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 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.Peer reviewe