Review: Predictive approaches to breast cancer risk

Early heart disease prediction using feature engineering and machine learning algorithms

Heart disease is one of the most widespread global health issues, it is the reason behind around 32 % of deaths worldwide every year. The early prediction and diagnosis of heart diseases are critical for effective treatment and sickness management. Despite the efforts of healthcare professionals, cardiovascular surgeons and cardiologists' misdiagnosis and misinterpretation of test results may happen every day. This study addresses the growing global health challenge raised by Cardiovascular Diseases (CVDs), which account for 32 % of all deaths worldwide, according to the World Health Organization (WHO). With the progress of Machine Learning (ML) and Deep Learning (DL) techniques as part of Artificial Intelligence (AI), these technologies have become crucial for predicting and diagnosing CVDs. This research aims to develop an ML system for the early prediction of cardiovascular diseases by choosing one of the powerful existing ML algorithms after a deep comparative analysis of several. To achieve this work, the Cleveland and Statlog heart datasets from international platforms are used in this study to evaluate and validate the system's performance. The Cleveland dataset is categorized and used to train various ML algorithms, including decision tree, random forest, support vector machine, logistic regression, adaptive boosting, and K-nearest neighbors. The performance of each algorithm is assessed based on accuracy, precision, recall, F1 score, and the Area Under the Curve metrics. Hyperparameter tuning approaches have been employed to find the best hyperparameters that reflect the optimal performance of the used algorithms based on different evaluation approaches including 10-fold cross-validation with a 95 % confidence interval. The study's findings highlight the potential of ML in improving the early prediction and diagnosis of cardiovascular diseases. By comparing and analyzing the performance of the applied algorithms on both the Cleveland and Statlog heart datasets, this research contributes to the advancement of ML techniques in the medical field. The developed ML system offers a valuable tool for healthcare professionals in the early prediction and diagnosis of cardiovascular diseases, with implications for the prediction and diagnosis of other diseases as well.

Novel sulfonamide-indolinone hybrids targeting mitochondrial respiration of breast cancer cells

Breast cancer (BC) still poses a threat worldwide which demands continuous efforts to present safer and efficacious treatment options via targeted therapy. Beside kinases' aberrations as Aurora B kinase which controls cell division, BC adopts distinct metabolic profiles to meet its high energy demands. Accordingly, targeting both aurora B kinase and/or metabolic vulnerability presents a promising approach to tackle BC. Based on a previously reported indolinone-based Aurora B kinase inhibitor (III), and guided by structural modification and SAR investigation, we initially synthesized 11 sulfonamide-indolinone hybrids (5a-k), which showed differential antiproliferative activities against the NCI-60 cell line panel with BC cells displaying preferential sensitivity. Nonetheless, modest activity against Aurora B kinase (18-49% inhibition) was noted at 100 nM. Screening of a representative derivative (5d) against 17 kinases, which are overexpressed in BC, failed to show significant activity at 1 μM concentration, suggesting that kinase inhibitory activity only played a partial role in targeting BC. Bioinformatic analyses of genome-wide transcriptomics (RNA-sequencing), metabolomics, and CRISPR loss-of-function screens datasets suggested that indolinone-completely responsive BC cell lines (MCF7, MDA-MB-468, and T-47D) were more dependent on mitochondrial oxidative phosphorylation (OXPHOS) compared to partially responsive BC cell lines (MDA-MB-231, BT-549, and HS 578 T). An optimized derivative, TC11, obtained by molecular hybridization of 5d with sunitinib polar tail, manifested superior antiproliferative activity and was used for further investigations. Indeed, TC11 significantly reduced/impaired the mitochondrial respiration, as well as mitochondria-dependent ROS production of MCF7 cells. Furthermore, TC11 induced G0/G1 cell cycle arrest and apoptosis of MCF7 BC cells. Notably, anticancer doses of TC11 did not elicit cytotoxic effects on normal cardiomyoblasts and hepatocytes. Altogether, these findings emphasize the therapeutic potential of targeting the metabolic vulnerability of OXPHOS-dependent BC cells using TC11 and its related sulfonamide-indolinone hybrids. Further investigation is warranted to identify their precise/exact molecular target.