Objective: To quantitatively assess the health risk of preservatives from beverages around primary schools in Anshun City, and to provide scientific basis for precise food safety supervision. Methods: From December 2023 to July 2024, 602 beverage samples were randomly collected from within 100 meters of 19 primary schools in Anshun City. The content of benzoic acid, sorbic acid, and dehydroacetic acid was detected according to GB 5009 series standards. Combined with children s physiological parameters (body weight 30 kg, daily intake 0.15 L), the Hazard Quotient (HQ) and Hazard Index (HI) models were used to evaluate health risks. Results: The total detection rate of preservatives from beverages around primary schools was 63.0%, and the total over limit rate was 9.0%. The detection rate of preservatives in flavored beverages was the highest (72.6%), and the highest over limit rate of preservatives in special purpose beverages was the highest (17.2%). The single preservative HQ (benzoic acid up to 0.47 ) and mixed HI (up to 0.55) of all samples were below 1(safety threshold). However, the HQ value of benzoic acid in flavored beverages (0.47) was 2.9 times that of sorbic acid (0.16), contributing significantly to health risk. Sensitivity analysis showed that if the daily consumption increased to 0.3 L, the HI value of flavored beverages would rise to 1.11, exceeding the safety threshold. Enterprise scale analysis showed that the exceedance rate of special purpose beverages in large enterprises reached 30.0%, while micro enterprises, accounting for a dominant market share (52.2%), constituted the main source of children s daily exposure to their products. Conclusions The overall health risk of perservatives in beverages sold near primary schools in Anshun City is controllable, but there is a noticeable risk of gradient. The risk of children’s exposure to preservatives through beverage consumption should not be ignored.

ObjectiveTo develop an assessment tool for apraxia of speech (AOS) of Chinese Putonghua speakers and test its reliability and validity. MethodsThe Chinese Apraxia of Speech Assessment and Diagnostic Tool (CAADT) was developed based on the Apraxia of Speech Rating Scale 3.5, combined with the linguistic characteristics of Chinese and clinical experience. The tool consistsed of eleven items across three sections: articulation, prosody and alternating motion rates. Six experts evaluated the content validity. From November, 2024 to May, 2025, 51 patients with post-stroke AOS (experimental group) and ten patients with post-stroke aphasia without AOS (control group) were recruited from Anhui Wannan Rehabilitation Hospital (the Fifth People's Hospital of Wuhu), and tested with CAADT. Reliability was assessed using Cronbach's α coefficient, Kendall's coefficient of concordance W and Pearson correlation coefficient. Validity was evaluated using the content validity index (CVI) and Spearman correlation coefficient. Discriminative effect was analyzed using the receiver operating characteristic (ROC) curve. ResultsThe Cronbach's α coefficients for the articulation and prosody sections and the total scale were all > 0.9, while it was 0.454 for the alternating motion rates. Inter-rater reliability was good (W ≥ 0.598, P < 0.001). Test-retest reliability showed high positive correlations for the three sections and the total score between the two assessments (r ≥ 0.84, P < 0.001). The scale-level CVI was 0.95, and the item-level CVI ≥ 0.83. The Spearman correlation coefficients among the sections ranged from 0.30 to 0.70. ROC analysis revealed an area under the curve of 0.953, with a cut-off value of 11, yielding a sensitivity of 0.92 and a specificity of 0.90. ConclusionCAADT demonstrates good reliability, validity and discriminative effect, which can be used for clinical assessment and auxiliary diagnosis of Chinese Putonghua speaking patients with post-stroke AOS.

ObjectiveTo investigate the T lymphocyte subset levels and viral loads in newly human immunodeficiency virus (HIV) antibody-confirmed positive cases in Suzhou (2021‒2024), and to analyze potential influencing factors by integrating their demographic characteristics, immune status, and viral replication patterns, thereby providing evidence for HIV/acquired immune deficiency syndrome (AIDS) prevention and control. MethodsPeripheral whole blood samples were collected from newly confirmed HIV-positive cases in Suzhou from 2021 to 2024. T lymphocyte subset analysis and viral load testing were performed, and influencing factors were identified in combination with demographic characteristics. Logistic regression models were employed to identify factors associated with CD4⁺T lymphocyte counts ≤350 cells·μL^-1, and Spearman’s rank correlation test was used to analyze the correlation between logarithmic value of viral load and CD4⁺/CD8⁺ ratio. ResultsAmong the 3 022 confirmed HIV-positive samples, the median CD4⁺T lymphocyte count was 298.00 cells·μL^-1, with 882 cases (29.19%) showing CD4⁺ T lymphocyte counts <200 cells·μL^-1. The median CD8⁺T lymphocyte count was 1 011.00 cells·μL^-1. The median CD4⁺/CD8⁺ ratio was 0.28, with 32.46% of cases exhibiting CD4⁺/CD8⁺ ratios <0.20, and there were statistically significant differences in CD4⁺/CD8⁺ ratio among different genders, age groups, marital status, and sample sources (all P<0.05). Multivariate logistic regression analyses indicated that individuals aged ≥20 years, those who were divorced or widowed, and cases identified through medical institutions had a significantly higher proportion of CD4⁺T lymphocyte counts ≤350 cells·µL⁻¹ compared to those aged <20 years, unmarried individuals, and cases sourced from voluntary counseling and testing (VCT) clinics, respectively. The mean logarithmic value of viral load was (4.29±1.15) copies·mL^-1. The logarithmic value of viral load demonstrated a significantly negative correlation with both CD4⁺/CD8⁺ ratio (r=-0.43, P<0.001) and CD4⁺T lymphocyte count (r=-0.37, P<0.001). ConclusionA substantial proportion of newly diagnosed HIV/AIDS cases in Suzhou are late presenters with high viral load levels. Targeted interventions should prioritize high-risk populations through enhanced active surveillance and the implementation of combined T lymphocyte subsets analysis and viral load testing, which can enable earlier case-finding and timely antiretroviral therapy initiation.

Background Lead (Pb) is a highly toxic heavy metal that accumulates in the body, potentially leading to multi-systemic impairment. Compared with traditional statistical methods, machine learning techniques offer unique advantages, opening new avenues for occupational health risk assessment and the exploratory analysis of complex associations. Objective To examine the association between occupational lead exposure and multiple health indicators and to identify key risk factors for lead toxicity. Methods A cross-sectional study was conducted, integrating occupational hygiene investigation results from 16 lead-acid battery enterprises in Jiangsu Province with occupational health examination data from 1914 lead-exposed workers. Inter-group differences were analyzed using the χ² test or Fisher's exact test. Binary logistic regression and machine learning algorithms [CatBoost, Naive Bayes model (NBM), and random forest (RF)] were employed to evaluate the association between blood lead (PbB), urine lead (PbU), and health indicators including blood pressure (BP), red blood cell count (RBC), and alanine aminotransferase (ALT). Results The prevalence of abnormal PbB and PbU were 14.52% and 9.35%, respectively. The risks of abnormal BP, RBC, and ALT were significantly increased in the population with high lead levels (P<0.05). PbB abnormalities were closely associated with gender, environmental lead concentration, wearing masks, smoking, and alcohol consumption (P<0.05). Regarding occupational hazards, workers exposed to lead dust had a 1.98-fold risk of PbU abnormality compared to those exposed to lead fumes. The plate coating and acid leaching process posed the highest risk for both PbB (OR=8.81) and PbU (OR=5.46) abnormalities compared with assembly process. Furthermore, the risks of PbB and PbU abnormalities were significantly elevated among workers with abnormal BP, RBC or ALT (P<0.05). Among the models, CatBoost performed best in predicting RBC abnormality (accuracy: 95.8%; precision: 44.9%; F1 score: 0.952; AUC: 0.981). Feature importance analysis identified PbB and PbU as the core factors affecting abnormal RBC and ALT, while RBC and ALT abnormalities as key features for predicting the risk of PbB and PbU abnormalities. Conclusion By integrating traditional statistical methods with machine learning, this study reveals a complex bidirectional association between occupational lead exposure and multiple health indicators, and identifies gender, job category, and environmental Pb concentration as the key factors influencing PbB abnormalities. These findings provide a scientific foundation for the implementation of precision occupational health management models.

Background Lead (Pb) is a highly toxic heavy metal that accumulates in the body, potentially leading to multi-systemic impairment. Compared with traditional statistical methods, machine learning techniques offer unique advantages, opening new avenues for occupational health risk assessment and the exploratory analysis of complex associations. Objective To examine the association between occupational lead exposure and multiple health indicators and to identify key risk factors for lead toxicity. Methods A cross-sectional study was conducted, integrating occupational hygiene investigation results from 16 lead-acid battery enterprises in Jiangsu Province with occupational health examination data from 1914 lead-exposed workers. Inter-group differences were analyzed using the χ² test or Fisher's exact test. Binary logistic regression and machine learning algorithms [CatBoost, Naive Bayes model (NBM), and random forest (RF)] were employed to evaluate the association between blood lead (PbB), urine lead (PbU), and health indicators including blood pressure (BP), red blood cell count (RBC), and alanine aminotransferase (ALT). Results The prevalence of abnormal PbB and PbU were 14.52% and 9.35%, respectively. The risks of abnormal BP, RBC, and ALT were significantly increased in the population with high lead levels (P<0.05). PbB abnormalities were closely associated with gender, environmental lead concentration, wearing masks, smoking, and alcohol consumption (P<0.05). Regarding occupational hazards, workers exposed to lead dust had a 1.98-fold risk of PbU abnormality compared to those exposed to lead fumes. The plate coating and acid leaching process posed the highest risk for both PbB (OR=8.81) and PbU (OR=5.46) abnormalities compared with assembly process. Furthermore, the risks of PbB and PbU abnormalities were significantly elevated among workers with abnormal BP, RBC or ALT (P<0.05). Among the models, CatBoost performed best in predicting RBC abnormality (accuracy: 95.8%; precision: 44.9%; F1 score: 0.952; AUC: 0.981). Feature importance analysis identified PbB and PbU as the core factors affecting abnormal RBC and ALT, while RBC and ALT abnormalities as key features for predicting the risk of PbB and PbU abnormalities. Conclusion By integrating traditional statistical methods with machine learning, this study reveals a complex bidirectional association between occupational lead exposure and multiple health indicators, and identifies gender, job category, and environmental Pb concentration as the key factors influencing PbB abnormalities. These findings provide a scientific foundation for the implementation of precision occupational health management models.

To summarize the clinical characteristics and genetic mutation spectrum of patients with SELENON-related myopathy(SELENON-RM), in order to improve awareness among physicians.

Coronavirus disease 2019 (COVID-19) is an acute infectious respiratory disease that has been prevalent since December 2019. Chinese medicine (CM) has demonstrated its unique advantages in the fight against COVID-19 in the areas of disease prevention, improvement of clinical symptoms, and control of disease progression. This review summarized the relevant material components of CM in the treatment of COVID-19 by searching the relevant literature and reports on CM in the treatment of COVID-19 and combining with the physiological and pathological characteristics of the novel coronavirus. On the basis of sorting out experimental methods in vivo and in vitro, the mechanism of herb action was further clarified in terms of inhibiting virus invasion and replication and improving related complications. The aim of the article is to explore the strengths and characteristics of CM in the treatment of COVID-19, and to provide a basis for the research and scientific, standardized treatment of COVID-19 with CM.
Humans ; Drugs, Chinese Herbal/pharmacology* ; COVID-19 Drug Treatment ; SARS-CoV-2/drug effects* ; COVID-19/therapy* ; Medicine, Chinese Traditional/methods* ; Antiviral Agents/pharmacology* ; Animals

Osteoarthritis (OA) causes chronic pain that significantly impairs quality of life, with current treatments often proving insufficient and accompanied by adverse effects. Recent research has identified the dorsal root ganglion (DRG) and its resident macrophages as crucial mediators of chronic OA pain through neuroinflammation driven by macrophage polarization. We present a novel injectable thermo-sensitive hydrogel system, KAF@PLEL, designed to deliver an anti-inflammatory peptide (KAF) specifically to the DRG. This biodegradable hydrogel enables sustained KAF release, promoting the reprogramming of DRG macrophages from pro-inflammatory to anti-inflammatory phenotypes. Through comprehensive in vitro and in vivo studies, we evaluated the hydrogel's biocompatibility, effects on macrophage polarization, and therapeutic efficacy in chronic OA pain management. The system demonstrated significant capabilities in preserving macrophage mitochondrial function, suppressing neuroinflammation, alleviating chronic OA pain, reducing cartilage degradation, and improving motor function in OA rat models. The sustained-release properties of KAF@PLEL enabled prolonged therapeutic effects while minimizing systemic exposure and side effects. These findings suggest that KAF@PLEL represents a promising therapeutic approach for improving outcomes in OA patients through targeted, sustained treatment.

Vascular damage plays a significant role in the onset and progression of Alzheimer's disease (AD). However, the precise molecular mechanisms underlying the induction of neuronal injury by vascular damage remain unclear. The present study aimed to examine the impact of fibrinogen (Fg) on tau pathology. The results showed that Fg deposits in the brains of tau P301S transgenic mice interact with tau, enhancing the cytotoxicity of pathological tau aggregates and promoting tau phosphorylation and aggregation. Notably, Fg-modified tau fibrils caused enhanced neuronal apoptosis and synaptic damage compared to unmodified fibrils. Furthermore, intrahippocampal injection of Fg-modified tau fibrils worsened the tau pathology, neuroinflammation, synaptic damage, neuronal apoptosis, and cognitive dysfunction in tau P301S mice compared to controls. The present study provides compelling evidence linking Fg and tau, thereby connecting cerebrovascular damage to tau pathology in AD. Consequently, inhibiting Fg-mediated tau pathology could potentially impede the progression of AD.
Animals ; tau Proteins/metabolism* ; Alzheimer Disease/metabolism* ; Fibrinogen/metabolism* ; Mice, Transgenic ; Mice ; Disease Models, Animal ; Memory Disorders/metabolism* ; Male ; Mice, Inbred C57BL ; Brain/metabolism* ; Hippocampus/metabolism* ; Protein Aggregation, Pathological/metabolism* ; Apoptosis ; Phosphorylation

Advancements in artificial intelligence (AI) and emerging technologies are rapidly expanding the exploration of chemical space, facilitating innovative drug discovery. However, the transformation of novel compounds into safe and effective drugs remains a lengthy, high-risk, and costly process. Comprehensive early-stage evaluation is essential for reducing costs and improving the success rate of drug development. Despite this need, no comprehensive tool currently supports systematic evaluation and efficient screening. Here, we present druglikeFilter, a deep learning-based framework designed to assess drug-likeness across four critical dimensions: 1) physicochemical rule evaluated by systematic determination, 2) toxicity alert investigated from multiple perspectives, 3) binding affinity measured by dual-path analysis, and 4) compound synthesizability assessed by retro-route prediction. By enabling automated, multidimensional filtering of compound libraries, druglikeFilter not only streamlines the drug development process but also plays a crucial role in advancing research efforts towards viable drug candidates, which can be freely accessed at https://idrblab.org/drugfilter/.