Background The prevalence of chronic diseases among the Chinese occupational population is rising steadily, with hypertension and diabetes becoming important health concerns. Occupational health examinations (OHE) provide stable population coverage, standardized protocols, and fixed follow-up intervals, offering a robust data foundation for risk assessment. However, most existing hypertension prediction studies rely on cross-sectional data and mainly focus on clinic onset, failing to capture the dynamic progression and cumulation of individual risk. Objective To construct a machine learning-based risk prediction model for blood pressure abnormality in occupational populations, providing a reference for health risk stratification and targeted health interventions. Methods Longitudinal data from 2020 to 2023 were extracted from the occupational health examination database of an institution in Shanghai. After excluding individuals with hypertension in any of the first three years, 3710 workers who participated 4 consecutive years of OHE were included. Six categories of blood pressure-related indicators from 2020 to 2022 were selected, including basic information, lifestyles, occupational exposures, cardiovascular indicators, routine blood tests, and biochemical markers, comprising 30 variables in total. For feature engineering, continuous variables were processed to reflect three-year dynamic characteristics: level (mean), variability (variance), and temporal trend (slope). Categorical variables were incorporated using 2022 values. Least absolute shrinkage and selection operator regression was applied to identify variables for inclusion in the machine learning models. Five models (decision tree, logistic regression, random forest, support vector machine, and XGBoost) were employed to predict blood pressure abnormality risk in 2023. And then sensitive analysis was conducted. The optimal model was selected based on the area under the curve (AUC) of receiver operating characteristic. Feature importance and SHAP analyses were applied to interpret the final model. Results Feature engineering transformed 17 continuous variables into 51 secondary variables, combined with 13 baseline variables, resulted in 24 variables screened out by LASSO regression. The logistic regression model achieved the best performance. Feature importance analysis indicated that the dynamic trajectory of blood pressure played a central role in the model, complemented by other biochemical and lifestyle indicators. SHAP analysis further showed that the model’s ability to not only identify risks of high-normal blood pressure and hypertension but also quantify the specific contribution of individual examination indicators to predicted risk, supporting risk-stratified population management in occupational health management. Conclusion Based on an existing occupational health examination database and routinely collected data, this study has developed a risk prediction model for blood pressure abnormalities in occupational populations. By incorporating dynamic change characteristics from longitudinal examinations, the study demonstrates the feasibility of using routine health data for early risk identification and stratified management. However, its applicability is limited by the availability of repeated examination data and the focus on specific occupational groups, requiring further validation across broader populations and diverse clinic scenarios.

As a primary approach to safeguarding workers' occupational health in China, the work-related injury insurance still has insufficient preventive measures for occupational groups. By reviewing the preventive measures embedded in Germany’s statutory accident insurance (Gesetzliche Unfallversicherung) and Japan’s workers’ accident compensation insurance, this study summarized their best practices and then proposed the following policy directions based on China’s specific context: strengthening the prevention-oriented institutional framework, increasing financial investment in occupational injury prevention, improving the incentive mechanisms of floating premium rate, and promoting data integration and intelligent applications in occupational health management. These measures are expected to shift China’s work-related injury insurance system from a compensation-oriented model to one that emphasizes comprehensive prevention and health promotion, thereby providing useful insights for better safeguarding the health of Chinese workers.

Background With the progression of industrialization, an increasing number of emerging contaminants are entering aquatic environments, posing significant threats to the safety of drinking water. Therefore, establishing a system for identifying unknown hazardous factors and implementing safety warning mechanisms for drinking water is of paramount importance. Among these efforts, non-target screening plays a critical role, but its effectiveness is largely constrained by the scope of coverage of sample pre-treatment methods. Objective To integrate modern chromatography/mass spectrometry techniques with advanced data mining methods to develop a non-discriminatory sample pre-treatment method for comprehensive enrichment of unknown contaminants in drinking water, laying a technical foundation for the discovery and identification of unknown organic hazardous factors in drinking water. Methods A non-discriminatory pre-treatment method based on supramolecular and solid-phase extraction was developed. The final target compounds including 333 pesticides, 194 pharmaceuticals and personal care products (PPCPs), and 59 per- and polyfluoroalkyl substances (PFASs) were used for optimizing the pre-treatment method, confirming its coverage. The impacts of different eluents on the absolute recovery rates of target compounds were compared to select the conditions with the highest recovery for sample pre-treatment. The effects of different supramolecular solvents and salt concentrations on target compound recovery were also evaluated to determine the most suitable solvent and salt concentration. Results The solid-phase extraction elution solvents, supramolecular extraction solvents, and salt concentrations were optimized based on the target compound recovery rates. The optimal recovery conditions were achieved using 2 mL methanol, 2 mL methanol (containing 1% formic acid), 2 mL ethyl acetate, 2 mL dichloromethane, hexanediol supramolecular solvent, and 426 mg salt. The detection method developed based on these conditions showed a good linear relationship for all target compounds in the range of 0.1-100.0 ng·mL⁻¹, with R² > 0.99. The method’s limit of detection ranged from 0.01 ng⁻¹ to 0.95 ng⁻¹, and 95% of target compounds were recovered in the range of 20%-120%, with relative standard deviation (RSD) less than 30%, indicating good precision. Conclusion The combined pre-treatment method of solid-phase extraction and supramolecular solvent extraction can effectively enrich contaminants in drinking water across low, medium, and high polarities, enabling broad-spectrum enrichment of diverse trace contaminants in drinking water. It provides technical support for broad-spectrum, high-throughput screening and identification of organic pollutants in drinking water, and also serves as a reference for establishing urban drinking water public safety warning systems.

As the prevalence of chronic diseases increases among occupational populations in China, the focus of occupational health management has shifted from ″occupational disease prevention and control″ to ″occupational disease and chronic disease prevention and control″. At present, occupational medical examinations (OME) are mainly focused on occupational disease prevention and control, and generally lack of chronic disease indicators such as blood lipids and blood glucose, limiting the role of OME in life-cycle health management of workers. To address this gap, it is necessary to incorporate chronic disease screening indicators into OME. Integrating OME and chronic disease screening faces bottlenecks, including concerns about increased operating costs in small- and medium-sized enterprises, and the risk of employment discrimination caused by the leakage of employee health privacy. A tiered strategy is recommended. Incorporating low-cost indicators such as blood lipids, waist circumference into routine examinations, and customizing advanced screening for elder or high-risk position workers. The prioritization of screening indicators can be determined by expert consultation and employer-employee discussions. Meanwhile, the results of chronic disease screening indicators should not be included in occupational health surveillance records to protect workers′ privacy. In summary, expanding OME to cover more chronic disease indicators provides valuable economic and social benefits. In the long run, common chronic disease screening indicators should be included in the regular medical examination indicators of OME, thereby reducing the burden of chronic diseases on occupational populations and promoting the health management of occupational populations.

Background Chlorinated perfluoroalkyl ether sulfonate Cl-PFAES, trade name F-53B, a novel per- and polyfluoroalkyl substance (PFAS), has been shown to induce multi-organ toxicity in humans and cross the blood-brain barrier. However, its toxic effects and underlying mechanisms on neural stem cells (NSCs) remain unclear. Objective To investigate the impact of F-53B on NSCs proliferation and differentiation through oxidative stress and explore its potential molecular mechanisms in associations with mitochondrial function damage and the expression of autophagy-related gene (PINK1/Parkin). Methods Primary NSCs isolated from neonatal C57BL/6 mice were used as a model and exposed to F-53B at concentrations of 0, 33, or 100 μmol·L⁻¹ for 24 h. Cell viability was assessed using the cell counting kit-8 (CCK-8) assay, while proliferation was evaluated by the 5-ethynyl-2’-deoxyuridine (EdU) incorporation assay. Immunofluorescence staining was performed to observe differentiation phenotypes. Intracellular and mitochondrial reactive oxygen species (ROS) levels were quantified using dihydroethidium (DHE) and MitoSOX probes, respectively. Mitochondrial morphology was observed using MitoTracker Green. ATP level was measured with a commercial kit. Additionally, real-time quantitative polymerase chain reaction (qPCR) was conducted to quantify the expression of PINK1 and Parkin genes. Results Exposure to 100 μmol·L⁻¹ F-53B significantly reduced cell viability to 93.6% of the control group (P<0.01), and decreased the proportion of EdU⁺ cells (P<0.01), indicating proliferation inhibition. The differentiation analysis showed a reduction in neuronal generation, axonal shortening, and an increase in astrocytes. The 100 μmol·L⁻¹ F-53B exposure elevated intracellular ROS to 122% (P<0.01) and mitochondrial ROS (MitoROS) to 135% (P<0.001) of the control levels, leading to mitochondrial fragmentation. The ATP levels after the F-53B exposure decreased to 62.4% relative to the control group (P<0.001). Furthermore, the mRNA expression levels of PINK1 and Par after the F-53B exposure were notably reduced (P<0.05). Conclusion F-53B may induce oxidative stress, thereby disrupting mitochondrial morphology and function while inhibiting the PINK1/Parkin-mediated mitophagy pathway, ultimately leading to impaired neural stem cell proliferation and abnormal differentiation. This study provides new insights into the neurotoxicity mechanisms of F-53B.

ObjectiveTo investigate the mechanisms by which the combination of berberine （BBR） and baicalin （BAI） ameliorates type 2 diabetes mellitus （T2DM） due to internal accumulation of dampness-heat from the perspectives of gut microbiota and metabolomics. MethodsAntibiotics were used to induce pseudo-sterile mice. Thirty pseudo-sterile mice were randomized into a normal fecal microbiota transplantation group （n=10） and a T2DM （syndrome of internal accumulation of dampness-heat） fecal microbiota transplantation group （n=20）. The mice were then administrated with suspensions of fecal microbiota from healthy volunteers and a patient with T2DM due to internal accumulation of dampness-heat by gavage， respectively. Each mouse received 200 µL suspension every other day for a total of 15 times to reshape the gut microbiota. The T2DM model mice were then assigned into a model group （n=8） and a BBR-BAI group （n=11）. BBR was administrated at a dose of 200 mg·kg^-1， and BAI was administrated in a ratio of BBR-BAI 10∶1 based on preliminary research findings. The administration lasted for 8 consecutive weeks. Fasting blood glucose （FBG）， glycated hemoglobin （HbA1c）， insulin （INS）， triglycerides （TG）， total cholesterol （CHOL）， low-density lipoprotein cholesterol （LDL-C）， high-density lipoprotein cholesterol （HDL-C）， aspartate aminotransferase （AST）， and alanine aminotransferase （ALT） levels were measured to evaluate the effects of the BBR-BAI combination on glucose and lipid metabolism and liver function in T2DM mice. Hematoxylin-eosin staining was employed to observe pathological changes in the colon tissue. The expression of claudin-1， zonula occludens-1 （ZO-1）， and occludin in the colon tissue was determined by Western blot. Real-time quantitative polymerase chain reaction（Real-time PCR） was employed to assess the levels of tumor necrosis factor-α （TNF-α）， interleukin-1β （IL-1β）， and interleukin-6 （IL-6） in the colon tissue. The fecal microbiota composition and differential metabolites were analyzed by 16S rRNA sequencing and ultra-high performance liquid chromatography-quadrupole-time of flight tandem mass spectrometry （UPLC-Q-TOF-MS）， respectively. ResultsThe BBR-BAI combination lowered the FBG， HbA1c， and INS levels （P<0.05， P<0.01） and alleviated insulin resistance （P<0.01） in T2DM mice. Additionally， BBR-BAI elevated the levels of ZO-1， occludin， and claudin-1 （P<0.05， P<0.01） and down-regulated the expression levels of TNF-α， IL-1β， and IL-6 in the colon （P<0.05， P<0.01）. The results of 16S rRNA sequencing showed that BBR-BAI increased the relative abundance of Ligilactobacillus， Phascolarctobacterium， and Akkermansia （P<0.05）， while significantly decreasing the relative abundance of Alistipes， Odoribacter， and Colidextribacter （P<0.05）. UPLC-Q-TOF-MS identified 28 differential metabolites， which were primarily involved in arachidonic acid metabolism and α-linolenic acid metabolism. ConclusionBBR-BAI can ameliorate T2DM due to internal accumulation of dampness-heat by modulating the relative abundance of various bacterial genera in the gut microbiota and the expression of fecal metabolites.

ObjectiveTo explore the mechanism of action of Wendantang on ApoE^-/- hyperlipidemic mice using non-targeted metabolomics technology. MethodsMale C57BL/6J mice served as the normal control group （n=6）， and they were fed with regular chow， while male ApoE^-/- mice constituted the high-fat group （n=30）， and they were fed with a 60% high-fat diet. After 11 weeks of model establishment， the mice in the high-fat group were randomly divided into the model group， simvastatin group （3.3 mg·kg^-1）， and high-dose， medium-dose， and low-dose groups of Wendantang （26， 13， 6.5 g·kg^-1， respectively， in terms of crude drug amount）， with six mice in each group. The normal control group and the model group were gavaged with an equivalent volume of normal saline， and all groups continued to be fed their respective diets， receiving daily medication for 10 weeks with weekly body weight measurements. Serum levels of total cholesterol （TC）， triglycerides （TG）， high-density lipoprotein cholesterol （HDL-C）， low-density lipoprotein cholesterol （LDL-C）， free fatty acids （NEFA）， blood glucose （GLU）， alanine aminotransferase （ALT）， and aspartate aminotransferase （AST） were detected in the mice. Pathological changes in liver tissue were observed using hematoxylin-eosin （HE） staining， and ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry （UPLC-Q-TOF-MS/MS） was employed for metabolomic analysis of mouse liver tissue. ResultsCompared to the normal control group， the model group exhibited significantly increased body weight， blood lipid levels， and liver function （P<0.05， P<0.01）， with disordered liver tissue structure， swollen hepatocytes， and accompanying vacuolar fatty degeneration and inflammatory cell infiltration. Compared to the model group， the simvastatin group and Wendantang groups showed significantly reduced body weight， TG， NEFA， GLU， ALT， and AST levels （P<0.05， P<0.01）， with a significant increase in HDL-C levels （P<0.05， P<0.01）， demonstrating a dose-dependent effect. The lesion of the liver tissue section was obviously improved after administration， tending towards a normal liver tissue morphology. Analysis of liver metabolites revealed 86 differential metabolites between the normal control group and the model group， with the high-dose group of Wendantang able to regulate 56 of these metabolites. Twenty-two differential metabolites associated with hyperlipidemia were identified， mainly including chenodeoxycholic acid， hyocholic acid， taurine， glycocholic acid， dihydroceramide， hydroxy sphingomyelin C14∶1， arachidonic acid， and linoleic acid， enriching 22 metabolic pathways， with 4 being the most significant （P<0.05）， namely primary bile acid biosynthesis， sphingolipid metabolism， unsaturated fatty acid biosynthesis， and linoleic acid metabolism pathways. ConclusionWendantang can improve blood lipid levels and liver function in ApoE^-/- hyperlipidemic mice， which may be related to the regulation of primary bile acid biosynthesis， sphingolipid metabolism， unsaturated fatty acid biosynthesis， and linoleic acid metabolism pathways.

Objective To investigate the impact of Toxoplasma gondii type I, II and III rhoptry protein 16 (ROP16) on programmed cell death ligand 1 (PD-L1) expression in lung adenocarcinoma cells, and to examine the effects of T. gondii type I ROP16 protein on the relative PD-L1 expression, the relative PD-L1 distribution on the cell membrane surface, and the binding of programmed cell death 1 (PD-1) to PD-L1 in lung adenocarcinoma cells. Methods Lentiviral vectors overexpressing T. gondii type I, II and III ROP16 proteins were generated, and transfected into the human lung adenocarcinoma A549 cell line. A549 cells were used as a blank control group, and A549 cells transfected with an empty lentiviral expression vector were used as a negative control group, while A549 cells transfected with lentiviral vectors overexpressing T. gondii type I, II and III ROP16 proteins served as experimental groups. Stably transfected cells were selected with puromycin and verified using Western blotting, quantitative real-time PCR (RT-qPCR), and immunofluorescence assays. The PD-L1 expression was quantified at translational and transcriptional levels using Western blotting and RT-qPCR assays in A549 cells in the five groups, and the relative PD-L1 distribution was detected on the A549 cell membrane surface using flow cytometry. In addition, the effect of T. gondii type I ROP16 protein on the PD-1/PD-L1 binding was measured in A549 cells using enzyme-linked immunosorbent assay (ELISA). Results The relative ROP16 protein expression was 0, 0, 1.546 ± 0.091, 1.822 ± 0.047 and 2.334 ± 0.089 in the blank control group, negative control group, and the T. gondii type I, II and III ROP16 protein overexpression groups (F = 1 339.00,P < 0.001), and the relative ROP16 mRNA expression was 2.153 ± 0.949, 2.436 ± 1.614, 14.343 ± 0.020, 12.577 ± 0.285 and 15.090 ± 0.420 in the blank control group, negative control group and the T. gondii type I, II and III ROP16 protein overexpression groups, respectively (F = 483.50,P < 0.001). The ROP16 expression was higher in the T. gondii type I, II and III ROP16 protein overexpression groups than in the blank control group at both translational and transcriptional levels (allP values < 0.001). Immunofluorescence assay revealed that T. gondii type I, II and III ROP16 proteins were predominantly localized in A549 cell nuclei. Western blotting showed that the relative PD-L1 protein expression was 0.685 ± 0.109, 0.589 ± 0.114, 1.007 ± 0.117, 0.572 ± 0.151, and 0.426 ± 0.116 in the blank control group, negative control group, and the T. gondii type I, II and III ROP16 protein overexpression groups (F = 9.46,P < 0.05), and RT-qPCR assay quantified that the relative PD-L1 mRNA expression was 1.012 ± 0.190, 1.281 ± 0.465, 1.950 ± 0.175, 0.889 ± 0.251, and 0.230 ± 0.192 in the blank control group, negative control group, and the T. gondii type I, II and III ROP16 protein overexpression groups (F = 14.18,P < 0.05). The PD-L1 expression was higher in the T. gondii type IROP16 protein overexpression group than in the blank control group at both translational and transcriptional levels (both P values < 0.05). Flow cytometry detected that the relative distributions of PD-L1 protein were (10.83 ± 0.60)%, (11.23 ± 0.20)%, and (14.61 ± 0.50)% on the A549 cell membrane surface (F = 28.31, P < 0.05), and the relative distribution of PD-L1 protein was higher in the T. gondii type IROP16 protein overexpression group than in the blank control group and negative control group (both P values < 0.001). ELISA measured significant differences in the absorbance (A) value among the T. gondii type IROP16 protein overexpression group, the blank control group and the negative control group if the concentrations of the recombinant PD-1 protein were 0.04 (F = 10.45, P < 0.05), 0.08 μg/mL (F = 11.68, P < 0.05) and 0.12 μg/mL (F = 52.68, P < 0.05), and the A value was higher in the T. gondii type IROP16 protein overexpression group than in the blank control group and the negative control group (both P values < 0.05), indicating that T. gondii type IROP16 protein promoted the PD-L1/PD-1 binding in A549 cells in a concentration-dose manner. Conclusions T. gondii type IROP16 protein overexpression may up-regulate PD-L1 expression in A549 cells at both transcriptional and translational levels and the relative PD-L1 distribution on the A549 cell membrane surface, and affect the PD-1/PD-L1 binding in a concentration-dependent manner.

The core idea of occupational health risk assessment models is to systematically evaluate occupational health risks according to target hazard characteristics and relevant exposure levels of workers. Occupational exposure assessment is based on concentration, frequency, exposure time, and other indicators that indicate actual exposure of workers to occupational hazards, which is a critical component of health risk assessment. However, the accuracy and comparability of assessment results are affected by differences in parameter assignment for exposure assessment across different studies, as well as insufficient emphasis on multiple occupational hazard exposure. This review aimed to explore the assignment and standardization of exposure assessment parameters for occupational health risk assessment modeling, and systematically sorted out the meaning, assignment methods, and sources of exposure assessment related parameters in commonly used occupational health risk assessment models, with the goal of providing researchers with standardized assessment tools to enhance the scientific rigor and practicality of occupational health risk assessments. Considering the individual differences and temporal fluctuations in occupational exposure, it is recommended that researchers should adopt appropriate sampling strategies, reasonably select sample subjects and time based on the division of similar exposure group (SEG), and conduct statistical inference on the obtained data to derive representative exposure parameters. For combined exposure to chemicals with similar toxic effects, the health risk assessment methods are relatively mature. However, the assessment of combined exposure to hazards with different properties and health effects still lacks scientific authority and needs further research and discussion.

The Pharmacopeia of the People’s Republic of China 2025 Edition (referred to as the Chinese Pharmacopoeia 2025 Edition, ChP 2025) will be promulgated and implemented. This article introduces the process of development of ChP 2025 Edition (Volume Ⅱ), including the selection, the revision of general notices,the addition and revision of drug monographs, etc., and provides some analysis and examples to illustrate,which can facilitate the readers to understand and implement the ChP 2025 Edition (Volume Ⅱ).