Microplastics (MPs) have received a lot of attention and have been detected in multiple environmental matrices as a new environmental hazard, but studies on human internal exposure to MPs are limited. Here, we collected lung tissue samples from 12 nonsmoking patients to evaluate the characteristics of MPs in human lung tissues using an Agilent 8700 laser infrared imaging spectrometer and scanning electron microscopy. We detected 108 MPs covering 12 types in the lung tissue samples, with a median concentration of 2.19 particles/g. Most of the MPs (88.89%) were sized between 20 to 100 μm. Polypropylene accounts for 34.26% of the MPs in the lung tissues, followed by polyethylene terephthalate (21.30%) and polystyrene (8.33%). Compared with males and those living far from a major road (≥300 m), females and those living near the main road (<300 m) had higher levels of MPs in lung tissues, which positively correlated with platelet (PLT), thrombocytocrit, fibrinogen (FIB), and negatively related with direct bilirubin (DB). These findings help confirm the presence in the respiratory system and suggest the potential sources and health effects of inhaled MPs.

The ubiquity of microplastics (MPs) in the environment has raised significant concerns, yet their potential impacts on human health are not fully elucidated. This study aimed to quantify human exposure to MPs in feces and evaluate their associations with oxidative stress and cardiopulmonary function. A panel study was conducted in 16 male college students with three-round visits. Fecal MPs were quantified using infrared micro-spectroscopy, and health effects were assessed through urinary biomarkers of oxidative damage (MDA and 8-OHdG) and cardiopulmonary function tests. Associations between MP exposure and health outcomes were analyzed using linear mixed-effect models. We found that fecal MP amount across 48 samples from 16 participants showed high intra-individual variation and poor reproducibility (ICCs < 0.4). MPs in feces were predominantly identified as sheets and fragments in the 100–200 μm size range, with polyamide (PA), polyester, polyethylene (PE), and polypropylene as the primary polymer types. Significant relationships were observed between fecal MP amount and oxidative damage biomarkers. Each one-unit increase in MPs corresponded to a 0.827 increase in MDA (95% CI: 0.116, 1.54) and a 1.11 increase in 8-OHdG (95% CI: 0.235, 1.98), with fibrous shapes and specific polymers (PE and PA) being the primary drivers. No significant associations were found between MP exposure and lung function or blood pressure. These findings indicated that MP exposure was significantly linked to increased oxidative damage, highlighting a pressing public health concern regarding their subclinical biological effects.

Background: Metabolic dysfunction-associated fatty liver disease (MAFLD) affects 38.9% of the global adult population and is associated with increased mortality when it co-occurs with depression. Women exhibit a 1.5– to 3–fold higher prevalence of depression, with postmenopausal hormonal imbalances amplifying susceptibility. This underscores the urgent need for sex-specific predictive models. The aim of this study was to develop a lightweight, high-accuracy model to identify key predictors of depression risk in female MAFLD patients using a five-year longitudinal UK Biobank cohort. Methods: We analyzed routine blood biomarkers (hematology and metabolites), lifestyle factors, and reproductive history from female participants with MAFLD identified within the UK Biobank cohort. Logistic regression was adjusted for socioeconomic, lifestyle, multimorbidity, and medication use, and was employed to evaluate sex-specific factors. Feature selection employed a two-stage approach to ensure balanced covariate distributions between cases and controls: Random Forest-based stratified bootstrap resampling (1000 iterations) instead of traditional random sampling, followed by recursive feature elimination. Five models—Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), Random Forest, Feature Tokenizer Transformer (FT-Transformer), and Gated Adaptive Network for Deep Automated Learning of Features (GANDALF)—were assessed via 10–fold cross-validation. SHapley Additive exPlanations (SHAP) and restricted cubic splines (RCS) elucidated feature importance and nonlinear effects. Results: A total of 39,430 female MAFLD patients were included in the final analysis, among whom 611 (1.55%) developed incident depression over the five-year follow-up. Adjusted logistic regression identified younger age at first live birth (<20 years) and early menopause (25 kg/m2) and obesity (body mass index [BMI] >30 kg/m2), and early reproductive milestones. Additionally, our feature set showed enhanced predictive validity compared to established biomarker panels from prior studies (ROC-AUC 0.96 vs. 0.886–0.940). Conclusions: This study introduces a highly accurate, lightweight predictive model tailored for female MAFLD patients, leveraging 17 key features to improve the prediction of depression risk. By enabling personalized risk assessment and targeted interventions, our model offers a transformative approach to improve mental health outcomes and care quality in this vulnerable population.