Enhancing Clinical Validation for Early Cardiovascular Disease Prediction through Simulation, AI, and Web Technology.

Cardiovascular diseases (CVDs) remain a major global health challenge and a leading cause of mortality, highlighting the need for improved predictive models. We introduce an innovative agent-based dynamic simulation technique that enhances our AI models' capacity to predict CVD progression. This method simulates individual patient responses to various cardiovascular risk factors, improving prediction accuracy and detail. Also, by incorporating an ensemble learning model and interface of web application in the context of CVD prediction, we developed an AI dashboard-based model to enhance the accuracy of disease prediction and provide a user-friendly app. The performance of traditional algorithms was notable, with Ensemble learning and XGBoost achieving accuracies of 91% and 95%, respectively. A significant aspect of our research was the integration of these models into a streamlit-based interface, enhancing user accessibility and experience. The streamlit application achieved a predictive accuracy of 97%, demonstrating the efficacy of combining advanced AI techniques with user-centered web applications in medical prediction scenarios. This 97% confidence level was evaluated by Brier score and calibration curve. The design of the streamlit application facilitates seamless interaction between complex ML models and end-users, including clinicians and patients, supporting its use in real-time clinical settings. While the study offers new insights into AI-driven CVD prediction, we acknowledge limitations such as the dataset size. In our research, we have successfully validated our predictive proposed methodology against an external clinical setting, demonstrating its robustness and accuracy in a real-world fixture. The validation process confirmed the model's efficacy in the early detection of CVDs, reinforcing its potential for integration into clinical workflows to aid in proactive patient care and management. Future research directions include expanding the dataset, exploring additional algorithms, and conducting clinical trials to validate our findings. This research provides a valuable foundation for future studies, aiming to make significant strides against CVDs.

Innovative Machine Learning Strategies for Early Detection and Prevention of Pregnancy Loss: The Vitamin D Connection and Gestational Health.

Early pregnancy loss (EPL) is a prevalent health concern with significant implications globally for gestational health. This research leverages machine learning to enhance the prediction of EPL and to differentiate between typical pregnancies and those at elevated risk during the initial trimester. We employed different machine learning methodologies, from conventional models to more advanced ones such as deep learning and multilayer perceptron models. Results from both classical and advanced machine learning models were evaluated using confusion matrices, cross-validation techniques, and analysis of feature significance to obtain correct decisions among algorithmic strategies on early pregnancy loss and the vitamin D serum connection in gestational health. The results demonstrated that machine learning is a powerful tool for accurately predicting EPL, with advanced models such as deep learning and multilayer perceptron outperforming classical ones. Linear discriminant analysis and quadratic discriminant analysis algorithms were shown to have 98 % accuracy in predicting pregnancy loss outcomes. Key determinants of EPL were identified, including levels of maternal serum vitamin D. In addition, prior pregnancy outcomes and maternal age are crucial factors in gestational health. This study's findings highlight the potential of machine learning in enhancing predictions related to EPL that can contribute to improved gestational health outcomes for mothers and infants.

Anti-Angiogenic Effect of Cantharellus cibarius Extracts, its Correlation with Lipoxygenase Inhibition, and Role of the Bioactives Therein.

Angiogenesis is a complex physiological process that cannot be treated with single agent therapy. Several edible fungi have been known to encompass bioactive compounds, and are promising sources of multi-component drugs. One such widely consumed edible fungi is Cantharellus cibarius, which has been explored for its biological activities. The present study focused on assessing the anti-angiogenic activity of petroleum ether and ethanol extracts of C. cibarius using chick chorioallantoic membrane (CAM) assay. Both the extracts showed a dose-dependent response which was compared with the anti-angiogenic activity of the positive controls silibinin, and lenalidomide. The extracts were also studied for their lipoxygenase (LOX) inhibitory potential and compared to ascorbic acid as the positive control. The IC50 values of the petroleum ether extract, ethanol extract, and ascorbic acid for LOX inhibition assay were 135.4, 113.1, and 41.5 µg/mL, respectively. Although both the extracts showed similar responses in CAM assay, ethanol extract proved to be more potent in LOX inhibition assay. Finally, the extracts were investigated for their chemical composition using GC-MS. A correlation between LOX inhibition and anti-angiogenic potential was established at the molecular level. A meticulous literature search was carried out to correlate the biochemical composition of the extracts to their anti-angiogenic activity.

Anti-angiogenic and anti-inflammatory activity of the summer truffle (Tuber aestivum Vittad.) extracts and a correlation with the chemical constituents identified therein.

Fungi are a huge source of unexplored bioactive compounds. Owing to their biological activities, several fungi have shown commercial application in the health industry. Tuber aestivum Vittad. is one such edible fungi with an immense scope for practical biological applications. In the present study, the anti-angiogenic activity of petroleum ether and ethanol extracts of T. aestivum was investigated using the chick chorioallantoic membrane assay and compared to the positive controls silibinin and lenalidomide. Both the extracts showed a dose-dependent anti-angiogenic response. The extracts were also assessed for their anti-inflammatory potential by lipoxygenase-inhibition assay. The IC50 values for LOX inhibition assay, computed by the Boltzmann plot, were 368.5, 147.3 and 40.2 µg/mL, for the petroleum ether extract, ethanol extract, and the positive control ascorbic acid, respectively. The ethanol extract of T. aestivum showed superior anti-angiogenic and anti-inflammatory activity than the petroleum ether extract. Compositional investigation of the extracts by GC-MS revealed the presence of various bioactive compounds. The compounds were correlated to their anti-angiogenic and anti-inflammatory activity based on a meticulous literature search.