Environmental pollution plays a major role in the development of prostate cancer (PCA). However, there has been no research on machine learning (ML) modelling between multiple heavy metal exposures and PCA risk. Based on the 8022 samples from the 2003-2018 National Health and Nutrition Examination Survey (NHANES) database, we utilized the information pertaining to the concentrations of 18 blood and urinary heavy metals and minerals as well as 14 covariates. Among the eight ML models evaluated, the random forest (RF) algorithm showed superior performance, achieving an accuracy of 72.835 %, an area under the receiver operating characteristic curve (AUC) of 0.869, an F₁ score of 0.145, a G-mean of 0.749, and a Youden index of 0.498 in the test set. Four interpretable methods were integrated into the ML model. RF found that specific levels of blood lead (Pb) (0.449-29.964 µg/dL), urinary cesium (Cs) (1.822-270.426 µg/L), and urinary antimony (Sb) (0.015-4.953 µg/L) were positively associated with the PCA risk, while blood cadmium (Cd) (0.247-9.025 µg/L) showed a negative association. Notably, urinary Cs and Sb emerged as novel risk-related metals for the PCA in our study. The synergistic effect analysis further identified blood Pb, urinary Sb, and urinary Cs as the major contributing factors. The predictive model established in this study can provide valuable strategies for the prevention and the control of PCA.

Background: Tumor-associated macrophages (TAMs) are key components of the hepatocellular carcinoma (HCC) microenvironment, but their spatial heterogeneity remains incompletely characterized. We aimed to assess the biological and prognostic relevance of a BAG3-associated TAM program in HCC. Methods: Public single-cell RNA sequencing (scRNA-seq) datasets were analyzed to characterize TAM heterogeneity, and an integrated validation scRNA-seq dataset was used to assess reproducibility. Spatial transcriptomics was used to provide spatial context in a small treatment-exposed cohort. Pseudotime, regulatory network, and cell–cell communication analyses were performed to characterize state transitions and microenvironmental interactions. Survival modeling evaluated the prognostic relevance of the BAG3-associated program. Results: Five TAM subsets were identified, including MARCO⁺, MT⁺ RTM−, MMP9⁺, UBE2C⁺, and BAG3⁺ TAMs. Among them, BAG3⁺ TAMs, a less well-characterized subset, exhibited coordinated stress-adaptive, proteostasis-related, and matrix-remodeling programs that were reproduced in the validation dataset. Pseudotime analysis suggested a continuum of TAM states, with BAG3⁺ TAM stress-remodeling features enriched toward late pseudotime. Communication analysis centered on BAG3⁺ TAMs suggested crosstalk between inflammatory stress cues and angiogenic, stromal-remodeling, and immunomodulatory programs; this pattern was primarily supported by HBV-derived samples and recurrently involved the MIF–CD74 axis. Spatial mapping further supported BAG3⁺ TAM-enriched niches with elevated AP-1, EGR1, and NFKB1 activity. A BAG3-associated risk score derived from a 10-gene signature remained an independent prognostic factor for overall survival after clinical adjustment. Conclusions: These findings characterize a BAG3-associated TAM program with spatial, immunoregulatory, and prognostic relevance in HCC, and support its further evaluation in biomarker and mechanistic studies.

The analysis of cognitive trajectories is relatively underexplored in China. Furthermore, most previous studies examining the association between cognitive function and mortality have been limited to cross-sectional perspectives. This study aims to identify distinct cognitive trajectories and the corresponding influencing factors and investigate the impact of these trajectories on all-cause mortality in Chinese older adults. A total of 6232 subjects aged 65 years and above were drawn from the Chinese Longitudinal Healthy Longevity Survey. Growth mixture models were utilized to identify different cognitive trajectories, while Cox proportional hazards models were used to examine the association between the cognitive trajectories and all-cause mortality after adjusting for covariates. Four cognitive trajectories were identified: rapid decline group, slow decline group, low-level stable group, and high-level stable group. Some factors such as age, sex, and marital status were significantly associated with trajectories. Compared to the high-level stable group, adjusted hazard ratios and 95% confidence intervals (CIs) for the all-cause mortality were 3.87 (95% CI: 3.35–4.48), 1.41 (95% CI: 1.24–1.59), and 1.37 (95% CI: 1.18–1.58) for the rapid decline group, the slow decline group, and the low-level stable group, respectively, indicating that these three groups had a higher mortality risk. In summary, these findings facilitate the development of targeted health promotion measures, which have implications for reducing the social and economic burdens of cognitive decline.