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Flavonoids have more potent therapeutic efficacy for the treatment of epileptic disorders. Certain flavonoids possess significant synergistic effects and can be taken with particular antiepileptic drugs (AEDs) to reduce the possibility of drug resistance. The combination of herbal and traditional medicines may not only prevent adverse effects but also increase the drug effects in overall comprehensive efficacy. The present study reviewed the potential efficacy of active constituents of selected flavonoids. Furthermore, in recent years, various researches have been attentive to specific flavonoids for their neuroprotective potential in in-vitro and in-vivo models. We have selected some specific flavonoids that have already proven therapeutic potential in animal epileptic models. Certain flavonoids possess significant synergistic effects and can be taken with particular AEDs to reduce the possibility of drug resistance. Some flavonoids are shown to exhibit their neuroprotective effects in Parkinson's disease, Alzheimer's disease, and ischemic stroke, which are the most common examples of neurological disorders. The major outcomes of flavonoids on progressive neurological disorders may be correlated to the regulation of gamma-aminobutyric acid (GABA) receptors. It has been reported that the neuroprotective actions of naringenin in experimental strokes are mediated by a reduction in NF-B-induced neuroinflammation. These discoveries appear to suggest that some flavonoids are more effective than conventional antiepileptic medicines, which have fewer side effects and can be used for the cure of epileptic seizures. Recently, in many research studies, it has been found that plant-derived flavonoids provide synergistic pharmacological action in experimental models of severe epilepsy. Keywords: Flavonoid, Therapeutic efficacy, Traditional medicine, Neuroprotective effect, Alternative therapy.

The relationship between environmental factors and child health is not well understood in rural Pakistan. This study characterized the environmental factors related to the morbidity of acute respiratory infections (ARIs), diarrhea, and growth using geographical information systems (GIS) technology. Anthropometric, address and disease prevalence data were collected through the SEEM (Study of Environmental Enteropathy and Malnutrition) study in Matiari, Pakistan. Publicly available map data were used to compile coordinates of healthcare facilities. A Pearson correlation coefficient (r) was used to calculate the correlation between distance from healthcare facilities and participant growth and morbidity. Other continuous variables influencing these outcomes were analyzed using a random forest regression model. In this study of 416 children, we found that participants living closer to secondary hospitals had a lower prevalence of ARI (r = 0.154, p &lt, 0.010) and diarrhea (r = 0.228, p &lt, 0.001) as well as participants living closer to Maternal Health Centers (MHCs): ARI (r = 0.185, p &lt, 0.002) and diarrhea (r = 0.223, p &lt, 0.001) compared to those living near primary facilities. Our random forest model showed that distance has high variable importance in the context of disease prevalence. Our results indicated that participants closer to more basic healthcare facilities reported a higher prevalence of both diarrhea and ARI than those near more urban facilities, highlighting potential public policy gaps in ameliorating rural health.

Infectious disease is the leading cause of mortality in children under five. This study has investigated environmental factors related to the morbidity of acute respiratory infections (ARIs), diarrhea, and growth using geographical information systems (GIS) technology. Anthropometric, address and disease prevalence data were collected through the SEEM study in Matiari, Pakistan. Publicly available map data was used to compile coordinates of healthcare facilities. A Pearson correlation coefficient (r) was used to calculate the correlation between distance from healthcare facilities and participant growth and morbidity. Other continuous variables influencing these outcomes were analyzed using a random forest regression model. In this study of 416 children, we found participants living closer to secondary hospitals had lower prevalence of ARI (r=0.154, p

ObjectivesStriking histopathological overlap between distinct but related conditions poses a significant disease diagnostic challenge. There is a major clinical need to develop computational methods enabling clinicians to translate heterogeneous biomedical images into accurate and quantitative diagnostics. This need is particularly salient with small bowel enteropathies; Environmental Enteropathy (EE) and Celiac Disease (CD). We built upon our preliminary analysis by developing an artificial intelligence (AI)-based image analysis platform utilizing deep learning convolutional neural networks (CNNs) for these enteropathies.MethodsData for secondary analysis was obtained from three primary studies at different sites. The image analysis platform for EE and CD was developed using convolutional neural networks (CNNs: ResNet and custom Shallow CNN). Gradient-weighted Class Activation Mappings (Grad-CAMs) were used to visualize the models’ decision making process. A team of medical experts simultaneously reviewed the stain color normalized images done for bias reduction and Grad-CAM visualizations to confirm structural preservation and biological relevance, respectively.Results461 high-resolution biopsy images from 150 children were acquired. Median age (interquartile range) was 37·5 (19·0 to 121·5) months with a roughly equal sex distribution; 77 males (51·3%). ResNet50 and Shallow CNN demonstrated 98% and 96% case-detection accuracy, respectively, which increased to 98·3% with an ensemble. Grad-CAMs demonstrated models’ ability to learn distinct microscopic morphological features.ConclusionOur AI-based image analysis platform demonstrated high classification accuracy for small bowel enteropathies which was capable of identifying biologically relevant microscopic features, emulating human pathologist decision making process, performing in the case of suboptimal computational environment, and being modified for improving disease classification accuracy. Grad-CAMs that were employed illuminated the otherwise ‘black box’ of deep learning in medicine, allowing for increased physician confidence in adopting these new technologies in clinical practice.What is knownStriking histopathological overlap exists between distinct but related conditions which poses a significant disease diagnostic challenge; such as for small bowel enteropathies including Environmental Enteropathy (EE) and Celiac Disease (CD).There is a major clinical need to develop computational [including Artificial Intelligence (AI) and deep learning] methods enabling clinicians to translate heterogeneous biomedical images into accurate and quantitative diagnostics.A major issue plaguing the use of AI in medicine is the so-called ‘black box’ of deep learning, an analogy which describes the lack of insight that humans have into how the models arrive at their decision-makingWhat is newAI-based image analysis platform demonstrated high classification accuracy for small bowel enteropathies (EE vs. CD vs. histologically normal controls).Gradient-weighted Class Activation Mappings (Grad-CAMs) illuminated the otherwise ‘black box’ of deep learning in medicine, allowing for increased physician confidence in adopting these new technologies in clinical practice.

Artificial intelligence (AI), a discipline encompassed by data science, has seen recent rapid growth in its application to healthcare and beyond, and is now an integral part of daily life. Uses of AI in gastroenterology include the automated detection of disease and differentiation of pathology subtypes and disease severity. Although a majority of AI research in gastroenterology focuses on adult applications, there are a number of pediatric pathologies that could benefit from more research. As new and improved diagnostic tools become available and more information is retrieved from them, AI could provide physicians a method to distill enormous amounts of data into enhanced decision-making and cost saving for children with digestive disorders. This review provides a broad overview of AI and examples of its possible applications in pediatric gastroenterology.

Physicians use biopsies to distinguish between different but histologically similar enteropathies. The range of syndromes and pathologies that could cause different gastrointestinal conditions makes this a difficult problem. Recently, deep learning has been used successfully in helping diagnose cancerous tissues in histopathological images. These successes motivated the research presented in this paper, which describes a deep learning approach that distinguishes between Celiac Disease (CD) and Environmental Enteropathy (EE) and normal tissue from digitized duodenal biopsies. Experimental results show accuracies of over 90% for this approach. We also look into interpreting the neural network model using Gradient-weighted Class Activation Mappings and filter activations on input images to understand the visual explanations for the decisions made by the model.

Team strategizing is an important aspect which requires critical analysis to ensure a desirable near-optimum performance. The key to solve this issue is by tapping the available talent within the team which at times, can be elusive. With increasing competition, a talented team, with an ineffective and outdated scouting strategy, may have to face unfavourable results. In this paper, we have conducted research in the domain of Sports, specifically Soccer. Strategy considered in the research is centered around deciding the lineup of a team by assessing the skillset of the players. Considering the novelty of the approach, we have developed our own web scraping algorithm to collect the dataset. Machine Learning models like Neural Network(MultiLayer Per-ceptron), Random Forests and Logistic Regression have been used to make predict the position a particular player will perform best at. The accuracy of the said models have been analysed for comparative analysis.