The immune microenvironment is a culmination of the collaborative effort of immune cells and is important in cancer development. The underlying mechanisms of the tumor immune microenvironment in regulating prostate cancer (PRAD) are unclear. In the current study, 144 natural killer cell-related genes were identified using differential expression, single-sample gene set enrichment analysis, and weighted gene coexpression network analysis. Furthermore, VCL, ACTA2, MYL9, MYLK, MYH11, TPM1, ACTG2, TAGLN, and FLNC were selected as hub genes via the protein-protein interaction network. Based on the expression patterns of the hub genes, endothelial, epithelial, and tissue stem cells were identified as key cell subpopulations, which could regulate PRAD via immune response, extracellular signaling, and protein formation. Moreover, 27 genes were identified as prognostic signatures and used to construct the risk score model. Receiver operating characteristic curves revealed the good performance of the risk score model in both the training and testing datasets. Different chemotherapeutic responses were observed between the low- and high-risk groups. Additionally, a nomogram based on the risk score and other clinical features was established to predict the 1-, 3-, and 5-year progression-free interval of patients with PRAD. This study provides novel insights into the molecular mechanisms of the immune microenvironment and its role in the pathogenesis of PARD. The identification of key cell subpopulations has a potential therapeutic and prognostic use in PRAD.

Artificial intelligence (AI) possesses the transformative potential to reshape pediatric medicine, offering powerful tools for diagnosis, prognosis, and personalized therapy. This review focuses on three domains selected for their relative maturity in AI development and proximity to clinical translation—pediatric critical care, perinatal and neonatal medicine, and precision oncology—evaluating current evidence for clinical utility and outlining challenges to implementation. AI is demonstrating significant potential across these domains: in critical care, deep learning models outperform traditional scoring systems for dynamic prediction of adverse events; in perinatal and neonatal medicine, AI enhances prenatal ultrasonography and integrates multiomics data to guide complex therapies; and in oncology, radiomics, and genomic analysis enable non‐invasive tumor characterization and personalized treatment strategies. However, significant hurdles remain. Foundational data challenges—including scarcity, heterogeneity, and limited sharing of pediatric data—are being addressed through transfer learning, federated learning, and synthetic data generation. Clinical translation is further impeded by algorithmic bias, the ‘black box’ problem, and the unique developmental physiology of children, which demands age‐specific model validation. Future progress depends on multi‐institutional collaboration, a research focus that extends beyond prediction to encompass causal inference and explainability, and the establishment of robust ethical, regulatory, and economic frameworks. Ultimately, responsible implementation of AI in pediatrics requires building systems that are not merely accurate but transparent, equitable, and trustworthy.

Background Acid sphingomyelinase deficiency (ASMD) disorder, also known as Niemann–Pick disease (NPD) is a rare genetic disease caused by mutations in SMPD1 gene, which encodes sphingomyelin phosphodiesterase (ASM). Except for liver and spleen enlargement and lung disease, two subtypes (Type A and B) of NDP have different onset times, survival times, ASM activities, and neurological abnormalities. To comprehensively explore the genotype-phenotype association and pathophysiological characteristics of NPD, we collected 144 NPD cases with strict quality control through literature mining. Results The difference in ASM activity can differentiate NPD type A from other subtypes, with the ratio of ASM activity to the reference values being lower in type A (threshold 0.045 (4.45%)). Severe variations, such as deletion and insertion, can cause complete loss of ASM function, leading to type A, whereas relatively mild missense mutations generally result in type B. Among reported mutations, p.Arg3AlafsX76 mutation is highly prevalent in the Chinese population, and p.R608del mutation is common in Mediterranean countries. The expression profiles of SMPD1 from GTEx and single-cell RNA sequencing data of multiple fetal tissues showed that high expressions of SMPD1 can be observed in the liver, spleen, and brain tissues of adults and in hepatoblasts, hematopoietic stem cells, STC2_TLX1-positive cells, mesothelial cells of the spleen, vascular endothelial cells of the cerebellum and the cerebrum of fetuses, indicating that SMPD1 dysfunction is highly likely to have a significant effect on the function of those cell types during development and the clinicians need pay attention to these organs or tissues as well during diagnosis. In addition, we also predicted 21 new pathogenic mutations in the SMPD1 gene that potentially cause the NPD, signifying that more rare cases will be detected with those mutations in SMPD1. Conclusions Our study is the first one to elucidate the effects of SMPD1 mutation on cell types and at the tissue level, which provides new insights into the genotype-phenotype association and can help in the precise diagnosis of NPD.

前列腺癌是男性常见恶性肿瘤之一, 其发生发展受多种因素影响.研究表明肠道菌群在前列腺癌的病理过程中扮演着重要角色. 本综述总结了肠道菌群失调通过慢性炎症、免疫调节和雄激素代谢等机制促进前列腺癌进展的最新研究进展. 前列腺癌患者的肠道菌群组异常,表现为厚壁菌门减少, 变形菌门增多. 高脂饮食导致的菌群紊乱通过代谢物变化和多种信号通路促进肿瘤进展. 针对性调节肠道菌群(如粪菌移植、益生菌补充)有望成为前列腺癌预防和辅助治疗的新策略, 研究表明补充益生菌可明显提高雄激素剥夺治疗效果. 深入研究肠道菌群与前列腺癌的互作机制有助于建立精准干预方案, 促进个体化治疗. 未来需开展大规模队列研究验证肠道菌群调控策略的临床应用价值.

Two variants (rs2069837, and rs13306435) in the IL-6 gene were identified and genotyped from 566 patients with ONFH and 566 healthy controls. The associations between IL-6 polymorphisms and ONFH susceptibility were assessed using odds ratio (OR) and 95% confidence interval (95% CI) via logistic regression. The results of the overall analysis revealed that IL-6 rs2069837 is correlated with decreased risk of ONFH among the Chinese Han population (p < 0.05). In stratified analysis, rs2069837 also reduced the susceptibility to ONFH in older people (> 51 years), males, nonsmokers, and nondrinkers (p < 0.05).