Background Toluene and chlorobenzene have been designated as surrogate contaminants in the challenge test for evaluating the safety of recycling processes for food-contact recycled polyethylene terephthalate (rPET). Establishing a reliable analytical method is essential for ensuring the compliant use of rPET and safeguarding food safety. Objective To develop a rapid quantitative method for determining toluene and chlorobenzene in rPET using headspace gas chromatography-mass spectrometry (HS-GC-MS), as part of the challenge test for process safety evaluation. Methods The effects of different chromatographic columns and headspace conditions on detection of target analytes were investigated. Three columns HP-5 ms UI (30 m×0.25 mm×0.25 μm), DB-624 (30 m×0.32 mm×1.8 μm), and VF-WAXms (30 m×0.25 mm×0.25 μm) were compared for separation efficiency and peak shape. Headspace equilibration temperatures (50-100 ℃) and equilibration times (10-30 min) were evaluated to determine the optimal instrumental parameters. The effect of sample grinding on recovery was assessed to select the best pretreatment conditions. The established method was validated for selectivity, linearity, sensitivity, accuracy, and precision, and was subsequently applied to the analysis of 12 rPET samples. Results The target analytes achieved good separation and response within 15 min, under the optimized conditions using an HP-5 ms UI column, a headspace equilibration temperature of 60 ℃ and a 10 min equilibration time. Direct analysis without grinding yielded satisfactory recovery rates. Toluene and chlorobenzene showed excellent linearity (0.0030-5.0 mg·kg⁻¹, R²≥0.999). The limits of detection (LOD) and quantification (LOQ) were 0.00003 mg·kg⁻¹ and 0.00011 mg·kg⁻¹ for toluene respectively, and 0.00011 mg·kg⁻¹ and 0.00037 mg·kg⁻¹ for chlorobenzene respectively. The recoveries of the matrix spike at low and high spiking levels ranged from 88.6% to 110%, with relative standard deviations of 0.52%-4.9% (n=6). Among the 12 rPET samples from different recycling stages, three samples contained detectable levels of toluene (0.196-0.250 mg·kg⁻¹) and chlorobenzene (0.704-0.867 mg·kg⁻¹). Conclusion The proposed method is simple, rapid, highly sensitive, and accurate, making it suitable for determining toluene and chlorobenzene in food-contact rPET. It provides a robust technical approach for the safety evaluation of rPET recycling processes.

Background Long working hours, as a common risk factor for occupational stress, is closely related to the occurrence of depressive symptoms. Understanding how long working hours affect occupational stress and depressive symptoms will inform occupational health interventions. Objective To quantify the impact of long working hours exposure on occupational stress and depressive symptoms among Internet industry employees, translate black-box outputs into actionable insights, and demonstrate the value of interpretable machine learning for early-warning occupational-health surveillance. Methods A dataset was derived from a cross-sectional survey involving 2866 internet industry employees in China. This survey was part of the project Risk Assessment Of Long Working Hour Exposure And Its Adverse Health Effects, conducted by the National Institute for Occupational Health and Poisoning Control, Chinese Center for Disease Control and Prevention, from 2021 to 2023. Working hours, occupational stress and depressive symptoms were quantified with a set of structured questionnaires including the Core Occupational Stress Scale and the Patient Health Questionnaire. Pairwise associations were screened by Mantel tests and variance-inflation factors. Key predictors identified through feature selection were fed into six machine-learning risk-prediction models. Visual interpretation was provided by feature importance, Shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME), while directed causal effects and intervention impacts of prolonged working hours exposure on occupational stress and depressive symptoms were dissected with causal explanation of features techniques. Results The positive rates of occupational stress and depressive symptoms among internet employees were 12.9% and 77.8% respectively. Twelve core features for occupational stress and nine for depressive symptoms were retained after selection. After these features were supplied to six predictive algorithms and evaluated on five metrics, the Light Gradient Boosting Machine (LGBM) achieved the highest accuracy—0.89 for occupational stress and 0.79 for depressive symptoms on the hold-out test set. The feature-importance rankings converged on fatigue accumulation and life satisfaction as dominant drivers for both outcomes, whereas weekly working hours and daily overtime emerged as the principal exposure-related predictors. The SHAP summary plots revealed that longer weekly hours and daily overtime systematically elevated the probability of occupational stress. The causal feature explanation further quantified that ascending one category in weekly working hours increased the probability of occupational stress by 7.04%. Conclusion Exposure to long working hours is associated with both occupational stress and depressive symptoms among internet industry employees. Interpretable machine-learning frameworks translate these associations into transparent, defensible drivers, enabling precise identification of the pivotal factors and their interplay. This evidence base equips occupational-health practitioners with actionable insights for designing targeted prevention and intervention strategies.

ObjectiveTo investigate the effect and underlying mechanism of the Wulao Qisun prescription on pathological new bone formation in ankylosing spondylitis （AS）. MethodsSynovial fibroblasts were isolated from the hip joints of AS patients and observed under a microscope to assess cell morphology. The cells were identified using immunofluorescence staining. The isolated AS fibroblasts were divided into blank group， low drug-containing serum group， medium drug-containing serum group， high drug-containing serum group， and positive drug group. After drug intervention， cell proliferation was measured using the cell counting kit-8 （CCK-8） assay to observe fibroblast growth and determine the optimal intervention time. Alkaline phosphatase （ALP） activity was measured using the alkaline phosphatase assay. Protein expression of osteocalcin （OCN）， osteopontin （OPN）， and runt-related transcription factor 2 （Runx2） was detected by Western blot. The mRNA expression levels of Wnt5a， β-catenin， and Dickkopf-1 （DKK-1） were measured by real-time quantitative polymerase chain reaction （Real-time PCR）. ResultsCompared with the blank group， each drug-containing serum group of Wulao Qisun prescription and the positive drug group inhibited the proliferation of AS fibroblasts and reduced ALP expression （P<0.01）. Compared with the blank group， the low drug-containing serum group of Wulao Qisun prescription downregulated β-catenin mRNA expression （P<0.05）. The medium and high drug-containing serum groups and the positive drug group significantly downregulated Wnt5a and β-catenin mRNA expression （P<0.05， P<0.01）， with the positive drug group showing the most pronounced effect （P<0.01）. The high drug-containing serum group and the positive drug group significantly upregulated DKK-1 mRNA expression （P<0.01）. Compared with the blank group， the low drug-containing serum group of Wulao Qisun prescription inhibited the expression of OPN and Runx2 proteins （P<0.05， P<0.01）， while the medium and high drug-containing serum groups and the positive drug group inhibited the expression of OCN， OPN， and Runx2 proteins （P<0.05， P<0.01）. ConclusionThe Wulao Qisun prescription can inhibit the proliferation and osteogenic differentiation of AS fibroblasts， thereby delaying the formation of pathological new bone in AS. The possible mechanism involves the regulation of Wnt/β-catenin-related gene expression， further inhibiting the transcription of downstream target genes.

Background Occupational stress has emerged as a critical public health concern affecting the physical and mental well-being of workers in the manufacturing sector. However, researchers typically evaluate its core driver—cumulative fatigue—using a crude binary “present/absent” variable, thereby overlooking the high-dimensional complexity and heterogeneity inherent in fatigue characteristics. This oversimplification constrains both the precision and predictive performance of occupational stress risk assessment model. Objective Leveraging a data-driven approach, to survey data on cumulative fatigue among manufacturing employees, and then use this new classification to develop and validate an occupational stress prediction model, with an ultimate aim of enhancing the accuracy and effectiveness of occupational stress assessment. Methods A set of cross-sectional survey data on 3871 manufacturing employees in 2021 were derived from the “Long working hours exposure and its adverse health effect risk assessment” program of the National Institute for Occupational Health and Poison Control, Chinese Center for Disease Control and Prevention. Occupational stress was assessed using the Core Occupational Stress Measurement Scale, while cumulative fatigue was evaluated via the Worker’s Self-Diagnostic Questionnaire for Fatigue Accumulation. Boruta method was applied to screen core variables from 20 occupational stress influencing factors. Dimensionality reduction of cumulative fatigue features was performed using a Bayesian sparse autoencoder (BASE), followed by comparison and application of clustering methods to achieve multi-class classification of the reduced features. Six machine learning classification models were then selected including logistic regression, support vector machine, decision tree, random forest, adaptive boosting, and lightweight gradient boosting machine (LightGBM) to construct and compare two occupational stress prediction models involving cumulative fatigue combined with ten other core variables: one utilizing the original binary cumulative fatigue label as a core variable, and another employing the novel fatigue classification proposed herein as a core variable. Results The positive rate of occupational stress among the manufacturing employees was 38.9%. The Boruta method identified 11 core variables of occupational stress: depressive symptoms, cumulative fatigue, age, length of service, tenure in current position, average weekly working hours, average daily overtime hours, average monthly income, low levels of exercise, life satisfaction, and sleep quality. The BSAE reduced the original cumulative fatigue factors to 12 dimensions, while the K-means clustering grouped the reduced fatigue features into three categories: no fatigue, moderate fatigue, and sever fatigue. Six occupational stress prediction models were constructed using the three-category labels of cumulative fatigue and ten additional factors. The results indicated that the LightGBM model performed best, achieving an area under the curve (AUC) of 0.78, an accuracy of 0.77, and an F1 score of 0.72. Compared with the model incorporating the traditional binary labels for cumulative fatigue, the best prediction AUC (0.72), accuracy (0.66), and F1 score (0.65) improved by 6%, 11%, and 7%, respectively. Conclusion Cumulative fatigue is a significant predictor of occupational stress among manufacturing employees. Applying data dimensionality reduction combined with three-class cluster analysis to characterize cumulative fatigue improves the performance of occupational stress prediction models. From a data-driven perspective, machine learning methods demonstrate strengths in processing complex datasets, capturing nonlinear relationships, and generating predictions based on key variables. This study therefore confirms that refined processing of core factors substantially enhances the predictive capability of machine learning models in assessing occupational stress risk in the manufacturing workforce.

Objective To investigate the distribution and antimicrobial resistance profiles of clinically isolated Haemophilus influenzae and Moraxella catarrhalis in hospitals across China from 2015 to 2021,and provide evidence for rational use of antimicrobial agents.Methods Data of H.influenzae and M.catarrhalis strains isolated from 2015 to 2021 in CHINET program were collected for analysis,and antimicrobial susceptibility testing was performed by disc diffusion method or automated systems according to the uniform protocol of CHINET.The results were interpreted according to the CLSI breakpoints in 2022.Beta-lactamases was detected by using nitrocefin disk.Results From 2015 to 2021,a total of 43 642 strains of Haemophilus species were isolated,accounting for 2.91％of the total clinical isolates and 4.07％of Gram-negative bacteria in CHINET program.Among the 40 437 strains of H.influenzae,66.89％were isolated from children and 33.11％were isolated from adults.More than 90％of the H.influenzae strains were isolated from respiratory tract specimens.The prevalence of β-lactamase was 53.79％in H.influenzae strains.The H.influenzae strains isolated from children showed higher resistance rate than the strains isolated from adults.Overall,779 strains of H.influenzae did not produce β-lactamase but were resistant to ampicillin(BLNAR).Beta-lactamase-producing strains showed significantly higher resistance rates to these antimicrobial agents than the β-lactamase-nonproducing strains.Of the 16 191 M.catarrhalis strains,80.06％were isolated from children and 19.94％isolated from adults.M.catarrhalis strains were mostly susceptible to both amoxicillin-clavulanic acid and cefuroxime,evidenced by resistance rate lower than 2.0％.Conclusions The emergence of antibiotic-resistant H.influenzae due to β-lactamase production poses a challenge for clinical anti-infective treatment.Therefore,it is very important to implement antibiotic resistance surveillance for H.influenzae and guide rational antibiotic use.All local clinical microbiology laboratories should actively improve antibiotic susceptibility testing and strengthen antibiotic resistance surveillance for H.influenzae.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

Objective:To determine the epidemiological characteristics of human bocavirus (HBoV) in children with acute respiratory infections (ARI) in Bengbu City, Anhui Province, in 2024.Methods:Nasopharyngeal swab samples were collected from 269 children with ARI in Bengbu City, Anhui Province, in 2024. Seventeen respiratory pathogens were screened using quantitative fluorescence PCR. For HBoV-positive samples, the VP1/VP2 structural gene fragments of HBoV were amplified and sequenced for genetic evolutionary analysis.Results:Among the 269 nasopharyngeal swab samples from children with ARI, the overall detection rate of respiratory pathogens was 48.33% (103/269). The top three pathogens with the highest detection rates were: Influenza A virus (FluA): 10.04% (27/269), Respiratory syncytial virus (RSV): 8.18% (22/269), Human bocavirus (HBoV): 7.43% (20/269). The age distribution of HBoV-infected children showed that the detection rate was highest in the 0-2 years age group (50%, 10/20), followed by the 3-5 years age group (25%, 5/20) and the over 6 years age group (25%, 5/20). However, there was no statistically significant difference in viral detection rates among the age groups. Genetic evolutionary analysis based on VP1/VP2 revealed that all 13 HBoV strains were of the HBoV-1 genotype.Conclusions:HBoV is one of the major pathogens causing ARI in children in Bengbu City, Anhui Province, in 2024, with HBoV-1 being the predominant genotype. Additionally, infants aged 0-2 years are the most susceptible population to HBoV infection.

With the rapid development of competitive sports, the incidence of anterior cruciate ligament (ACL) injury is on the rise. Such injuries may shorten athletes′ career and lead to other long-term adverse consequences. Although athletes generally recover well after ACL reconstruction, many still struggle to return to their pre-injury performance levels. Advances in the understanding of ACL anatomy and injury mechanisms, along with the evolution of surgical techniques and rehabilitation methods, have provided more individualized and tailored options for athletes following ACL injuries. However, there is currently no consensus in China regarding surgical and rehabilitation strategies for competitive athletes aiming to return to sports after ACL injuries. To this end, the Sports Medicine Committee of the Chinese Research Hospital Association and the Editorial Board of the Chinese Journal of Trauma jointly formulated the Expert consensus on surgical treatment and rehabilitation for competitive sports athletes returning to sports after anterior cruciate ligament injury ( version 2025), and presented 14 recommendations covering surgical indications, preoperative rehabilitation, surgical timing, surgical strategies and postoperative rehabilitation strategies, aiming to improve the surgical treatment and rehabilitation system for ACL injuries in competitive athletes and facilitate their return to high-level sports performance after injury.

Nonunion of osteoporotic vertebral fractures (OVF), predominantly affecting the elderly, can lead to intractable pain, vertebral collapse, progressive kyphotic deformity, and neurological impairment, significantly compromising patients′ quality of life. There exists considerable debate on diagnosis and management of OVF, encompassing key issues such as clinical diagnosis and staging criteria for nonunion, surgical indications and procedure selection, and postoperative rehabilitation planning. Currently, there lacks standardized clinical guideline and expert consensus on the diagnosis and management of OVF nonunion in China. To address this gap, Minimally Invasive Surgery Group of Chinese Orthopedic Association, Osteoporosis Committee of Chinese Association of Orthopedic Surgeons, Prevention and Rehabilitation Committee for Osteoporosis of Chinese Association of Rehabilitation Medicine and Minimally Invasive Orthopedic Surgery Branch of China Association for Geriatric Care jointly organized domestic experts in spinal surgery, endocrinology, and rehabilitation to formulate the Clinical guideline for the diagnosis and treatment for nonunion of osteoporotic vertebral fractures ( version 2025), based on existing literature and clinical experience and adhering to principles of scientific rigor and practicality. The guideline provided 13 evidence-based recommendations encompassing diagnosis and treatment of OVF nonunion, aiming to standardize its clinical management.

Acute symptomatic osteoporotic thoracolumbar compression fracture (ASOTLF) can lead to chronic low back pain, kyphosis deformity, pulmonary dysfunction, loss of mobility, and even life-threatening complications. Vertebral augmentation is currently the mainstream treatment method for this condition. In 2019, the Editorial Board of Chinese Journal of Trauma and the Spinal Trauma Group of Orthopedic Surgeons Branch of Chinese Medical Doctor Association collaboratively led the development of Clinical guideline for vertebral augmentation for acute symptomatic osteoporotic thoracolumbar compression fractures. Six years later, with advances in clinical diagnosis and treatment techniques as well as accumulating evidence in related fields, the 2019 guideline requires updating. To this end, the Spinal Trauma Group of Orthopedic Surgeons Branch of Chinese Medical Doctor Association, the Spinal Health Professional Committee of China Human Health Science and Technology Promotion Association, and the Minimally Invasive Orthopedics Professional Committee of Shaanxi Medical Doctor Association have organized experts in the field to develop the Clinical guideline for vertebral augmentation of acute symptomatic osteoporotic thoracolumbar compression fractures ( version 2025) , based on the latest evidence-based medical researches. This guideline incorporates 3 recommendations retained from the 2019 version with updated strength of evidence, along with 12 new recommendations. It provides recommendations from six aspects of diagnosis, pain management, treatment option selection, prevention of postoperative complications, anti-osteoporosis therapy, and postoperative rehabilitation, aiming to provide a reference for standard treatment of vertebral augmentation for ASOTLF in hospitals at all levels.