm infants requiring respiratory support using a prospective observational study. Methods: Infants born <30 weeks, with a post-menstrual age (PMA) of 30-33 weeks were enrolled. Weight, infant’s length, and head circumference were measured weekly. Data on calories/kg/d, protein g/kg/d, weight for length percentiles, z-scores, and BMI at 33 through 40 weeks PMA were collected. PS was calculated. Results: We analyzed 91 infants. The mean gestational age was 26.9 ± 1.7 weeks. There was a significant association between PS and W/L z scores(p<0.0001). For every one-unit increase in W/L z-scores, PS increased by 0.063. There was a significant association between PS and W/L percentiles (p = 0.0017) as well as BMI (p = <0.0001). For every unit increase in W/L percentiles, PS increased by 0.002, and for a unit increase in BMI, PS increased by 0.04. The association remained significant after postnatal steroids, sex, corrected, and birth gestational ages were included in the regression analysis. Nutrition did not affect anthropometric measurements. Conclusion: Our study is the first to demonstrate that higher BMI and W/L may adversely affect respiratory severity in infants.

The Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) first emerged in 2019 in China and has resulted in millions of human morbidities and mortalities across the globe. It has been shown that this novel virus originated in animals, mutated, and made the cross-species jump to humans. At the time of this communication, the Coronavirus disease (COVID-19) may be on way to an endemic form, however the threat of the virus is more for susceptible (older people and immunocompromised) people. The human body has more bacterial cells than its own, thus making us more bacterial than humans. As a consequence, the bacteriomes in the human body heavily influence human health and disease. The bacteriomes in the body and the immune system seem to be in constant association during bacterial and viral infections. In this review, we identify various bacterial spp. in major bacteriomes (oral, nasal, lung, and gut) of the body in healthy humans and compare them with dysbiotic bacteriomes of COVID-19 patients. We try to identify key bacterial spp. that have a positive effect on the functionality of the immune system and human health. These select bacterial spp. could be used as potential probiotics to counter or prevent COVID-19 infections. In addition, we try to identify key metabolites produced by probiotic bacterial spp. that could have potential anti-viral effects against SARS-CoV-2. These metabolites could be subject to future therapeutic trials to determine their anti-viral efficacy.

(1) Background: Nicotine is implicated in the SARS-COV-2 infection through activation of the α7-nAChR and over-expression of ACE2. Our objective is to clarify the role of nicotine in SARS-CoV-2 infection exploring its molecular and cellular activity. (2) Methods: HBEpC or si-mRNA-α7-HBEpC were treated for 1, 48 h or continuously with 10-7 M Nicotine, a concentration mimicking human exposure to a cigarette. Cell viability and proliferation were evaluated by trypan blue dye exclusion and cell counting, migration by cell migration assay, senescence by SA-beta-Gal activity, and anchorage-independent growth by cloning in soft agar. Expression of Ki67, p53/phospho-p53, VEGF, EGFR/pEGFR, phospho-p38, intracellular Ca+2, ATP and EMT were evaluated by ELISA and/or western blotting. (3) Results: Nicotine induced through α7-nAChR (i) increase in cell viability (ii) cell proliferation; (iii) Ki67 over-expression, (iv) phospho-p38 up-regulation (v) EGFR/pEGFR over-expression; (vi) increase in basal Ca+2 concentration, (vii) reduction of ATP production; (viii) decreased level of p53/phospho-p53, (ix) delayed senescence, (x) VEGF increase, (xi) EMT and consequent (xii) enhanced migration, and (xiii) ability to grow independently of the substrate. (4) Conclusions: Based on our results and on evidence showing that Nicotine potentiates viral infection it is likely that Nicotine is involved in SARS-CoV-2 infection and severity

This study empirically investigates the complex interplay between the severity of the coronavirus pandemic, mobility changes in retail and recreation, transit stations, workplaces, and residential areas, and lockdown measures in 88 countries of the word. To conduct the study, data on mobility patterns, socioeconomic and demographic characteristics of people, lockdown measures, and coronavirus pandemic were collected from multiple sources (e.g., Google, UNDP, UN, BBC, Oxford University, Worldometer). A Structural Equation Modeling (SEM) technique is used to investigate the direct and indirect effects of independent variables on dependent variables considering the intervening effects of mediators. Results show that lockdown measures have significant effects to encourage people to maintain social distancing. However, pandemic severity and socioeconomic and institutional factors have limited effects to sustain social distancing practice. The results also explain that socioeconomic and institutional factors of urbanity and modernity have significant effects on pandemic severity. Countries with a higher number of elderly people, employment in the service sector, and higher globalization trend are the worst victims of the coronavirus pandemic (e.g., USA, UK, Italy, and Spain). Social distancing measures are reasonably effective at tempering the severity of the pandemic.

All transmission disease depends on the transmission opportunity or medium like humans in COVID-19. Due to globalization and regular movement of people from one country to another, spread of COVID 19 reached to 208 countries till May 10, 2020. For any society health is major concern for humanity as well as administration. Any pandemic is declared as and when it reached at a particular severity level and control vice versa. So, we have continued the daily COVID 19 cases analysis and segregated till May 10, 2020. We have included at least 25 countries for the analysis purpose due to limitation of number of observations in the analysis. Maximum number of day’s data available for China is for 100 days, followed by Iran for 81 days, minimum number of days data is for 16 days for Western Sahara and Tajikistan.

Hepatitis C presents a significant global health challenge, necessitating early diagnosis and precise severity classification for timely medical intervention. This study explores the application of machine learning techniques to predict the severity of hepatitis C, leveraging an extensive dataset. Our approach encompasses rigorous data preprocessing, advanced model development, and fine-tuned hyperparameter optimization to ensure accurate and reliable predictions. We evaluated four classification models: Logistic Regression, Random Forest, Gradient Boosting, and Support Vector Machine (SVM), comparing their accuracy in classifying patients. The results showed that the Random Forest and Gradient Boosting models outperformed the others with an accuracy of approximately 93.50%, demonstrating their potential in assisting Hepatitis C diagnosis. Further model enhancements through hyperparameter tuning and feature engineering can improve the precision of Hepatitis C diagnosis, contributing to better patient care.

Background: The level of food processing has gained interest for a potential determinant of human health. The aim of this study was to assess the relation between level of food processing and prostate cancer severity. Methods: A sample of 120 consecutive patients were examined for their dietary habits assessed through validated food frequency questionnaires, their dietary intake of food groups categorized according to the NOVA classification, and the severity of prostate cancer according to the European Association of Urology (EAU) guidelines groups risk. Uni- and multivariate logistic regression analyses were performed to test the association between the variables of interest. Results: Individuals reporting higher consumption of unprocessed/minimally processed foods were less likely to have worse prostate cancer severity than lower consumers in the energy-adjusted model [odds ratio (OR) = 0.38, 95% confidence interval (CI): 1.17-0.84, P = 0.017 and OR = 0.33, 95% CI: 0.12-0.91, P = 0.032 for medium/high vs. low grade and high vs. medium/low grade prostate cancers, respectively); however, after adjusted for potential confounding factors, the association was no more significant (Table 4). A borderline association was also found between higher consumption of UPF and worse prostate cancer severity in the energy-adjusted model (OR = 2.11, 95% CI: 0.998-4.44; P = 0.051), but again the association was no more significant after adjusting for the other covariates. Conclusions: The level of food processing seems not to be independently associated with prostate cancer severity, while potentially related to other factors that need further investigation.

Microcephaly has been regarded the most remarkable consequence of the Zika virus (ZIKV) epidemic in Brazil 2015. It remains to be determined whether there are factors that contribute to the degree of brain lesion associated with ZIKV infection during pregnancy. Previous studies showed that socioeconomic conditions correlate with ZIKV-associated microcephaly. Certain nutritional deficits display the potential to interfere in the mechanistic target of rapamycin (mTOR) signaling, which plays a major role in the pathophysiology of ZIKV-associated microcephaly. We hypothesize that a nutritional or environmental co-factor that interferes in mTOR signaling correlates with ZIKV-associated birth defects. To assess this hypothesis, we plan to: 1) develop a mouse model of ZIKV-associated microcephaly through intravenous injection of ZIKV and rapamycin for a straightforward interference on mTOR receptor; 2) determine in the experimental model and in cases of ZIKV-associated microcephaly the epigenetic signature (DNA methylation pattern) in neurons and muscle cells harvested by biopsy, and in hematopoietic and mesenchymal stem cells sorted from blood; 3) analyze through mass spectrometry in serum of pregnant female mice submitted to ZIKV and rapamycin injection and in serum of mothers of children with ZIKV-associated microcephaly the metabolomic pattern of cholesterol (a nutritional status marker), vitamin A and its metabolite retinoic acid, folate, and other metabolites related to these three nutritional factors; 4) check whether pregnant female mice submitted to intravenous injection of ZIKV and feed with a deficient diet of the most likely co-factor found in this study give birth to microcephalic mice with features that mimic clinical cases. In summary, our general objective is to develop an experimental model that mimics ZIKV-associated microcephaly cases and to find a co-factor involved in the microcephaly outbreak in Brazil 2015.

The recent outbreak and spread of the COVID-19 virus infection across the world has seen a massive global system-wide shutdown of human social and economic activity. Both developed and developing nations have been forced to contain and isolate their citizens as much as possible. However, the continuous rising cases in both categories of nations, especially those with poor or nonexistent testing facilities and healthcare systems pose a hidden danger. The seemingly lack of and access to a truly global concerted research effort in both temporary but effective symptom mitigation may lead to more deaths in infected cases. We propose that a fusion of both technological and home-grown solutions can be utilized effectively to manage symptoms. This would add to the preventive methods of social distancing, isolation, quarantine and frequent handwashing to halt the impact of the disease. We also hope to spur further research in such drug/non-drug combination therapy-based methods with emphasis on effectiveness based on quality of nutrient sources.

Background: According to the National Highway Traffic Safety Administration, 116 pedestrians were killed in motor vehicle crashes in Ohio in 2015. However, no study to date has analyzed crashes in Ohio exploring the factors contributing to the pedestrian injury severity resulting from motor vehicle crashes. This study fills this gap by investigating the crashes involving pedestrians exclusively in Ohio. Materials and Methods: This study uses the crash data from the Highway Safety Information System, from 2009 to 2013. The explanatory factors include the pedestrian, driver, vehicle, crash, and roadway characteristics. Both fixed- and random-parameters ordered probit models of injury severity (where possible outcomes are major, minor, and possible/no injury) were estimated. Results: The model results indicate that being older pedestrian (65 and over), younger driver (less than 24), driving under influence (DUI), being struck by truck, dark-unlighted roadways, six-lane roadways, and speed limit of 40 mph and 50 mph were associated with more severe injuries to the pedestrians. Conversely, older driver (65 and over), passenger car, crash occurring in urban locations, daytime traffic off-peak (10 AM to 3:59 PM), weekdays, and daylight condition were associated with less severe injuries. Conclusion: This study provides specific safety recommendations so that effective countermeasures could be developed and implemented by the policy makers, which in turn will improve overall highway safety.

We present the full case pictorial assay including preoperative imaging as well as the surgical correction of the uterine anomaly and associated complication of a very rare case of occult unicornuate right uterus with rudimentary non-communicating functioning left horn and its natural evolution following an initially incomplete surgical treatment.

Dreams and positive symptoms of schizophrenia are similar in many aspects, such as bizarre-ness, impairment of reality testing, intense emotional participation. Hallucinations, delusions, and disorganized thoughts have been proposed to be an intrusion of the dreaming state in wake-fulness: conversely, psychotic patients’ abnormal cognitive and behavioral features could over-flow into sleep, transferring peculiar features to their dreams which would differ, in quantity and quality, from those of healthy people. Here we assess this latter hypothesis by analyzing the dreams of 46 patients, hosted in a residential facility or in a day-treatment center, affected by disorders of the schizophrenic spectrum with psychotic symptoms, versus those of 28 healthy controls, in terms of several quantitative and qualitative characteristics. In patients, we also in-vestigated whether dream variables correlated with the severity of symptoms assessed via the Brief Psychiatric Rating Scale. Overall, patients reported fewer and shorter dreams than controls, with a reduction of many ele-ments like characters and settings. The greatest differences regarded emotions, which were less frequent in patients and inversely correlated with symptoms’ severity. This evidence supports the hypothesis that dreams are crucial for emotion regulation during wakefulness and that this func-tion could be totally or partially ineffective in psychoses.

Due to the intensification of climate change in the world, the incidence of natural disasters is increasing year by year, and monitoring, forecasting, and detecting evolution using satellite imaging technology is an important guide for remote sensing. This study aims to monitor the occurrence of fire disasters using Sentinel-2 satellite imaging technology, to determine the burned severity area with its classification and the recovery process for determining the extraordinary natural phenomena. The study area was sampled in the southeastern part of Mongolia, where have most wildfires in each year, near the Shiliin Bogd mountain in the natural steppe zone and in Bayan-Uul soum in the forest-steppe natural zone. For the methods, the NBR was used to map the area of ​​the fire site and the classification of the burned area into 5 categories: unburned, low, moderate-low, moderate-high, and high, which are process-defined works. NDVI index was used to determine the recovery process in a timely series in the summer from April to October. In result, the burned areas were mapped from the satellite images, and the total burned area of steppe natural zone was 1164.27 km2, of which 757.34 km2 (65.00 percent) was low, 404.57 km2 (34.70 percent) was moderate-low, and remaining 2.36 km2 (0.30 percent) was moderate-high, and the total burned area of forest-steppe natural zone was 588,35 km2, of which 158.75 km2 (26.90 percent) was low, 297.75 km2 (50.61 percent) was moderate-low, 131.25 km2 (22.31 percent) was moderate-high and the remaining 0.60 km2 (0.10 percent) was high-medium. Finally, we believe that this research is most important to helpful for emergency workers, researchers, and environmental specialists.

We aimed to evaluate the magnesium content in human cirrhotic liver and its correlation with serum AST levels, expression of hepatocellular injury, and MELDNa prognostic score. In liver biopsies obtained at liver transplantation we measured the magnesium content in liver tissue in 27 cirrhotic patients (CIRs) and 16 deceased donors with healthy liver (CTRLs) by atomic absorption spectrometry and within hepatocytes in 15 CIRs using synchrotron-based X-ray Fluorescence Microscopy. In 31 CIRs and 10 CTRLs we evaluated the immunohistochemical expression in hepatocytes of the transient receptor potential melastatin 7 (TRPM7), a magnesium influx chanzyme also involved in inflammation. CIRs showed a lower hepatic magnesium content [117.2 (IQR 110.5-132.9) vs 162.8 (IQR 155.9-169.8) μg/g; p&lt;0.001] and a higher percentage of TRPM7 positive hepatocytes [53.0 (IQR 36.8-62.0) vs 20.7 (10.7-32.8) %; p&lt;0.001) than CTRLs. In CIRs, MELDNa and serum AST at transplant correlated: a) inversely with the magnesium content both in liver tissue and hepatocytes; b) directly with the percentage of hepatocytes stained intensely for TRPM7. The latter also directly correlated with worsening of MELDNa at transplant compared to waitlisting. Magnesium depletion and overexpression of its influx chanzyme TRPM7 in hepatocytes are associated with severity of hepatocyte injury and prognosis in cirrhosis. These data represent the pathophysiological basis for a possible beneficial effect of magnesium supplementation in cirrhotic patients.

Through the use of machine learning algorithms like the Support Vector Machine, it has been show that burn extent can be accurately mapped from hyperspatial drone imagery in both grasslands and forests. Despite these successes, hyperspatial imagery must be acquired via drones, requiring large amounts of time and resources to capture areas much smaller than the large catastrophic fires which result in the majority of the lands burned each year by wildland fires. To overcome this difficulty, high spatial resolution satellite imagery from Worldview2 can be substituted for hyperspatial drone imagery, allowing for larger regions of images to be acquired more easily and efficiently. Additionally, Worldview2 trades spatial resolution for spectral resolution and extent, capturing images in 8 multispectral bands as opposed to 3 band imagery in the visible spectra. This research examines the utility of each of the 8 bands observed in Worldview2 imagery using an Iterative Dichotomiser 3 decision tree, then uses these bands to map burn extent and biomass consumption. Several classifications of burn extent and biomass consumption are produced and compared based on the bands used as inputs. The results show that using Worldview2 imagery to map burn extent and biomass consumption results in highly accurate maps, with slight improvements when additional bands are added.

Results: A total of 6270 individuals were assessed (1615 severe and 4655 non-severe patients). The median age was 63 (95% CI: 49-74) and 47 (95% CI: 19-63) years in the severe and non-severe groups, respectively. Moreover, about 41% of patients had comorbidities. Severity was higher in patients with history of cerebrovascular disease: OR 4.85 (95% CI: 3.11-7.57). The odds of being in severe group increase by 4.81(95% CI: 3.43-6.74) for history of cardiovascular disease (CVD). This was 4.19 (95% CI: 2.84-6.19) for chronic lung disease and 3.18, 95% CI: 2.09-4.82 for cancer .The odds ratio of a diabetes and hypertension were 2.61 (95% CI: 2.02-3.3), and 2.37( 95% CI: 1.80-3.13) respectively.

Background: Traumatic events adversely affect the clinical course of obsessive-compulsive disorder (OCD). Our investigation explores the correlation between prolonged interpersonal trauma and the severity of symptoms related to obsessive-compulsive and anxiety disorders. Methods: The study follows a cross-sectional and observational design, employing the International Trauma Questionnaire (ITQ) to examine areas linked to interpersonal trauma, the Hamilton Anxiety Rating Scale (HAM-A), and the Yale-Brown Obsessive Compulsive Scale (Y-BOCS) to assess anxious and obsessive-compulsive symptoms, respectively. Descriptive analysis, analysis of variance (ANOVA), and logistic regression analyses were conducted. Results: We recruited 107 OCD-diagnosed patients, categorizing them into subgroups based on the presence or absence of complex post-traumatic stress disorder (cPTSD). The ANOVA revealed statistically significant differences between the two groups in the onset age of OCD (p=0.083), psychiatric familial history (p=0.023), HAM-A, and Y-BOCS (p=0.000). Logistic regression indicated a statistically significant association between the presence of cPTSD and Y-BOCS scores (p=0.000). Conclusion: The coexistence of cPTSD in OCD exacerbates obsessive-compulsive symptoms and increases the burden of anxiety. Further advancements in this field are crucial for mitigating the impact of early trauma on the trajectory of OCD and associated anxious symptoms.

Previous studies found high but very variable levels of tetranor-PGEM and PGD-M (urine metabolites of prostaglandin (PG) E2 and PGD2, respectively) in persons with cystic fibrosis (pwCF), which correlated with the severity of the CF transmembrane receptor (CFTR) genetic alteration. This study aims to assess the potential role of cyclooxygenase (COX)-1 and COX-2 genetic polymorphisms in the variability of PG production, and determine the potential role of PG metabolites as markers of symptoms severity and imaging findings. Thirty healthy subjects and 103 pwCF were included in the study. Clinical and radiological CF severity was evaluated using clinical scoring methods and the extent of bronchiectasis and air trapping were assessed by chest computed tomography (CT). Urine metabolites were measured using liquid chromatography/tandem mass spectrometry. The presence of variants in the COX-1 gen (PTGS1 639 C>A, PTGS1 762+14delA, and COX-2 gen: PTGS2-899G>C (-765G>C) and PTGS2 (8473T>C) were analysed. PGE-M and PGD-M urine concentrations were significantly higher in pwCF than in healthy controls. There were also statistically significant differences between clinically mild and moderate disease and severe disease. Patients with bronchiectasis and/or air trapping had higher PGE-M levels than patients without these complications. The four polymorphisms did not associate with clinical severity, air trapping, bronchiectasis, or urinary PG levels. Taken together, the present study suggests that urinary PG level testing can be used as a biomarker of CF severity. We found no relationship between COX-1 and COX-2 genetic polymorphisms in the variability of PG production.

We aimed to explore symptoms severity and adherence to therapy for patients with myelofibrosis treated with ruxolitinib in Bulgaria. It is a prospective, non-interventional study performed at the Specialized hospital for active treatment of hematological diseases in Sofia during 2022 - 2023. Date of diagnosis, demographic characteristics, clinical indicators, ruxolitinib dose, and other data points were collected. Clinical indicators were assessed at baseline, in the middle and at the end of observation. Severity of symptoms was measured with MPN-SAF TSS and adherence to therapy with the Morisky 4 questionnaire 6 times during the observation. The mean age of diagnosis was 58.5 years, with the average duration of disease of 3 years. Pa-tients’ laboratory results were within physiological ranges, with spleen size experiencing a con-stant decrease. The average value for the severity of the symptoms per MPN-SAF TSS results decreased significantly, indicating better disease control. The average adherence to therapy did not change and remained high at around 9 points, except for one patient. In conclusion the treatment of myelofibrosis patients with ruxolitinib decreased symptoms se-verity and spleen size. Patients were adherent to the therapy over the observed period but as treatment duration increases the risk of adherence decreasing.

Fire regimes in mountain landscapes of southern Europe have been shifting from their baselines due to rural abandonment and fire exclusion policies. Understanding the effects of fire on biodiversity is paramount to implement adequate management. Herein, we evaluated the relative role of burn severity and heterogeneity on bird abundance in an abandoned mountain range located in the biogeographic transition between the Eurosiberian and Mediterranean region (the Natural Park ‘Baixa Limia–Serra do Xurés’). We surveyed the bird community in 206 census plots distributed across the Natural Park, both inside and outside areas affected by wildfires over the last 11 years (from 2010 to 2020). We used satellite images of Sentinel 2 and Landsat missions to quantify the burn severity and heterogeneity of each fire within each surveyed plot. We also accounted for the past land use (forestry or agropastoral use) by using a land cover information for year 2010 derived from satellite image classification. We recorded 1,735 contacts from 28 bird species. Our models, fitted by using GLMs with Poisson error distribution (pseudo-R²_-average of 0.22 ± 0.13), showed that up to 71% of the modelled species were linearly correlated with at least one attribute of the fire regime. The spatiotemporal variation in burnt area and severity were relevant factors for explaining the local abundance of our target species (39% of the species; Akaike weights > 0.75). We also found a quadratic effect of at least one fire regime attribute on bird abundance for 60% of the modeled species. The past land use, and its legacy after 10 years, was critical to understand the role of fire (Akaike weights > 0.75). Our findings confirm the importance of incorporating remotely sensed indicators of burn severity into the toolkit of decision makers to accurately anticipate the response of birds to fire management.

(1) Background: Recent studies provide some evidence for the contribution of antibody-mediated autoimmune mechanisms to the nature of fibromyalgia (FM) and myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). Much attention was paid to the autoantibodies (AAb) targeting G protein-coupled receptors as natural components of the immune system. However, natural AAb network is much more extensive, and has not been previously investigated in these disorders; (2) Methods: The enzyme immunoassays ELI-Viscero-Test and ELI-Neuro-Test were used to determine changes in serum content of a 33 natural AAb to neural, organ-specific and non-tissue-specific autoantigens a) in 11 FM patients with comorbid ME/CFS; b) in 11 ME/CFS patients without FM; c) in 11 healthy controls. Individual autoantibody profiles and their correlation with some clinical symptoms were analyzed. (3) Results: both patients with ME/CFS and ME/CFS+FM were characterized by more frequent and pronounced deviations in the immunoreactivity to GABA-receptors than healthy controls. Although the level of other natural AAb did not differ between study groups, AAb correlation signatures were changing in patients compared to healthy controls. Both in patients and healthy controls the level of natural AAb to various neural and tissue-specific antigens correlated with the severity of fatigue, bodily pain, depression, anxiety, physical and mental-health related quality of life. Notably, that widely different correlation patterns were observed between study groups. (4) Conclusions: Findings from this pilot study provide some evidence that the homeostasis of autoimmune relationships, which are possibly a physiological part of our immune system, may break down in FM and ME/CFS. The correlation of disease-induced perturbations in individual AAb profiles with some clinical symptoms may arise from the immune system's ability to reflect qualitative and quantitative changes in antigenic composition of the body.

Pneumonia is the leading cause of hospitalization in pediatric patients. Disease severity greatly influences pneumonia progression and adverse health outcomes such as hospital readmission. Hospital readmissions have become a measure of healthcare quality to reduce excess expenditures. The aim of this study was to examine 30-day all-cause readmission rates and evaluate the association between pneumonia severity and readmission among pediatric pneumonia hospitalizations. Using 2018 Nationwide Readmissions Database (NRD), we conducted a cross-sectional study of pediatric hospitalizations for pneumonia. Pneumonia severity was defined by the presence of respiratory failure, sepsis, mechanical ventilation, dependence on long-term supplemental oxygen, and/or respiratory intubation. Outcomes of interest were 30-day all-cause readmission, length of stay, and cost. The rate of 30-day readmission for the total sample was 5.9%, 4.7% for non-severe pneumonia, and 8.7% for severe pneumonia (p&lt;0.01). Among those who were readmitted, hospitalizations for severe pneumonia had a longer length of stay (6.5 vs. 5.4 days, p&lt;0.01) and higher daily cost ($3,246 vs. $2,679, p&lt;0.01) than admissions for non-severe pneumonia. Factors associated with 30-day readmission were pneumonia severity, immunosuppressive conditions, length of stay, and hospital case volume. To reduce potentially preventable readmissions, clinical interventions to improve the disease course and hospital system interventions are necessary.

In the war against the COVID-19 pandemic, the world is experiencing severe resource constraints. Although transmission routes are well understood, we suspect that they cause different disease consequences. We evaluate them in different forms to understand how they affect infection rates and disease severity. In determining how they affect disease outcome, we evaluated target tissue vulnerability, functional role, defense mechanisms, viral concentration, infection vicinity to target vital tissue, and host factors. We found that direct lung infection is the most lethal transmission route followed by bronchi infection. Transmissions by physical contacts, foods, and blood by low viral concentration (as expected in normal human activities) pose lower or much lower risks unless the infection is followed by subsequent lung exposures. After adding transmission route, treatment timings, and improper treatments into the list of known risk factors, we found that death rate and disability rate for young or healthy persons are nearly zero. We show that population based medical model improperly shifts nominal death rate from few vulnerable people to the population resulting in unnecessary population panic, and such panic is responsible for shutting down human activities and the world economy. Finally, we examined limitations in population-based mitigating measures and proposed for governmental and private adoption community guidelines, which are mainly to enable vulnerable people avoid exposures, prevent non-vulnerable people from serving as viral transmitters, get rid of high-risk exposure modes in working environment, improve safety for people in buses, ships and planes, and reduce death and disability rates for infected people.

This overview study set out to compare and synthesise the findings of review studies conducted on predicting student academic performance (SAP) in higher education using educational data mining (EDM) methods, EDM algorithms and EDM tools from 2013 to June 2020. It conducted multiple searches for suitable and relevant peer-reviewed articles on two online search engines, on nine online databases, and on two online academic social networks. It, then, selected 26 eligible articles from 2,050 articles. Some of the findings of this overview study are worth mentioning. First, only 2 studies explicitly stated their precise sample sizes with maths and science as the two most mentioned subject areas. Second, 16 review studies had purposes related to either EDM techniques, EDM methods, EDM models, or EDM algorithms employed to predict SAP and student success in the higher education sector. Third, there are six commonly used typologies of input variables reported by 26 review studies, of which student demographics was the most commonly utilised variable for predicting SAP. Fourth and last, seven common EDM algorithms employed for predicting SAP were identified, of which Decision Tree emerged both as the most used algorithm and as the algorithm with the highest prediction accuracy rate for predicting SAP.

Our report presents the findings from a treatment program that involved plant-based diets (PBDs) and supplements for 1,750 elderly cardiology patients diagnosed with COVID-19 between April 2020 and June 2023. At the start of the program, there was no published data supporting the use of PBDs for COVID-19 patients. However, after 18 months, studies were released that indicated the effectiveness of PBDs in decreasing the incidence and severity of COVID-19. Our treatment protocol differed from these studies in several ways. We carefully controlled the quality, quantity, and processing method of the foods we provided, opting for raw instead of cooked vegetables. Additionally, we incorporated supplementation that complemented the nutrients lacking in a PBD and enhanced the anti-viral, anti-inflammatory, antioxidant, antithrombotic, and immunomodulatory properties of a PBD. Our patients recovered faster (12±1.4 days vs. 20.9±4.3 days), with lower severity (2% vs. 10-20%), hospitalizations (0 vs. 5-10%), and deaths (0 vs. 15-17%) compared to the general population. These favorable outcomes are particularly noteworthy given Indonesia's high COVID-19 morbidity and mortality rates, among the highest in Asia. The findings of this report provide valuable insights for practitioners in managing high-risk elderly COVID-19 patients, particularly in avoiding severity and mortality.

Introduction: The objectives of this study were to (1) investigate the association between human blood type and COVID-19 infection in both inpatient and longitudinal populations at our institution in South Florida and (2) identify the association between blood type and severity of COVID-19 infection via presence of cellular biomarkers of severe infection in hospitalized individuals. Methods: This study consists of (1) a single-center retrospective analysis of 669 of 2,741 COVID-19 positive screened patients seen from January 1, 2020 until March 31, 2021 at the University of Miami Emergency Department (ED) who tested positive for COVID-19 infection and had a documented ABO blood type and (2) a longitudinal SARS-CoV-2 immunity study (“CITY”) at the University of Miami Miller School of Medicine consisting of 185 survey participants. COVID-19 outcome rates and 95% confidence intervals were calculated for each ABO blood type and Rh group using Chi-squared tests for categorical and ANOVA for continuous variables in the inpatient cohort and Chi-squared tests for the longitudinal cohort, all executed in R studio. Results: Our inpatient cohort had a mean age of 53.6 (± 17.9) years with 48% males and 52% females. When compared to patients with other blood types, those with O- demonstrated less risk of developing COVID-19 pneumonia (26.7% for O- vs. 69.2% for type A-, 56.5% for A+, 71.4% for AB+, 53.7% for B+, 51.5% for O+, p-value= 0.003 via Chi-squared test) and decreased mortality due to COVID-19 infection (17.0% mortality for O vs. 26.3% for A, 29.2% for B, p-value= 0.012 via Chi-squared test). Blood type O- demonstrated less elevated levels of LDH than did other blood types (p-value= 0.001, ANOVA) and a higher frequency of clinically accepted baseline levels of troponin (0.04 ng/mL) (p-value= 0.026, ANOVA) (Figure 1). Most of the longitudinal cohort was ABO blood grouping type O (47% (87/185)), with 33.5% type A, 14.1% B, and 5.4% AB. Most individuals were Rh positive (85.9%). There was no significant association found between COVID status (infection vs. non-infection) and ABO (Chi-squared test (3, N = 185) = 1.8, p = 0.6) or Rh (Chi-squared test (1, N = 185) = 0.16, p = 0.7) blood typing. Nearly all participants had been vaccinated (95.1% (176/185)), with a majority reporting primary vaccination with Pfizer (57.8% (107/185)), followed by Moderna (33% (61/185)), and Johnson & Johnson (4.3% (8/185)). 8 participants (4.3%) were unvaccinated. Conclusions: In an inpatient setting, blood type O sustained less risk of COVID-19 mortality and O- demonstrated less risk of developing COVID-19 pneumonia. In a longitudinal setting, there was no association found between blood type and COVID-19 severity. As the novelty of COVID-19 infection declines with time, further studies on risk-stratification by blood type are encouraged.