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.

The disease-syndrome combination animal model in traditional Chinese medicine （TCM） andrology serves as an important bridge linking TCM theory with modern medical research， providing a key experimental platform for elucidating the 'syndrome-disease' correlation mechanism in male-specific diseases and for screening effective prescriptions. This article reviews recent progress in animal model research on common TCM andrological diseases， including prostatic diseases， sexual dysfunction， and male infertility， with a focus on analyzing the application， advantages， and disadvantages of various modeling strategies， such as immune induction， hormonal intervention， and multi-factor combination across different syndrome types. However， despite breakthroughs in model construction techniques， current research still faces several challenges， including insufficient standardization of syndrome differentiation and difficulties in quantifying TCM-specific indicators. Future studies need to optimize model evaluation systems by integrating modern technologies， in order to promote the standardization and internationalization of TCM andrology research.

Objective To explore the effect and mechanism of Gegen Qinlian decoction (GQD) on myocardial injury induced by high-fat diet combined with streptozotocin in type 2 diabetic mice through network pharmacology and animal experiments. MethodsNetwork pharmacology was applied to screen the potential signaling pathways of GQD in the intervention of diabetic heart disease (DHD). A total of 36 specific pathogen-free (SPF) male C57BL/6J mice aged 8 weeks were acclimatized for 1 week and randomly divided into a normal control group (n=6) and a model group (n=30). Mice in the model group were fed a 60% high-fat diet and 5% glucose water for 4 weeks, and were intraperitoneally injected with 100 mg/kg streptozotocin after 12 h of fasting to establish the type 2 diabetes model. Mice in the normal control group were given a conventional diet and drinking water, and were intraperitoneally injected with an equal volume of citrate buffer after 12 h of fasting at the same time point. After successful establishment of the diabetic mouse model, the model mice were randomly divided into five groups according to body weight: model control group, GQD low-dose group, GQD medium-dose group, GQD high-dose group, and metformin positive control group, with 6 mice in each group. After 6 weeks of intragastric administration of corresponding doses of GQD, metformin, and ddH₂O respectively, all mice in the six groups (one normal control group and five model groups) were anesthetized with inhaled 2% isoflurane, blood samples were collected via eyeball enucleation, and heart tissues were harvested after euthanasia by cardiac exsanguination. Hematoxylin-eosin (HE) staining and wheat germ agglutinin (WGA) staining were performed to observe the pathological damage of myocardial tissues and measure the cross-sectional area of cardiomyocytes. Enzyme-linked immunosorbent assay (ELISA) was used to measure the levels of pro-inflammatory cytokines in serum. Immunohistochemical staining was adopted to determine the protein expression levels of receptor for advanced glycation end products (RAGE), nucleotide-binding domain leucine-rich repeat and pyrin domain-containing receptor 3 (NLRP3), and phosphorylated nuclear factor-κB p65 (p-p65) in myocardial tissues. ResultsNetwork pharmacology analysis revealed that the targets of GQD against DHD were significantly enriched in inflammatory pathways, including the advanced glycation end products (AGEs)-RAGE axis, tumor necrosis factor (TNF) and interleukin-17 (IL-17) signaling pathways. HE and WGA staining results showed that, compared with the normal control group, the model control group exhibited myocardial fiber rupture, cardiomyocyte swelling, and significantly increased cardiomyocyte cross-sectional area (P<0.05). Compared with the model control group, GQD intervention significantly ameliorated the pathological changes, including myocardial fiber rupture, cell swelling, and cardiomyocyte hypertrophy. The results of immunohistochemical staining and ELISA indicated that compared with the model control group, GQD intervention markedly downregulated the protein expression of RAGE, NLRP3, and p-p65 in myocardial tissues, as well as the levels of serum pro-inflammatory cytokines (P<0.05). ConclusionGQD can effectively alleviate myocardial pathological injury in diet-induced type 2 diabetic mice, and its mechanism may be related to inhibiting the activation of the RAGE/p-p65/NLRP3 signaling pathway and reducing myocardial inflammatory response.

The sirtuin 1(SIRT1)is an important NAD+-dependent deacetylase that has attracted much attention in ophthalmic research in recent years. This is because the expression of SIRT1 in ocular tissues and its function are inextricably linked to the pathogenesis and progression of many ocular diseases, including dry eye, glaucoma, cataract and diabetic retinopathy. Through in-depth investigations, we have found that SIRT1, as a key regulatory protein, has a profound impact on the pathophysiological processes of ocular diseases through a variety of mechanisms, such as regulating apoptotic programs, modulating oxidative stress, mediating inflammatory responses and maintaining normal mitochondrial function. These findings indicate that SIRT1 plays an important protective role in ocular diseases. The aim of this article is to comprehensively review the latest research findings on SIRT1 in ophthalmic diseases in recent years, and hopes to provide new ideas and methods for the prevention and treatment of ophthalmic diseases by thoroughly analyzing the mechanism of action of SIRT1.

Objective To explore the diagnostic value of exhaled volatile organic compounds (VOCs) for cystic fibrosis (CF). Methods A systematic search was conducted in PubMed, EMbase, Web of Science, Cochrane Library, CNKI, Wanfang, VIP, and SinoMed databases up to August 7, 2024. Studies that met the inclusion criteria were selected for data extraction and quality assessment. The quality of included studies was assessed by the Newcastle-Ottawa Scale (NOS), and the risk of bias and applicability of included prediction model studies were assessed by the prediction model risk of bias assessment tool (PROBAST). Results A total of 10 studies were included, among which 5 studies only identified specific exhaled VOCs in CF patients, and another 5 developed 7 CF risk prediction models based on the identification of VOCs in CF. The included studies reported a total of 75 exhaled VOCs, most of which belonged to the categories of acylcarnitines, aldehydes, acids, and esters. Most models (n=6, 85.7%) only included exhaled VOCs as predictive factors, and only one model included factors other than VOCs, including forced expiratory flow at 75% of forced vital capacity (FEF75) and modified Medical Research Council scale for the assessment of dyspnea (mMRC). The accuracy of the models ranged from 77% to 100%, and the area under the receiver operating characteristic curve ranged from 0.771 to 0.988. None of the included studies provided information on the calibration of the models. The results of the Prediction Model Risk of Bias Assessment Tool (PROBAST) showed that the overall bias risk of all predictive model studies was high, and the overall applicability was unclear. Conclusion The exhaled VOCs reported in the included studies showed significant heterogeneity, and more research is needed to explore specific compounds for CF. In addition, risk prediction models based on exhaled VOCs have certain value in the diagnosis of CF, but the overall bias risk is relatively high and needs further optimization from aspects such as model construction and validation.

Objective To explore the reproductive health literacy of young female sailors,investigate the influence of long voyage on the health,and propose strategies on health education.Methods A total of 49 young female sailors were enrolled.Their healthy status was evaluated by annual medical records.The questionnaire survey was also conducted to evaluate general status related to reproductive health,knowledge pertaining to reproductive health,and health status after long voyage.Results Some female sailors had symptoms of thyroid diseases,such as hypothyroidism(32.65%),benign thyroid nodules(59.18%),and suspected papillary carcinoma(in one sailor,2.04%).Ultrasonography revealed breast nodules in 5 subjects(10.00%),mammary gland hyperplasia in 46 subjects(93.88%)and reproductive benign diseases in 6 subjects(12.24%).The physical and mental states significantly changed in the late stage of long voyage.The changes in work and living environment were the main factors affecting the emotional and psychological status in the long voyage.The discomfort in menstrual cycle piled up with the above-mentioned changes during a long voyage,and the menstruation significantly affected the duty of 36.73%of female sailors during a long voyage.Female sailors had little knowledge about the prevention and examination of breast cancer and thyroid cancer.Conclusion The living and work environment during a long voyage may affect the health of the thyroid,mammary gland and reproductive system of young female sailors.Active health education via online and offline ways is helpful to improve the health literacy and self-care of female sailors,subsequently enhancing the combat effectiveness.

Test Methods for Disinfection Products(WS/T 10009-2023),a health industry standard,was officially released by the National Disease Control and Prevention Administration on December 15,2023.The standard came into effect on May 1,2024.This standard systematically specifies the testing and evaluation methods for disinfection products,covering three core components—disinfection effect testing and evaluation,physical and chemical testing techniques,and toxicological testing methods.The standard provides an important technical basis for the testing of disinfection products in China.To promote an accurate understanding and effective implementation of the standard,this article focuses on the in-depth interpretation of the section concerning the testing and evaluation methods of disinfection effects.It provides a detailed explanation of the major updates,technical highlights,and scientific rationale behind the standard.The article incorporates discussions on optimizing the validation test methods for the disinfection and sterilization effects of disinfectants,simulated field testing of disinfectants,evaluation of air disinfection effects,and the supplementation and improvement of testing methods for disinfection devices(including sterilization devices).This article aims to provide clear technical guidance for disinfection product inspection personnel,researchers,and other professionals involved,promote the standardized application of the standard,improve the quality of disinfection products,and ensure scientific,effective,and safe disinfection practices.

Atopic dermatitis（AD）is a chronic，recurrent inflammatory skin disease.The pathogenesis of AD is complicated，involving many factors such as genetic，immune，and skin barrier.Previous studies have attributed skin barrier dysfunction to the changes in skin microbiota caused by immune disorders.In recent years，researchers have found that abnormal immune regulation related to the gut microbiota may be an important factor that leads to the development of AD.Gut microbiota dysbiosis and metabolomic disorders are of great significance to explore the pathogenesis of AD.The treatment of AD is currently focused on topical anti-inflammatory drugs，but the long-term efficacy is poor.Therefore，the treatment of fecal microbiota transplantation based on correcting gut microbiota dysbiosis came into being.This article summarized the current research status of gut microbiota dysbiosis in the occurrence and progression of AD，and the application progress and efficacy of fecal microbiota transplantation in children with AD，providing reference for the etiological analysis and clinical intervention of AD.

Objective To explore the perceptions,diagnostic criteria and clinical management strategies of joint surgeons regarding perioperative knee extension lag in patients with total knee arthroplasty.Methods From June to July,2024,17 joint surgeons from Peking University the First Hospital and Peking University the Third Hospital were selected as research subjects using purposive and convenience sampling.Semi-structured in-terviews were conducted.The interview data were analyzed and themes were summarized using NVivo 12.0 soft-ware.Results A total of four themes and ten sub-themes emerged from the study.Theme 1 was the basics related to knee exten-sion lag,including definition,diagnosis and incidence.Theme 2 was the attitudes of joint surgeons towards knee extension lag,including clinical value and importance.Theme 3 was the impact of knee extension lag on postop-erative functional outcomes,including the impact of preoperative knee extension lag and postoperative knee ex-tension lag on functional recovery.Theme 4 was management strategies for knee extension lag,including preop-erative rehabilitation,surgical strategies and postoperative management.Conclusion The standardized diagnosis and treatment of knee extension lag are still required further research.It is rec-ommended that relevant content be added and emphasized in the training of joint surgeons and the early rehabili-tation intervention of rehabilitation practitioners to improve the diagnosis and treatment of knee extension lag and to make it a useful addition to the perioperative management of total knee arthroplasty.