AIM: To evaluate the efficacy and safety of 3% diquafosol sodium eye drops in children wearing orthokeratology lenses and with dry eye disease(DED)or at risk of DED.METHODS: Randomized controlled trials. Children with DED or at risk of DED were randomly assigned in a 1:1 ratio to receive either 3% diquafosol sodium eye drops 6 times daily or a blank control at Chongqing Aier Children's Eye Hospital from November 2023 to November 2024. The primary endpoint was the change in the Dry Eye Questionnaire-5(DEQ-5)score from baseline at 12 wk. Secondary assessments included non-invasive breakup time(NIBUT), tear meniscus height, Schirmer's test, corneal fluorescein staining score, and axial length.RESULTS: A total of 80 participants(80 eyes)were enrolled(40 in each group), the average age of the participants was 11.11±1.88 years, with 43 females(54%)and 37 males(46%), and all completed the trial. After 12 wk, the DEQ-5 scores for the diquafosol sodium group and the blank control group were 1.88±2.02 and 2.88±2.79, respectively(P=0.079). The diquafosol sodium group demonstrated a significant improvement in DEQ-5 dryness symptom scores(-0.33±0.66 vs. 0.05±0.81, P=0.023)and NIBUT(6.18±3.73 vs. -1.09±4.40 s, P<0.001)at 12 wk. Additionally, the diquafosol sodium group showed no axial length elongation, in contrast to the blank control group, which exhibited elongation(0.00±0.08 vs. 0.05±0.10 mm, P=0.013). No other significant differences were found in the secondary endpoints. No adverse events occurred during the trial.CONCLUSION: Although no statistically significant improvements were noted in the overall DEQ-5 scores, the 3% diquafosol sodium eye drops significantly improved dryness symptoms and NIBUT when compared to the blank control group.

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.

Objective To construct a dynamic prediction model for JC polyomavirus (JCV) reactivation after kidney transplantation. Methods A retrospective analysis was conducted on the clinical data of 128 recipients who met the inclusion criteria and received kidney transplantation in Nanfang Hospital, Southern Medical University, from June 2021 to December 2024. Dynamic Cox regression and dynamic restricted mean survival time (RMST) regression based on the landmark method were adopted to establish the dynamic prediction models, and Monte Carlo cross-validation was used to evaluate model performance. Results The dynamic models could predict the incidence and onset time of JCV reactivation within the subsequent 3 months according to covariates at each landmark time point from the 1st to the 6th month after transplantation. Remuzzi score, body mass index and warm ischemia time were independent risk factors for JCV reactivation, while a history of urinary BK polyomavirus reactivation served as a protective factor. The median values of the area under the curve, C-index and Brier score of the dynamic Cox model were 0.873, 0.851 and 0.065 respectively, and the C-index of the dynamic RMST model was 0.829, all of which were superior to those of the static model. Conclusions The landmark-based dynamic models exhibit excellent predictive performance, which may integrate baseline data and post-transplant follow-up information to realize dynamic assessment of the risk and time window of JCV reactivation, thereby providing evidence for optimizing post-operative monitoring and individualized intervention strategies.

Traditional Chinese medicine and its bioactive components have garnered increasing attention as potential therapeutic options for allergic asthma. By targeting the phosphatidylinositol 3-kinase (PI3K)/protein kinase B (AKT) signaling pathway, these natural compounds exhibit unique advantages in multilevel immunomodulation and inflammation suppression compared with single-target synthetic drugs. Accumulating pharmacological evidence supports their capacity to restore pathway homeostasis, positioning them as promising candidates for complementary strategies in asthma management. Allergic asthma, a heterogeneous respiratory disorder affecting approximately 150 million individuals worldwide, arises from a complex interplay of genetic predisposition, environmental exposures, and lifestyle factors. Its pathological progression is marked by aberrant activation of the PI3K/AKT signaling cascade, with the mechanistic target of rapamycin serving as a key downstream regulatory node. This evolutionarily conserved pathway orchestrates fundamental cellular processes that contribute to three hallmark pathological features of allergic asthma: chronic airway inflammation, structural remodeling of the bronchial architecture, and airway hyperresponsiveness. This review has 3 primary objectives: (1) to evaluate the role of the PI3K/AKT pathway in allergic asthma pathogenesis, (2) to analyze the molecular mechanisms of representative traditional Chinese medicine preparations and their active ingredients, and (3) to identify novel bioactive inhibitors derived from natural products. Collectively, these investigations provide a conceptual framework for the development of next-generation targeted therapies and for optimizing clinical management strategies for allergic asthma.

Hepatic vascular malformations are a group of congenital or acquired disorders characterized by structural abnormalities of the intrahepatic arterial， venous， or portal venous systems， with the clinical manifestations of hyperdynamic circulation， portal hypertension， and complications of portosystemic shunting. Due to the low incidence rate and nonspecific clinical manifestations of such disorders， they are easily confused with cirrhotic portal hypertension， leading to the delay in diagnosis and treatment. This article introduces the common clinical manifestations and differential diagnosis of hepatic vascular malformations and elaborates on three typical types of hepatic vascular malformations， in order to provide ideas and references for clinical diagnosis and treatment.

To address the challenges of fragmented data, inconsistent standards, and delayed feedback in the full-cycle competency development process at teaching hospitals, this paper proposes the LEADS data governance system for Precision Medical Education (LEADS-PME), drawing on the practical experience of building the integrated medical education management platform at Zhongshan Hospital, Fudan University. The LEADS-PME framework comprises five interconnected core components: Longitudinal foundation, Evidence-chain generation, Analytics and profiling, Decision-support and prediction, and Simulation and digital-twin governance. Currently, Zhongshan Hospital has established a full-cycle educational data infrastructure and achieved multi-system data integration and competency-based profiling and evaluation, while the prediction and simulation modules are still in the prototype exploration phase. A mixed-methods evaluation will be subsequently conducted from the perspectives of data quality, management effectiveness, and predictive intervention. As a locally adapted framework, LEADS-PME can transform operational data derived from clinical practice, teaching, assessment, and management processes into traceable, interpretable, and reusable evidence of competency development. It is expected to facilitate the transformation of medical education management from a outcome-oriented approach to process monitoring, evidence-driven decision-making, and precision governance, thereby providing both a theoretical framework and practical reference for precision medical education in Chinese teaching hospitals.

Objective:To explore the correlation between skeletal muscle mass and strength with metabolic syndrome in children.Methods:This cross-sectional study was conducted involving 383 children aged 10 to 15 years who visited the Department of Child Health Care, Children′s Hospital of Nanjing Medical University from June 2021 to December 2022. Their height, weight, waist circumference, body composition, grip strength and blood pressure were measured. Relative skeletal muscle mass, muscle-to-fat ratio, and grip strength-to-body weight index were calculated. The levels of fasting blood glucose, lipids and insulin were tested. Homeostasis model assessment of insulin resistance (HOMA-IR) was calculated. Children were divided into the metabolic syndrome group and the non-metabolic syndrome group. Independent t test or Mann-Whitney U test etc. was used to compare the difference between groups. Spearman correlation analysis and binary Logistic regression were used to investigate the correlation between skeletal muscle mass and strength and metabolic syndrome. The area under the curve (AUC) of receiver operating characteristic (ROC) curve was used to compare the accuracy of the index of skeletal muscle in predicting metabolic syndrome in children. Results:Among the 383 children, 282 (73.6%) were male, at the age of 11.4 (10.6, 12.5) years. There were 216 children (56.4%) diagnosed with obesity and 90 children (23.5%) diagnosed with metabolic syndrome. Relative skeletal muscle mass, muscle-to-fat ratio, and grip strength-to-body weight index of the metabolic syndrome group were all lower than those in the non-metabolic syndrome group (all P<0.001). After adjusting for sex and age, relative skeletal muscle mass, muscle-to-fat ratio, and grip strength-to-body weight index were all negatively correlated with body mass index ( r=-0.84, -0.38, -0.63), waist circumference ( r=-0.76, -0.36, -0.70), systolic blood pressure ( r=-0.42, -0.21, -0.38), diastolic blood pressure ( r=-0.33, -0.18, -0.24), triglycerides ( r=-0.29, -0.13, -0.23), fasting insulin ( r=-0.28, -0.20, -0.29), and HOMA-IR ( r=-0.26, -0.18, -0.26) (all P<0.05), and positively correlated with high-density lipoprotein cholesterol ( r=0.38, 0.13, 0.31, all P<0.01). After adjusting for sex and age, high relative skeletal muscle mass, high muscle-to-fat ratio, and high grip strength-to-body weight index all decreased the risks of metabolic syndrome ( OR=0.80, 0.55, 0.90), obesity ( OR=0.53, 0.64, 0.82), hypertension ( OR=0.86, 0.58, 0.92), low high-density lipoprotein cholesterol ( OR=0.83, 0.62, 0.92), hypertriglyceridemia ( OR=0.88, 0.78, 0.96). After adjusting for sex and age, high relative skeletal muscle mass and high grip strength-to-body weight index all decreased the risks of hyperglycemia ( OR=0.93 and 0.95, all P<0.05). ROC curve analysis showed that the relative skeletal muscle mass, muscle-to-fat ratio, and grip strength-to-body weight index all had good predictive accuracy of metabolic syndrome in children (AUC=0.79, 0.71, 0.76), with optimal cutoff values of 40%, 1.2, and 35%, respectively. Conclusions:High relative skeletal muscle mass, high muscle-to-fat ratio, and high grip strength-to-body weight index are all protective factors for metabolic syndrome in children. Regular measurement of skeletal muscle mass and grip strength can aid in the early identification and prevention of obesity and metabolic syndrome during childhood .

Objective:To explore the trend of influenza positive rate in Beijing by using classic autoregressive integrated moving average (ARIMA) model, autoregressive integrated moving average model with exogenous variables (ARIMAX) and vector autoregression model (VAR) to compare the performance of three models in influenza prediction and select the most suitable one for Beijing.Methods:The weekly positive rate of influenza virus nucleic acid test and meteorological data in Beijing from week 1 of 2013 to week 40 of 2024 were collected. The data were divided into four groups with expanding training sets and corresponding testing sets. The training set of the first group was from week 1 of 2013 to week 40 of 2016, and the testing set was from week 41 of 2016 to week 40 of 2017. Subsequent groups extended the training set by one year each time. Data from 2020 to 2023 were excluded due to COVID-19 pandemic. The fourth group used data from the week 1 of 2013 to week 40 of 2023 for training and from the week 41 of 2023 to week 40 of 2024 for testing.Results:The incidence of influenza had seasonality in Beijing with higher incidence in winter and spring. The positive rate of influenza virus was positively correlated with the weekly average atmospheric pressure ( r=0.482, P<0.001) and weekly average wind speed ( r=0.003, P=0.034), and negatively correlated with the weekly average temperature ( r=-0.541, P<0.001). The ARIMAX model incorporating meteorological factors had the best prediction performance, with test set's root mean square error ( RMSE) of 0.115 3 and mean absolute error ( MAE) of 0.076 7 (the RMSE and MAE values for ARIMA and VAR models were 0.117 1 and 0.163 8, and 0.078 6 and 0.122 3, respectively). The prediction results of the optimal model showed that the positive rate of influenza virus would continue to rise in Beijing after October 2024 and reach peak in the second week of 2025, but the peak positive rate would be lower than that of previous influenza season. Conclusions:Compared with the ARIMA model and the VAR model,the ARIMAX model which used meteorological parameters is more suitable for prediction of long-term influenza trend in Beijing. The influenza trend peak was predicted to occur in the second week of 2025, but lower than that in previous influenza season.