Background: Ovarian cancer (OC) is the most lethal gynecological cancer in the United States. Among the different types of OC, serous ovarian cancer (SOC) stands out as the most prevalent. Transcriptomics techniques generate extensive gene expression data, yet only a few of these genes are relevant to clinical diagnosis. Methods: Methods for feature selection (FS) address the challenges of high dimensionality in extensive datasets. This study proposes a computational framework that applies FS techniques to identify genes highly associated with platinum-based chemotherapy response on SOC patients. Using SOC datasets from the Gene Expression Omnibus (GEO) database, LASSO and varSelRF FS methods were employed. Machine learning classification algorithms such as random forest (RF) and support vector machine (SVM) were also used to evaluate the performance of the models. Results: The proposed framework has identified biomarkers panels with 9 and 10 genes that are highly correlated with platinum–paclitaxel and platinum-only response in SOC patients, respectively. The predictive models have been trained using the identified gene signatures and accuracy of above 90% was achieved. Conclusions: In this study, we propose that applying multiple feature selection methods not only effectively reduces the number of identified biomarkers, enhancing their biological relevance, but also corroborates the efficacy of drug response prediction models in cancer treatment.

JP4-039, a mitochondrial-targeted nitroxide, has emerged as a promising candidate in addressing the intricate interplay of reactive oxygen species (ROS) in cardiovascular disease (CVD). Given the substantial mortality and economic burden associated with CVD globally, novel therapeutic strategies targeting oxidative stress hold significant promise. The pathophysiology of CVD encompasses multifaceted mechanisms, including endothelial dysfunction, inflammation, and oxidative stress, where dysregulated ROS levels play a pivotal role. JP4-039, by selectively targeting mitochondrial ROS, offers a targeted approach to mitigate oxidative stress-induced damage in cardiovascular tissue. Current research elucidates the molecular mechanisms underlying JP4-039’s antioxidant properties, including its ability to scavenge superoxide radical anions and mitigate oxidative chain reactions within mitochondria. Moreover, preclinical studies highlight JP4-039’s efficacy in ameliorating CVD-related pathologies, including atherosclerosis and cardiac hypertrophy, through its antioxidative and anti-inflammatory effects. Future milestones in JP4-039 research involve optimizing its pharmacokinetic (PK) properties and exploring potential synergistic effects with existing cardiovascular therapies, followed by advancing into clinical trials.

Despite significant improvements in revascularization strategies and medical management, ischemic heart disease (IHD) remains the top cause of mortality and disability worldwide. The myocardium lacks regenerative capacity and consequently, recovery depends on re-establishing microvascular integrity and sustaining angiogenesis to preserve viable myocardium. Emerging and novel bioengineering approaches, such as stem cells, extracellular vesicles (EVs), and matrix-based strategies, seek to address this unmet need by promoting neovascularization and structural restoration. However, clinical translation remains limited by poor engraftment, product variability, and arrhythmogenic risk. Large animal models provide a clinically relevant platform to thoroughly investigate these interventions and ideally enhance their translational potential. This review discusses cellular approaches leveraging stem and progenitor cells and acellular modalities using extracellular vesicles, growth factors, or extracellular matrix-based scaffolds with an emphasis on large animal translational models and clinical trials.