The molecular mechanisms underlying premature ovarian failure, which seriously impacts the physical and psychological health of patients, are not fully understood. Here, we present the role of TRDMT1 in reactive oxygen species-induced granulosa cells death, which is considered an important cause of premature ovarian failure. We found that reactive oxygen species were increased in a H2O2 dose-dependent manner and accompanied by the nuclear shuttling of TRDMT1, increased DNA damage and increased apoptosis of granulosa cells. In addition, reactive oxygen species-induced granulosa cells apoptosis could be prevented by the antioxidant N-acetylcysteine or overexpression of TRDMT1. Furthermore, DNA repair following reactive oxygen species induction was severely impaired/enhanced in TRDMT1 mutants, which exhibited reduced/increased RNA m5C methylation activity. Altogether, our results reveal a novel role of TRDMT1 in the regulation of premature ovarian failure through the repair of reactive oxygen species-triggered DNA damage in granulosa cells and provide an improved understanding of the mechanisms underlying granulosa cells apoptosis, which could potentially be useful for future clinical treatments of premature ovarian failure. Diffuse gliomas are the most common malignant brain tumors, and immune checkpoint inhibitors have limited therapeutic effects against this cancer. Three oncogenic pathways are altered in diffuse gliomas the RTK/Ras/PI3K/AKT signaling, TP53, and RB pathways. AZD3965 datasheet Although these pathways may affect the tumor immune microenvironment, their association with immunotherapy biomarkers remains unclear. We used copy number variation and mutation data to stratify patients with specific oncogenic signaling alterations, and evaluated their correlation with predictive immunotherapy biomarkers, including tumor mutation burden (TMB), immune cytolytic activity (CYT), tumor purity, and tumor-infiltrating CD8 T cells. Immune checkpoint expression and interferon-γ signaling activity were also compared in these samples. We identified differentially expressed genes in three distinct oncogenic pathways. Gene ontology analysis of these genes revealed the involvement of RTK/Ras/PI3K/AKT-associated genes in immune and inflammatorye sensitive to immunotherapy. A combination of small-molecule kinase inhibitors and immunotherapy is proposed for this subgroup of tumors. Dysphonia influences the quality of life by interfering with communication. However, a laryngoscopic examination is expensive and not readily accessible in primary care units. Experienced laryngologists are required to achieve an accurate diagnosis. This study sought to detect various vocal fold diseases through pathological voice recognition using artificial intelligence. We collected 189 normal voice samples and 552 samples of individuals with voice disorders, including vocal atrophy (n=224), unilateral vocal paralysis (n=50), organic vocal fold lesions (n=248), and adductor spasmodic dysphonia (n=30). The 741 samples were divided into 2 sets 593 samples as the training set and 148 samples as the testing set. A convolutional neural network approach was applied to train the model, and findings were compared with those of human specialists. The convolutional neural network model achieved a sensitivity of 0.66, a specificity of 0.91, and an overall accuracy of 66.9% for distinguishing normal voice, vocl doubts about the presence of pathologies.Voice alone could be used for common vocal fold disease recognition through a deep learning approach after training with our Mandarin pathological voice database. This approach involving artificial intelligence could be clinically useful for screening general vocal fold disease using the voice. The approach includes a quick survey and a general health examination. It can be applied during telemedicine in areas with primary care units lacking laryngoscopic abilities. It could support physicians when prescreening cases by allowing for invasive examinations to be performed only for cases involving problems with automatic recognition or listening and for professional analyses of other clinical examination results that reveal doubts about the presence of pathologies. Chemopreventive agents such as selective estrogen receptor modulators and aromatase inhibitors have proven efficacy in reducing breast cancer risk by 41% to 79% in high-risk women. Women at high risk of developing breast cancer face the complex decision of whether to take selective estrogen receptor modulators or aromatase inhibitors for breast cancer chemoprevention. RealRisks is a patient-centered, web-based decision aid (DA) designed to promote the understanding of breast cancer risk and to engage diverse women in planning a preference-sensitive course of decision making about taking chemoprevention. This study aims to understand the perceptions of women at high risk of developing breast cancer regarding their experience with using RealRisks-a DA designed to promote the uptake of breast cancer chemoprevention-and to understand their information needs. We completed enrollment to a randomized controlled trial among 300 racially and ethnically diverse women at high risk of breast cancer who were assigneacceptability of the RealRisks web-based DA among a diverse group of high-risk women, who provided some recommendations for improvement. Although big data and smart technologies allow for the development of precision medicine and predictive models in health care, there are still several challenges that need to be addressed before the full potential of these data can be realized (eg, data sharing and interoperability issues, lack of massive genomic data sets, data ownership, and security and privacy of health data). Health companies are exploring the use of blockchain, a tamperproof and distributed digital ledger, to address some of these challenges. In this viewpoint, we aim to obtain an overview of blockchain solutions that aim to solve challenges in health care from an industry perspective, focusing on solutions developed by health and technology companies. We conducted a literature review following the protocol defined by Levac et al to analyze the findings in a systematic manner. In addition to traditional databases such as IEEE and PubMed, we included search and news outlets such as CoinDesk, CoinTelegraph, and Medium. Health care companies are using blockchain to improve challenges in five key areas.