Background Work-related musculoskeletal disorders (WMSDs) are a major occupational health concern, particularly among workers exposed to adverse ergonomic conditions. Manganese production involves heavy physical demands, yet research on WMSDs among manganese workers remains limited. Objective To investigate the prevalence and influencing factors of WMSDs among manganese workers in a manganese enterprise in Guangxi. Methods A cross-sectional survey was conducted from May to June 2024 on workers at a manganese factory in Guangxi. The Chinese Musculoskeletal Disorders Questionnaire was used to collect information on demographic characteristics, distribution of musculoskeletal symptoms, and work-related exposures. χ² test was applied to compare differences in positive WMSDs rates across groups, and logistic regression analysis was performed to identify associated factors. Results A total of 1476 workers were enrolled in the study after pre-determined inclusion and exclusion criteria. The overall prevalence of WMSDs was 34.15%. The most commonly affected body regions were the lower back (17.28%), neck (16.67%), and shoulders (13.82%). The results of logistic regression analysis indicated that female, older age, and education level of college or above were associated with a higher risk of WMSDs (P<0.05). Awkward working postures were significantly associated with WMSDs in corresponding body regions; in particular, awkward postures of the neck, upper limbs, trunk, and lower limbs were related to an increased risk of WMSDs in multiple body sites (P<0.05). In addition, poor lighting conditions, high workplace temperature, frequent or sustained arm support during work, and high job demands were associated with an increased risk of overall or site-specific WMSDs (P<0.05). Conclusion The high prevalence of WMSDs among manganese workers is closely associated with demographic characteristics, working postures, and work environment and organizational factors. Targeted ergonomic interventions focusing on high-risk body regions and key ergonomic exposures are warranted to reduce the risk of WMSDs among manganese workers.

ObjectiveThis paper aims to find the identified and validated clinical biomarker data building upon a clinical study of early-phase phase Ⅱ and investigate the correlation analysis of Huanglian Jiedu Wan on syndrome improvement and clinical biomarkers in the treatment of "excess heat-toxicity" based on a machine learning model. Additionally， the effective prediction of clinical biomarker values for the main symptoms of the "excess heat-toxicity" syndrome was assessed. MethodsA total of 229 patients meeting the inclusion criteria for "excess heat-toxicity" syndrome were randomly divided into the Huanglian Jiedu Wan group and the placebo group. Syndrome score transition matrices were constructed for the Huanglian Jiedu Wan group and the placebo group based on three main symptoms of "excess heat-toxicity" syndrome， such as oral ulcers， sore throat， and gum swelling and pain. Data from the patients with these three syndromes were also integrated for an overall analysis. The corresponding syndrome score transition matrices were further constructed to visualize symptom change trends of the patients in the two groups via heatmaps. Based on the identified and validated clinical biomarkers related to inflammation， oxidative stress， and energy metabolism in the early phase， Spearman correlation analysis was employed to analyze and evaluate the associations between clinical biomarkers and syndrome improvement. Key clinical biomarkers reflecting the effect of Huanglian Jiedu Wan were screened through the comparison of differences between groups. An extreme gradient boosting （XGBoost） algorithm was used to develop a prediction model for main symptom classification， with classification performance evaluated through 10-fold cross-validation. Feature importance analysis was applied to identify variables with the greatest contribution to the prediction result. ResultsThe syndrome transition matrix results indicated that the Huanglian Jiedu Wan group showed a superior effect to the placebo group in improving oral ulcers， sore throat， and overall symptoms， with significant effects observed especially in sore throat and overall symptom analyses （P<0.01）. Spearman correlation analysis revealed that several clinical biomarkers positively correlated with "excess heat-toxicity" syndrome and its main symptom improvement， were also called "heat-related biomarkers"， including succinic acid， α-ketoglutaric acid， glycine， lactic acid， adenosine monophosphate （AMP）， tumor necrosis factor-α （TNF-α）， interferon-γ （IFN-γ）， interleukin-1β （IL-1β）， interleukin-4 （IL-4）， interleukin-6 （IL-6）， interleukin-8 （IL-8）， interleukin-10 （IL-10）， and so on. Conversely， clinical biomarkers negatively correlated with symptom severity， were also called "heat-clearing related biomarkers" after administration of Huanglian Jiedu Wan， including malic acid， fumaric acid， cis-aconitic acid， adrenocorticotropic hormone （ACTH）， IL-1β， IL-4， IL-8， succinic acid， and citric acid. The XGBoost classification model using all 52 biomarkers as variables achieved an average test accuracy of 0.754 and an average F1 score of 0.777. Feature importance analysis identified the scores of glutamic acid in saliva and IL-6 were the highest in all the variables， with importance scores of 0.081 and 0.080， respectively. After screening out 14 key variables and optimizing the parameters， model performance improved to an average accuracy of 0.758 and an F1 score of 0.798. Feature importance analysis further determined that the glutamic acid in saliva and IL-6 showed obvious changes after screening the variables， confirming the good syndrome prediction ability of the model constructed by these key clinical biomarkers. ConclusionThis study systematically elucidates the correlation between syndrome improvement and clinical biomarkers of Huanglian Jiedu Wan in the treatment of "excess heat-toxicity" syndrome. An XGBoost classification model based on key clinical biomarkers is successfully established， achieving effective prediction of the symptoms related to the "excess heat-toxicity" syndrome such as oral ulcers and sore throat and providing a new insight for objective identification of traditional Chinese medicine syndromes.

ObjectiveThis paper aims to find the identified and validated clinical biomarker data building upon a clinical study of early-phase phase Ⅱ and investigate the correlation analysis of Huanglian Jiedu Wan on syndrome improvement and clinical biomarkers in the treatment of "excess heat-toxicity" based on a machine learning model. Additionally， the effective prediction of clinical biomarker values for the main symptoms of the "excess heat-toxicity" syndrome was assessed. MethodsA total of 229 patients meeting the inclusion criteria for "excess heat-toxicity" syndrome were randomly divided into the Huanglian Jiedu Wan group and the placebo group. Syndrome score transition matrices were constructed for the Huanglian Jiedu Wan group and the placebo group based on three main symptoms of "excess heat-toxicity" syndrome， such as oral ulcers， sore throat， and gum swelling and pain. Data from the patients with these three syndromes were also integrated for an overall analysis. The corresponding syndrome score transition matrices were further constructed to visualize symptom change trends of the patients in the two groups via heatmaps. Based on the identified and validated clinical biomarkers related to inflammation， oxidative stress， and energy metabolism in the early phase， Spearman correlation analysis was employed to analyze and evaluate the associations between clinical biomarkers and syndrome improvement. Key clinical biomarkers reflecting the effect of Huanglian Jiedu Wan were screened through the comparison of differences between groups. An extreme gradient boosting （XGBoost） algorithm was used to develop a prediction model for main symptom classification， with classification performance evaluated through 10-fold cross-validation. Feature importance analysis was applied to identify variables with the greatest contribution to the prediction result. ResultsThe syndrome transition matrix results indicated that the Huanglian Jiedu Wan group showed a superior effect to the placebo group in improving oral ulcers， sore throat， and overall symptoms， with significant effects observed especially in sore throat and overall symptom analyses （P<0.01）. Spearman correlation analysis revealed that several clinical biomarkers positively correlated with "excess heat-toxicity" syndrome and its main symptom improvement， were also called "heat-related biomarkers"， including succinic acid， α-ketoglutaric acid， glycine， lactic acid， adenosine monophosphate （AMP）， tumor necrosis factor-α （TNF-α）， interferon-γ （IFN-γ）， interleukin-1β （IL-1β）， interleukin-4 （IL-4）， interleukin-6 （IL-6）， interleukin-8 （IL-8）， interleukin-10 （IL-10）， and so on. Conversely， clinical biomarkers negatively correlated with symptom severity， were also called "heat-clearing related biomarkers" after administration of Huanglian Jiedu Wan， including malic acid， fumaric acid， cis-aconitic acid， adrenocorticotropic hormone （ACTH）， IL-1β， IL-4， IL-8， succinic acid， and citric acid. The XGBoost classification model using all 52 biomarkers as variables achieved an average test accuracy of 0.754 and an average F1 score of 0.777. Feature importance analysis identified the scores of glutamic acid in saliva and IL-6 were the highest in all the variables， with importance scores of 0.081 and 0.080， respectively. After screening out 14 key variables and optimizing the parameters， model performance improved to an average accuracy of 0.758 and an F1 score of 0.798. Feature importance analysis further determined that the glutamic acid in saliva and IL-6 showed obvious changes after screening the variables， confirming the good syndrome prediction ability of the model constructed by these key clinical biomarkers. ConclusionThis study systematically elucidates the correlation between syndrome improvement and clinical biomarkers of Huanglian Jiedu Wan in the treatment of "excess heat-toxicity" syndrome. An XGBoost classification model based on key clinical biomarkers is successfully established， achieving effective prediction of the symptoms related to the "excess heat-toxicity" syndrome such as oral ulcers and sore throat and providing a new insight for objective identification of traditional Chinese medicine syndromes.

ObjectiveTo investigate the expression changes of the Endophilin A1 （EPA1）-reactive oxygen species （ROS）-NLR family pyrin domain containing 1 （NLRP1） signaling pathway in chronic unpredictable mild stress （CUMS）-induced depression model mice. Methods50 C57BL/6 mice were randomly divided into control group and CUMS model group（n=25）. The model mice received 42 days of CUMS stress exposure， after which behavioral changes were assessed through monitoring body weight， sucrose preference test， forced swim test， tail suspension test， open field test， and elevated plus-maze test. Hematoxylin-Eosin and Nissl staining were used to observe neuronal damage in hippocampal CA1 and CA3 regions. Calcium ion （Ca²⁺） assay kit was used to detect Ca²⁺ levels in the hippocampus. Immunofluorescence was used to detect colocalization of EPA1 and NLRP1 as well as ROS changes in the hippocampus. Transmission electron microscopy was used to observe mitochondrial structure in hippocampal neurons. The expression levels of calcium-activated neutral protease 1 （Calpain-1）， nicotinamide adenine dinucleotide phosphate oxidase 2 （NOX2）， NLRP1 inflammasome， downstream inflammatory proteins， and synapse-associated proteins in the hippocampus of mice was detected by using Western blot. ResultsCompared with the control group， mice in the CUMS model group exhibited depressive-like behavior and hippocampal neuronal damage. The levels of NLRP1 and EPA1 significantly increased in the hippocampus， and both were co-expressed in the cytoplasm and membrane of hippocampal neurons in the CUMS group mice（P<0.01）. Ca²⁺ concentration was elevated（P<0.01）， and the protein levels of Calpain 1 and NOX2 were upregulated（P<0.01）. The average fluorescence intensity of ROS significantly increased， accompanied by structural damage to neuronal mitochondria（P<0.01）. The levels of NLRP1 and its downstream inflammatory proteins significantly increased （P<0.01）， while the expression levels of synapse-associated proteins significantly decreased （P<0.01）. ConclusionEPA1 exhibits abnormal expression changes in CUMS-induced depression model mice， which may be closely associated with ROS generation， NLRP1 inflammasome activation， and the regulation of synaptic protein expression.

Palmitoylation, an essential covalent attachment of a fatty acid (usually C16 palmitate) to cysteine residues within proteins, is crucial for regulating protein functionality and enzymatic activities. This lipid modification facilitates the anchoring of proteins to cellular membranes, dictating their subcellular distribution and influencing protein transport dynamics and intracellular positioning. Additionally, it plays a role in regulating protein degradation through the ubiquitin-proteasome system. Palmitoylation is implicated in the pathogenesis and progression of cardiovascular diseases by modulating substrates and prompting additional post-translational modifications, as well as by interacting with other molecular alterations. Moreover, an intervention strategy focusing on palmitoylation processes is anticipated to offer novel therapeutic avenues for cardiovascular pathologies and address extant challenges in clinical settings. This review consolidates current research on the role and importance of palmitoylation in cardiovascular diseases by exploring its regulatory functions, the catalyzing enzymes, and the involved substrates. It highlights recent discoveries connecting palmitoylation-targeted therapies to cardiovascular health and examines potential approaches and future challenges in cardiovascular treatment.

Objective:To investigate the effect and mechanism of Jianpi Qinghua granule in improving skeletal muscle insulin resistance induced by long-term low-dose cadmium exposure in rats.Methods:A total of 24 SPF-grade healthy 2-month-old Sprague-Dawley rats were assigned to normal control(NC) group, cadmium exposure(Cd) group, and Jianpi Qinghua granule protection(Cd+ JPQHG) group. After 24 weeks, fasting blood glucose and insulin levels were measured, and HOMA-IR was calculated. The intraperitoneal glucose tolerance test and insulin tolerance test were conducted to assess insulin sensitivity. Skeletal muscle tissues were extracted for Western blot analysis to detect levels of insulin signaling pathway-related proteins. Immunofluorescence was used to assess the translocation of glucose transporter 4(GLUT4), and oxidative stress markers were measured.Results:Compared to the NC group, the Cd group showed significant increases in fasting plasma glucose, fasting insulin levels, and HOMA-IR after 24-week exposure. Abnormal glucose and insulin tolerance were also observed in the Cd group. The 12-week intervention with Jianpi Qinghua granule significantly improved glucose metabolism and alleviated the abnormalities in glucose and insulin tolerance. Western blot results indicated that the phosphorylation levels of PI3K and Akt in the skeletal muscle of the Cd group were significantly reduced compared to the NC group, while these levels were significantly elevated in the Cd+ JPQHG group, along with increased translocation of GLUT4 to the cell membrane. Additionally, cadmium exposure significantly increased H 2O 2 and malondialdehyde levels while decreased antioxidant enzyme activity. These oxidative stress indicators improved significantly after Jianpi Qinghua granule intervention( P<0.05). Conclusion:Jianpi Qinghua granule may improve skeletal muscle insulin resistance and glucose metabolism disorders due to long-term low-dose cadmium exposure by reducing oxidative stress, regulating the phosphorylation levels of key proteins in the insulin signaling pathway, and promoting GLUT4 translocation to the cell membrane.

Objective:To investigate the assoication of amino acid metabolism levels with severity of diabetic peripheral neuropathy(DPN) in patients with type 2 diabetes mellitus.Methods:A retrospective analysis was conducted on 118 patients with type 2 diabetes mellitus who were admitted to Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine from January to April 2021. Patients were divided into groups according to the Toronto Clinical Scoring System(TCSS), and amino acid profiling was performed. General demographics and biochemical indicators of each group were collected to analyze the relationship between DPN severity and amino acid metabolism.Results:As TCSS scores increased, patients were older and had a longer duration of diabetes. Statistically significant differences in leucine, isoleucine, tryptophan, and phenylalanine levels were observed among the four groups. After adjusting for confounding variables using covariance analysis, when the TCSS score was≥13, the serum phenylalanine level was significantly elevated, and the difference was statistically significant( P<0.05). Multivariate linear regression analysis indicated that TCSS score was an influencing factor for phenylalanine levels( P=0.010). Multivariate logistic regression analysis demonstrated that higher serum phenylalanine levels correlated with higher TCSS scores( OR=1.047, 95% CI 1.011-1.083, P=0.010). Receiver operating characteristic(ROC) curve analysis revealed that serum phenylalanine and the difference between phenylalanine and tryptophan had diagnostic value for severe DPN patients, with areas under the ROC curve of 0.673(95% CI 0.553-0.793, P=0.006) and 0.746(95% CI 0.641-0.852, P<0.001) respectively. Conclusions:The levels of phenylalanine and tryptophan in the serum of patients with type 2 diabetes mellitus are correlated with the severity of DPN. These findings suggest that serum phenylalanine, tryptophan, or their metabolic products may be involved in the pathogenesis and progression of DPN.

ObjectiveTo elucidate the critical role of rehabilitation medical records (including electronic records) in rehabilitation medicine's clinical practice and management, comprehensively analyzed the structure, core content and data standards of rehabilitation medical records, to develop a standardized medical record data architecture and core dataset suitable for rehabilitation medicine and to explore the application of rehabilitation data in performance evaluation and payment. MethodsBased on the regulatory documents Basic Specifications for Medical Record Writing and Basic Specifications for Electronic Medical Records (Trial) issued by National Health Commission of China, and referencing the World Health Organization (WHO) Family of International Classifications (WHO-FICs) classifications, International Classification of Diseases (ICD-10/ICD-11), International Classification of Functioning, Disability and Health (ICF), and International Classification of Health Interventions (ICHI Beta-3), this study constructed the data architecture, core content and data standards for rehabilitation medical records. Furthermore, it explored the application of rehabilitation record summary sheets (home page) data in rehabilitation medical statistics and payment methods, including Diagnosis-related Groups (DRG), Diagnosis-Intervention Packet (DIP) and Case Mix Index. ResultsThis study proposed a systematic standard framework for rehabilitation medical records, covering key components such as patient demographics, rehabilitation diagnosis, functional assessment, rehabilitation treatment prescriptions, progress evaluations and discharge summaries. The research analyzed the systematic application methods and data standards of ICD-10/ICD-11, ICF and ICHI Beta-3 in the fields of medical record terminology, coding and assessment. Constructing a standardized data structure and data standards for rehabilitation medical records can significantly improve the quality of data reporting based on the medical record summary sheet, thereby enhancing the quality control of rehabilitation services, effectively supporting the optimization of rehabilitation medical insurance payment mechanisms, and contributing to the establishment of rehabilitation medical performance evaluation and payment based on DRG and DIP. ConclusionStructured rehabilitation records and data standardization are crucial tools for quality control in rehabilitation. Systematically applying the three reference classifications of the WHO-FICs, and aligning with national medical record and electronic health record specifications, facilitate the development of a standardized rehabilitation record architecture and core dataset. Standardizing rehabilitation care pathways based on the ICF methodology, and developing ICF- and ICD-11-based rehabilitation assessment tools, auxiliary diagnostic and therapeutic systems, and supporting terminology and coding systems, can effectively enhance the quality of rehabilitation records and enable interoperability and sharing of rehabilitation data with other medical data, ultimately improving the quality and safety of rehabilitation services.

ObjectiveTo explore the data standard system of electronic medical records in the field of rehabilitation, focusing on the terminology and coding standards, data structure, and key content categories of rehabilitation electronic medical records. MethodsBased on the Administrative Norms for the Application of Electronic Medical Records issued by the National Health Commission of China, the electronic medical record standard architecture issued by the International Organization for Standardization and Health Level Seven (HL7), the framework of the World Health Organization Family of International Classifications (WHO-FICs), Basic Architecture and Data Standards of Electronic Medical Records, Basic Data Set of Electronic Medical Records, and Specifications for Sharing Documents of Electronic Medical Records, the study constructed and organized the data structure, content, and data standards of rehabilitation electronic medical records. ResultsThe data structure of rehabilitation electronic medical records should strictly follow the structure of electronic medical records, including four levels (clinical document, document section, data set and data element) and four major content areas (basic information, diagnostic information, intervention information and cost information). Rehabilitation electronic medical records further integrated information related to rehabilitation needs and characteristics, emphasizing rehabilitation treatment, into clinical information. By fully applying the WHO-FICs reference classifications, rehabilitation electronic medical records could establish a standardized framework, diagnostic criteria, functional description tools, coding tools and terminology index tools for the coding, indexing, functional description, and analysis and interpretation of diseases and health problems. The study elaborated on the data structure and content categories of rehabilitation electronic medical records in four major categories, refined the granularity of reporting rehabilitation content in electronic medical records, and provided detailed data reporting guidance for rehabilitation electronic medical records. ConclusionThe standardization of rehabilitation electronic medical records is significant for improving the quality of rehabilitation medical services and promoting the rehabilitation process of patients. The development of rehabilitation electronic medical records must be based on the national and international standards. Under the general electronic medical records data structure and standards, a rehabilitation electronic medical records data system should be constructed which incorporates core data such as disease diagnosis, functional description and assessment, and rehabilitation interventions. The standardized rehabilitation electronic medical records scheme constructed in this study can support the improvement of standardization of rehabilitation electronic medical records data information.

ObjectiveTo analyze the data structure and standards of rehabilitation outpatient medical records, to provide data support for improving the quality of rehabilitation outpatient care and developing medical insurance payment policies. MethodsBased on the normative documents issued by the National Health Commission, Basic Standards for Medical Record Writing and Standards for Electronic Medical Record Sharing Documents, in accordance with the Quality Management Regulations for Outpatient (Emergency) Diagnosis and Treatment Information Pages (Trial), reference to the framework of the World Health Organization Family of International Classifications (WHO-FICs), the data framework and content of rehabilitation outpatient medical records were determined, and the data standards were discussed. ResultsThis study constructed a data framework for rehabilitation outpatient medical records, including four main components: patient basic information, visit process information, diagnosis and treatment information, and cost information. Three major reference classifications of WHO-FICs, International Classification of Diseases, International Classification of Functioning, Disability and Health, and International Classification of Health Interventions,were used to establish diagnostic standards and standardized terminology, as well as coding disease diagnosis, functional description, functional assessment, and rehabilitation interventions, to improve the quality of data reporting, and level of quality control in rehabilitation. ConclusionThe structuring and standardization of rehabilitation outpatient medical records are the foundation for sharing of rehabilitation data. The using of the three major classifications of WHO-FICs is valuable for the terminology and coding of disease diagnosis, functional description and assessment, and intervention in rehabilitation outpatient medical records, which is significant for sharing and interconnectivity of rehabilitation outpatient data, as well as for optimizing the quality and safety of rehabilitation medical services.