中南大学学报(医学版)最新文献

As a disease that seriously affects the health of men and women of childbearing age, the incidence of infertility is increasing worldwide, and the popularization of assisted reproductive technology (ART) is expected to improve the situation. However, invitro fertilization and embryo transfer (IVF) success rates are only about 50%, and IVF success rates are affected by a number of factors. For example, semen quality, endometrial thickness, fallopian tube patency, embryo selection and transplantation, uterine microenvironment, etc., and the treatment process of IVF is highly dependent on the clinical experience of embryologists, and there is a lack of objective and unified evaluation criteria. Artificial intelligence (AI) is ideally suited to processing and analyzing large, dynamic temporal data sets to assist physicians in making more objective and precise decisions, thereby improving IVF success. At present, artificial intelligence technology using different types of algorithms has been used for sperm classification, oocyte and embryo selection, and prediction of embryo development after implantation, etc. The application of AI in the field of assisted reproduction is expected to improve infertility diagnosis results and increase the pregnancy rate and live birth rate of ART, but there are still certain controversies in privacy, safety and other aspects.In the future, with the accumulation of high-quality datasets, algorithm optimization and the advancement of imaging technology, AI is expected to increase the success rate of ART by selecting higher-quality sperm and oocytes, as well as embryos with greater developmental potential. This will bring significant innovation to the field of reproductive medicine and the entire healthcare sector, while also reducing treatment costs.

Radiotherapy is an important treatment modality for tumors and is involved in the treatment course of more than 50% of cancer patients. However, resistance to ionizing radiation results in suboptimal radiotherapy efficacy and contributes to tumor recurrence and metastasis at later stages. With the increasing understanding of tumor pathogenesis, cancer stem cell (CSC), characterized by self-renewal capacity and differentiation potential, have attracted growing attention and play key roles in radiotherapy resistance, tumor progression, metastasis, immune evasion, and other processes. Mechanisms including the tumor microenvironment, DNA damage repair, and epithelial-mesenchymal transition (EMT) are critical drivers of CSC-mediated radiotherapy resistance. Reviewing the key pathways involved and identifying CSC-specific therapeutic targets may provide new insights for developing more efficient and less toxic radiotherapy strategies.

Flavonoids are naturally occurring polyphenolic compounds widely distributed in nature, exhibiting pharmacological activities including anti-inflammatory effects and inhibition of cell proliferation. Their broader application has been constrained by unclear therapeutic targets. Recent advances in high-throughput sequencing and high-resolution mass spectrometry have elevated the importance of multi-omics analysis for elucidating flavonoid pharmacological effects, therapeutic targets, and regulatory networks. Integration of genomics, transcriptomics, proteomics, metabolomics, and metagenomics enables systematic characterization of flavonoid targets and modulation networks. Clarifying the application of multi-omics technologies in this field may support the clinical translation of flavonoids and provide new strategies for precision research in traditional Chinese medicine.

Tear fluid, also referred to as tears or tear film, is an important biological fluid that plays a key role in maintaining ocular surface health and immune homeostasis. Recent studies have found that tear fluid not only participates in the occurrence and development of ocular diseases, but also exerts profound effects in the immune pathological mechanisms of systemic diseases, breaking through the inherent understanding previously held by the scientific community. Immune cells in tear fluid (such as T cells, neutrophils, natural killer cells, macrophages), cytokines, and immunoglobulins can specifically participate in autoimmune diseases (such as Sjögren's syndrome, rheumatoid arthritis, systemic lupus erythematosus, multiple sclerosis, Graves' ophthalmopathy) and systemic diseases (such as Alzheimer's disease, diabetes mellitus, graft-versus-host disease). The dynamic changes in tear fluid components can reflect systemic immune homeostasis imbalance. Tear fluid biomarkers, such as exosomal microRNA (miR)-204, miR-200b-5p, and the protein marker β2-microglobulin, have shown great potential in early disease screening, diagnostic stratification, and therapeutic target discovery. Tear fluid immune component analysis may provide innovative diagnostic tools and therapeutic targets for systemic diseases. Future research should focus on promoting the standardization and clinical transformation of tear fluid testing technologies and their clinical application.

Breast cancer is one of the most common malignant tumors in women worldwide, and its high incidence and mortality rate seriously threaten women's health. Studies show that the forkhead box O3a (FoxO3a) plays a key role in the occurrence and progression of breast cancer, particularly in the regulation of apoptosis. As a major member of the FoxO family, FoxO3a exerts tumor-suppressive functions by participating in apoptosis regulation and cell-cycle control. In breast cancer cells, FoxO3a acts as a downstream signaling hub of multiple upstream pathways including phosphatidylinositol 3-kinase/protein kinase B (PI3K/AKT), mitogen-activated protein kinase (MAPK), and serum- and glucocorticoid-regulated kinase 1 (SGK1). Through nucleocytoplasmic shuttling and alterations in transcriptional activity, FoxO3a precisely modulates the expression of apoptosis-related target genes such as Bcl-2-interacting mediator of cell death (Bim) and p53-upregulated modulator of apoptosis (PUMA), thereby influencing cell survival or death. In addition, multiple natural compounds and combination therapies can induce apoptosis in breast cancer cells by restoring or enhancing FoxO3a activity, and may partially overcome treatment resistance. Systematic elucidation of the complexity of the FoxO3a signaling network and its dual roles in breast cancer therapy may provide theoretical support for understanding tumor-drug resistance mechanisms and for developing precision therapeutic strategies targeting FoxO3a nodes. Future research should further clarify the functional differences among FoxO3a splice variants and FoxO family members, reveal the molecular basis of FoxO3a functional switching in the tumor microenvironment, and promote the clinical translation of biomarkers and targeted drugs.