Background: Machine learning is a potentially effective method for predicting the response to platinum-based treatment for ovarian cancer. However, the predictive performance of various machine learning methods and variables is still a matter of controversy and debate. Objective: This study aims to systematically review relevant literature on the predictive value of machine learning for platinum-based chemotherapy responses in patients with ovarian cancer. Methods: Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we systematically searched the PubMed, Embase, Web of Science, and Cochrane databases for relevant studies on predictive models for platinum-based therapies for the treatment of ovarian cancer published before April 26, 2023. The Prediction Model Risk of Bias Assessment tool was used to evaluate the risk of bias in the included articles. Concordance index (C-index), sensitivity, and specificity were used to evaluate the performance of the prediction models to investigate the predictive value of machine learning for platinum chemotherapy responses in patients with ovarian cancer. Results: A total of 1749 articles were examined, and 19 of them involving 39 models were eligible for this study. The most commonly used modeling methods were logistic regression (16/39, 41%), Extreme Gradient Boosting (4/39, 10%), and support vector machine (4/39, 10%). The training cohort reported C-index in 39 predictive models, with a pooled value of 0.806; the validation cohort reported C-index in 12 predictive models, with a pooled value of 0.831. Support vector machine performed well in both the training and validation cohorts, with a C-index of 0.942 and 0.879, respectively. The pooled sensitivity was 0.890, and the pooled specificity was 0.790 in the training cohort. Conclusions: Machine learning can effectively predict how patients with ovarian cancer respond to platinum-based chemotherapy and may provide a reference for the development or updating of subsequent scoring systems.

Infertility is a global reproductive disorder which is caused by a variety of complex diseases. Infertility affects the individual, family, and community through physical, psychological, social and economic consequences. The results from recent preclinical studies regarding stem cell-based therapies are promising. Stem cell-based therapies cast a new hope for infertility treatment as a replacement or regeneration strategy. The main features and application prospects of mesenchymal stem cells in the future of infertility should be understood by clinicians. Mesenchymal stem cells (MSCs) are multipotent stem cells with abundant source, active proliferation, and multidirectional differentiation potential. MSCs play a role through cell homing, secretion of active factors, and participation in immune regulation. Another advantage is that, compared with embryonic stem cells, there are fewer ethical factors involved in the application of MSCs. However, a number of questions remain to be answered prior to safe and effective clinical application. In this review, we summarized the recent status of MSCs in the application of the diseases related to or may cause to infertility and suggest a possible direction for future cytotherapy to infertility.

Copper, an essential trace element for the human body, plays a key role in energy metabolism, mitochondrial respiration, redox reactions, and neural signal transmission. The recently proposed concept of “cuproptosis” has further revealed the unique status of copper in cellular regulation: when copper abnormally accumulates within cells, it can directly bind to the lipoylated proteins of the mitochondrial TCA cycle, triggering protein aggregation and metabolic disorders, ultimately leading to cell death. This form of cell death plays an important role in various neurodegenerative diseases of the central nervous system, such as Alzheimer’s disease (AD), Parkinson’s disease (PD), amyotrophic lateral sclerosis (ALS), Huntington’s disease (HD), and stroke. This review summarizes recent research on the mechanisms of cuproptosis, providing new perspectives and a theoretical basis for understanding the pathogenesis of these neurodegenerative diseases.

Major depressive disorder (MDD) is a highly prevalent and heterogeneous psychiatric disorder shaped by the interplay of genetic, environmental, and epigenetic factors. Histone modifications, particularly acetylation and methylation, have emerged as critical regulators of chromatin dynamics and gene expression in stress adaptation, neuroplasticity, and emotional regulation. This review synthesizes current evidence linking dysregulated histone deacetylases (HDACs) and histone methyltransferases (HMTs) to impaired neuroplasticity, neuroinflammation, mitochondrial dysfunction, and hypothalamic–pituitary–adrenal (HPA) axis hyperactivity in depression. We further evaluate the therapeutic potential of HDAC and HMT inhibitors, highlight their effects beyond transcriptional control, and discuss peripheral epigenetic biomarkers as candidate tools for patient stratification and treatment prediction. Emerging technologies, including single-cell and spatial epigenomics as well as CRISPR-based epigenetic editing, are outlined as future avenues toward precision medicine. While isoform specificity, off-target effects, and translational heterogeneity remain major challenges, targeting histone modifications represents a promising strategy for next-generation antidepressant development.

Neurodegenerative diseases are complex disorders characterized by the progressive loss of neuronal structure and function, involving pathological mechanisms such as oxidative stress, chronic inflammation, protein misfolding, and impaired synaptic plasticity. Recent studies have revealed that epigenetic regulation plays a critical role in the onset and progression of these diseases, including mechanisms such as DNA methylation, histone modifications, and non-coding RNA regulation. Natural polyphenolic compounds, known for their safety and multi-target properties, have emerged as promising candidates for neuroprotection and therapeutic intervention. This review summarizes recent advances in the neuroprotective effects of polyphenols in neurodegenerative diseases through epigenetic mechanisms. It focuses on their regulation of DNA methyltransferase activity, histone acetylation status, and non-coding RNA expression, as well as their influence on neurotrophic factors, inflammatory mediators, and synapse-related gene expression. Polyphenolic compounds such as curcumin, resveratrol, and epigallocatechin gallate (EGCG) have been shown to modulate epigenetic enzyme activity, alleviate neuroinflammation, improve mitochondrial function, and promote neuroregeneration, demonstrating multi-level neuroprotective effects. However, clinical translation is limited by low oral bioavailability, rapid metabolism, and insufficient brain-targeting capacity. Emerging strategies including nano-delivery systems, prodrug design, and omics technologies hold promise in overcoming these limitations and enhancing therapeutic efficacy. Polyphenols exert multi-target regulatory effects via epigenetic mechanisms in neurodegenerative diseases, showing great potential for intervention. In the future, integrating precision nutrition, personalized treatment, and multi-omics approaches may facilitate the development of novel epigenetic-based strategies for the prevention and treatment of neurodegenerative disorders.