Employing bibliometric methods and adhering to principles of textual research， this study systematically investigated prescription source， formula name， composition evolution， dose evolution， origin， processing， ancient and modern applications of Xiaofengsan. Xiaofengsan， also known as Renshen Xiaofengsan and Chantui Xiaofengsan， was first recorded in the Taiping Huimin Hejijufang（hereafter referred to as Jufang） of the Southern Song dynasty. The formula composition included Schizonepetae Spica， Glycyrrhizae Radix et Rhizoma， Chuanxiong Rhizoma， Notoptery Rhizoma et Radix， Bombyx Batryticatus， Saposhnikoviae Radix， Poria， Cicadae Periostracum， Pogostemonis Herba， Ginseng Radix et Rhizoma， Magnoliae Officinalis Cortex and Citri Reticulatae Pericarpium， a total of 12 medicinal materials. In terms of the evolution of formula composition， formulas across dynasties largely aligned with those recorded in Jufang， with only minor variations in application. The results of the formula dosage research indicated that one dose of medication in Jufang corresponded to the following modern dosages：Schizonepetae Spica of 82.6 g， Glycyrrhizae Radix et Rhizoma of 82.6 g， Chuanxiong Rhizoma of 82.6 g， Notoptery Rhizoma et Radix of 82.6 g， Bombyx Batryticatus of 82.6 g， Saposhnikoviae Radix of 82.6 g， Poria of 82.6 g， Cicadae Periostracum of 82.6 g， Pogostemonis Herba of 82.6 g， Ginseng Radix et Rhizoma of 82.6 g， Magnoliae Officinalis Cortex of 20.65 g and Citri Reticulatae Pericarpium of 20.65 g， the origins of all the constituent drugs were consistent with the 2020 edition of Pharmacopoeia of the People's Republic of China. The results of the investigation into the decoction method indicated that the aforementioned drugs should be finely ground into powder（pass through the No.5 sieve）， and 8.26 g was taken for each dose， which was taken with the clear liquid obtained by steeping tea leaves in boiling water for several minutes. This mixture was administered three times daily， 30 min after meals. The ancient functional indications of this formula mainly involved dispelling wind-heat， eliminating pathogenic factors and regulating the middle Jiao. It primarily treated all wind-heat syndromes manifesting as skin diseases， predominantly affecting the upper body， especially the head and face. The diseases involved in modern applications were mostly dermatological diseases， including urticaria， eczema， atopic dermatitis and others. In this paper， by combing the relevant ancient literature， the key information of Xiaofengsan was textual researched， in order to provide reference for the modern application and development of this formula.

With the rapid advancement of artificial intelligence (AI) technology, particularly breakthroughs in large models, AI applications in healthcare are becoming increasingly widespread.Ophthalmology, as a critical branch of medicine, has emerged as a clinical specialty with comprehensive AI research and applications, especially in the AI-driven analysis of ophthalmic imaging.Currently, AI in ophthalmology is undergoing profound transformation driven by large model technology.AI large models, with their robust data comprehension and multimodal interaction capabilities, offer new possibilities for the diagnosis, treatment, and health education of ophthalmic diseases, powerfully propelling the evolution of current AI paradigms in ophthalmology.This article explores the novel opportunities brought by the extensive application of AI large models in ophthalmic research and practice, and provides recommendations for ophthalmologists and insights for industry development.

With the rapid advancement of artificial intelligence (AI) technology, particularly breakthroughs in large models, AI applications in healthcare are becoming increasingly widespread.Ophthalmology, as a critical branch of medicine, has emerged as a clinical specialty with comprehensive AI research and applications, especially in the AI-driven analysis of ophthalmic imaging.Currently, AI in ophthalmology is undergoing profound transformation driven by large model technology.AI large models, with their robust data comprehension and multimodal interaction capabilities, offer new possibilities for the diagnosis, treatment, and health education of ophthalmic diseases, powerfully propelling the evolution of current AI paradigms in ophthalmology.This article explores the novel opportunities brought by the extensive application of AI large models in ophthalmic research and practice, and provides recommendations for ophthalmologists and insights for industry development.

Objective:To investigate the influencing factors of secondary type 2 diabetes in young obesity patients, and construct and validate a risk prediction model.Methods:The retrospective cohort study was conducted. The clinical data of 847 young obesity patients who were admitted to The First Affiliated Hospital of Nanjing Medical University from January 2022 to July 2024 were collected. There were 382 males and 465 females, aged (29.4±3.8)years. Patients were randomly divided into a training set of 593 cases and a validation set of 254 cases based on a random number table method of 7∶3 ratio. The training set was used to construct the prediction model, and the validation set was used to validate prediction model. Observation indicators: (1) analysis of influencing factors of secondary type 2 diabetes in young obesity patients; (2) construc-tion and validation of a prediction model for secondary type 2 diabetes in young obesity patients. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test. Univariate analysis was performed using the corresponding statistical methods based on data types. Multivariate analysis was performed using the Logistic regression model, and the area under the curve (AUC) of receiver operating characteristic (ROC) curve, the Hosmer-Lemeshow test, the calibration curve and decision curve were used to evaluate the predictive performance of the model. Results:(1) Analysis of influencing factors of secondary type 2 diabetes in young obesity patients. Of the 847 young obesity patients, there were 238 patients with secondary type 2 diabetes, including 161 cases in the training set and 77 cases in the validation set, 609 patients of simple obesity, including 432 cases in the training set and 177 cases in the validation set. Results of multivariate analysis showed that family history of diabetes, hypertension, high-sugar diet, exercise habits, triglyceride (TG), homeostasis model assessment of insulin resistance (HOMA-IR) and neutrophil-to-lymphocyte ratio (NLR) were independent factors influencing secondary type 2 diabetes in young obesity patients [ odds ratio=9.476, 2.420, 3.219, 0.272, 2.137, 26.759, 41.535, 95% confidence interval ( CI) as 3.242-27.696, 1.159-5.052, 1.525-6.796, 0.117-0.632, 1.019-4.481, 12.907-55.476, 16.085-107.251, P<0.05]. (2) Construction and validation of a prediction model for secondary type 2 diabetes in young obesity patients. A nomogram prediction model for secondary type 2 diabetes in young obesity patients was constructed based on the results of multivariate analysis. Results of ROC curve analysis showed that the AUC of prediction model for the training set was 0.963(95% CI as 0.946-0.980), with sensitivity of 89.6% and specificity of 93.2%, respectively, and the AUC of prediction model for the validation set was 0.966(95% CI as 0.944-0.988), with sensitivity of 92.7% and specificity of 88.3%, respectively. Results of Hosmer-Lemeshow test showed that the P-values for both the training set and validation set were >0.05, indicating good model fit. The calibration curves for both the training set and validation set closely matched the actual curve, demonstrating the prediction model with a good fit. The decision curve analysis showed high practical value of the model. Conclusions:Family history of diabetes, hypertension, high-sugar diet, exercise habits, TG, HOMA-IR and NLR are independent factors influencing secondary type 2 diabetes in young obesity patients. The prediction model constructed based on these factors demons-trates good predictive performance.

Objective:To investigate the influencing factors of secondary type 2 diabetes in young obesity patients, and construct and validate a risk prediction model.Methods:The retrospective cohort study was conducted. The clinical data of 847 young obesity patients who were admitted to The First Affiliated Hospital of Nanjing Medical University from January 2022 to July 2024 were collected. There were 382 males and 465 females, aged (29.4±3.8)years. Patients were randomly divided into a training set of 593 cases and a validation set of 254 cases based on a random number table method of 7∶3 ratio. The training set was used to construct the prediction model, and the validation set was used to validate prediction model. Observation indicators: (1) analysis of influencing factors of secondary type 2 diabetes in young obesity patients; (2) construc-tion and validation of a prediction model for secondary type 2 diabetes in young obesity patients. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test. Univariate analysis was performed using the corresponding statistical methods based on data types. Multivariate analysis was performed using the Logistic regression model, and the area under the curve (AUC) of receiver operating characteristic (ROC) curve, the Hosmer-Lemeshow test, the calibration curve and decision curve were used to evaluate the predictive performance of the model. Results:(1) Analysis of influencing factors of secondary type 2 diabetes in young obesity patients. Of the 847 young obesity patients, there were 238 patients with secondary type 2 diabetes, including 161 cases in the training set and 77 cases in the validation set, 609 patients of simple obesity, including 432 cases in the training set and 177 cases in the validation set. Results of multivariate analysis showed that family history of diabetes, hypertension, high-sugar diet, exercise habits, triglyceride (TG), homeostasis model assessment of insulin resistance (HOMA-IR) and neutrophil-to-lymphocyte ratio (NLR) were independent factors influencing secondary type 2 diabetes in young obesity patients [ odds ratio=9.476, 2.420, 3.219, 0.272, 2.137, 26.759, 41.535, 95% confidence interval ( CI) as 3.242-27.696, 1.159-5.052, 1.525-6.796, 0.117-0.632, 1.019-4.481, 12.907-55.476, 16.085-107.251, P<0.05]. (2) Construction and validation of a prediction model for secondary type 2 diabetes in young obesity patients. A nomogram prediction model for secondary type 2 diabetes in young obesity patients was constructed based on the results of multivariate analysis. Results of ROC curve analysis showed that the AUC of prediction model for the training set was 0.963(95% CI as 0.946-0.980), with sensitivity of 89.6% and specificity of 93.2%, respectively, and the AUC of prediction model for the validation set was 0.966(95% CI as 0.944-0.988), with sensitivity of 92.7% and specificity of 88.3%, respectively. Results of Hosmer-Lemeshow test showed that the P-values for both the training set and validation set were >0.05, indicating good model fit. The calibration curves for both the training set and validation set closely matched the actual curve, demonstrating the prediction model with a good fit. The decision curve analysis showed high practical value of the model. Conclusions:Family history of diabetes, hypertension, high-sugar diet, exercise habits, TG, HOMA-IR and NLR are independent factors influencing secondary type 2 diabetes in young obesity patients. The prediction model constructed based on these factors demons-trates good predictive performance.

OBJECTIVES: This study aimed to explore the active components, potential targets, and mechanism of Cnidii Fructus in the treatment of periodontitis with osteoprosis through network pharmacology, molecular docking, and molecular dynamics simulation technology. METHODS: The main chemical constituents and targets of Cnidii Fructus were screened using the TCMSP and SwissTargetPrediction databases, as well as literature reports. Targets of periodontitis and osteoporosis were predicted using different databases. The intersection targets of Cnidii Fructus, periodontitis, and osteoporosis were obtained using Venny 2.1. The protein-protein interaction network was formed on the STRING platform. Cytoscape 3.9.1 was used to construct the active component-intersection target interaction network, perform the topological analysis, and screen key targets and core active components. Furthermore, the Metascape database was used to perform gene ontology (GO) function and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis on the intersection targets. The top five key targets and core active components were selected as receptor proteins and ligand small molecules. Discovery Studio 2019 was used to dock ligands and receptors and visualize the docking results. Molecular dynamics simulation was conducted using Gromacs2022.3 to assess the stability of the interactions between the core active components and the main targets. RESULTS: A total of 20 potential active ingredients of Cnidii Fructus were screened, and 116 targets of Cnidii Fructus were obtained for treating periodontitis and osteoporosis. GO and KEGG analyses of the 116 targets showed that Cnidii Fructus may play a therapeutic role through the phosphoinositide 3-kinase-protein kinase B (PI3K-Akt) and advanced glycation end products-receptor for advanced glycation end products (AGE-RAGE) signaling pathways. Molecular docking showed that the core constituents were well bound to the main targets. Molecular dynamics simulations confirmed the stability of the Diosmetin-AKT1 complex system. CONCLUSIONS The preliminary discovery of the potential molecular pharmacological mechanism of Cnidii Fructus extract in the targeted treatment of periodontitis with osteoporosis through a multi-component, multitarget, and multi-pathway approach can serve as a theoretical foundation for future drug-development research and clinical application.
Molecular Docking Simulation ; Molecular Dynamics Simulation ; Network Pharmacology ; Periodontitis/complications* ; Drugs, Chinese Herbal/chemistry* ; Osteoporosis/complications* ; Humans ; Protein Interaction Maps ; Cnidium/chemistry*

Objective To explore the application of psychological stress theory in endoscopic dilatation in patients with esophageal stricture.Methods A total of 110 patients who underwent endoscopic dilatation for esophageal stricture in Jiangsu Provincial People's Hospital from September 2019 to January 2021 were selected as research objects.They were divided into control group and observation group according to random number table method,with 55 cases in each group.The control group implemented conventional psychological intervention,and the observation group implemented conventional psychological intervention and the intervention based on psychological stress theory.The level of stress disorder,psychological resilience,and rehabilitation status were evaluated by the Post Traumatic Stress Disorder Self-Rating Scale(PTSD-SS),Connor-Davidson Resilience Scale(CD-RISC),and Morningside Rehabilitation Status Scale(MRSS)before intervention,on the day 3 of intervention,and 1 week after intervention.Results Repeated measure ANOVA showed that there were time,group,time-group interactions in the scores of PTSD-SS,CD-RISC,and MRSS in patients undergoing endoscopic dilatation for esophageal stricture when psychological intervention based on psychological stress theory was applied(P<0.05).Conclusion The intervention based on psychological stress theory can relieve the stress disorder in patients undergoing endoscopic dilatation for esophageal stricture,restore the psychological resilience,and promote postoperative recovery.

The clinical decision in bariatric and metabolic surgery is influenced by several factors. Both patients and medical providers face conflicts when deciding whether to proceed with the surgery and which procedure to choose. Some clinical decision-making studies have provided high-level evidence to assist in selecting patients and surgical methods. Additionally, there are clinical decision support systems tailored to the needs of this specialty. However, there is currently a lack of a high-level clinical decision system for bariatric and metabolic surgery in China. The authors refer to previous research progress and conduct in-depth discussions on the clinical decision-making of bariatric and metabolic surgery based on patient selection and surgical method selection.

Objective:To investigate the surgical decision-making types and its influencing factors for obesity patients participating in bariatric metabolic surgery.Methods:The survey targets were patients who were scheduled to bariatric metabolic surgery in The First Affiliated Hospital of Nanjing Medical University, and the survey period was from January 1 to May 30, 2024. The survey was conducted using the general demographic questionnaire, control preference scale, and shared-decision requirements questionnaire for bariatric metabolic surgery. Count data were expressed as absolute numbers and percentages, and comparison between groups was performed using the chi-square test. Comparison of ordinal data was performed using the non parametric test. Univariate analysis was performed using the corresponding statistical methods based on data types. Multivariate analysis was conducted using the willingness of patients to participate in bariatric metabolic surgery decision-making (passive decision-making=1, shared decision-making=2, active decision-making=3) as the dependent variable, and the statistically significant variables in univariate analysis were included as independent variables for disordered multi-class Logistic regression analysis.Results:(1) Results of survey. A total of 568 questionnaires were distributed and collected. After removing 48 unqualified questionnaires, 520 valid questionnaires were collected. Of the 520 patients who completed the questionnaire survey, there were 231 cases participating as the passive decision-making type, 140 cases as shared decision-making type, and 149 as active decision-making type in bariatric metabolic surgery decision-making. (2) Influencing factors for decision-making type of obesity patients participating in bariatric metabolic surgery. Results of multivariate analysis showed that taking the shared decision-making type as a reference, the number of complication (0 compared to ≥4, 1?3 compared to ≥4), medical payment method (medical insurance compared to self-payment), degree of disease understanding (not very understanding compared to general understanding, not understanding compared to general understanding), and the interval between knowing and accep-ting surgery (1-3 months compared to >6 months) were independent factors influencing the willingness of passive decision-making patients to participate in bariatric metabolic surgery deci-sions ( odds ratios=3.520, 2.457, 2.255, 3.147, 1.920, 1.854, 95% confidence interval as 1.552-7.984, 1.215-4.968, 1.335-3.809, 1.865-5.311, 1.025-3.596, 1.065-3.230, P<0.05). Body mass index (BMI) (28.0-31.9 kg/cm 2 compared to ≥37.0 kg/cm 2, 32.0-36.9 kg/cm 2 compared to ≥37.0 kg/cm 2), number of complication (0 compared to ≥4, 1?3 compared to ≥4), family and social support status (poor compared to good), and the interval between knowing and accepting surgery (1?3 months compared to >6 months) were independent factors influencing the willingness of passive decision-making patients to participate in bariatric metabolic surgery decisions ( odds ratios=2.391, 2.478, 6.918, 3.335, 2.974, 2.139, 95% confidence intervals as 1.207-4.735, 1.345-4.563, 2.498-19.159, 1.350-8.242, 1.755-5.039, 1.156-3.957, P<0.05). Taking the passive decision-making type as a reference, BMI (28.0-31.9 kg/cm 2 compared to ≥37.0 kg/cm 2, 32.0-36.9 kg/cm 2 compared to ≥37.0 kg/cm 2) and family social support status (poor compared to good) were independent factors influencing the willingness of passive decision-making patients to participate in bariatric metabolic surgery decisions ( odds ratios=0.404, 0.554, 0.336, 95% confidence interval as 0.221-0.740, 0.327-0.938, 0.212-0.534, P<0.05). (3) Analysis of decision support needs for bariatric metabolic surgery. Results of shared-decision requirements questionnaire showed that the information needs of obesity patients from high to low were surgical indications, postoperative physical changes and dietary habits adjust-ments, surgical costs, surgical complications and risks, and surgical outcomes. The demand for content preference from high to low were in the form of sharing patient experience after bariatric metabolic surgery, comparing before and after bariatric metabolic surgery, popularizing science after bariatric metabolic surgery, introducing surgical methods, live streaming of medical staff, and introducing the hospital environment. In terms of recognition of networked support pathways, 94.04%(489/520) of patients believed that implementing shared decision support based on networked pathways was reliable. Conclusions:Obesity patients are more willing to participate in bariatric metabolic surgery decision-making. But the proportion of patients selecting passive decision-making is relatively high. BMI, number of complication, medical payment method, degree of disease under-standing, family and social support status and the interval between knowing and accepting surgery are independent factors influencing the willingness of obesity patients to paticipate in bariatric metabolic surgery decisions.

ObjectiveTo observe the effectiveness and safety of the Chinese herbal medicine Mingshi Granules （明视方颗粒） for low myopia in children with heart yang insufficiency. MethodsA multicentre， prospective， double-blind randomised controlled study was conducted， in which 290 children with low myopia from 8 centres were randomly divided into 145 cases in the treatment group and 145 cases in the control group， and the treatment group was given education， dispensing glasses， and Chinese herbal medicine Mingshi Granules， while the control group was given education， dispensing glasses， and granules placebo. Both Mingshi Granules and placebo granules were taken orally， 1 bag each time， twice daily， 4 weeks of oral intake and 2 weeks of rest as 1 course of treatment， a total of 4 courses of treatment （24 weeks）. Equivalent spherical lenses， best naked-eye distance visual acuity， ocular axis， corneal curvature K1， adjustment amplitude， traditional Chinese medicine （TCM） symptom scores， calculate the amount of progression of equivalent spherical lenses， were observed at the 12th and the 24th week of treatment， at the 36th week and 48th week of follow-up， resectively， the control rate of myopia progression was evaluated at the 24th week， and safety indexes were observed before treatment. ResultsThe amount of progression of equivalent spherical lenses was lower in the treatment group than in the control group at the 48-week follow-up （P＜0.05）. The control rate of myopia progression at 24 weeks after treatment in the treatment group was higher （57.60%， 72/125） than that in the control group （44.63%， 54/121） （P＜0.05）. The best naked-eye distance visual acuity at 36-week follow-up in the treatment group was higher than that in the control group （P＜0.05）. Equivalent spherical lenses were significantly lower in both groups at all observation time points compared with pre-treatment （P＜0.05）， and were higher in the treatment group than in the control group at the 48-week follow-up （P＜0.05）. The ocular axes of both groups were significantly higher at each observation time point after treatment and at follow-up compared with before treatment （P＜0.05）. The amount of eye axis growth in the treatment group was lower than that in the control group at 24 weeks after treatment and at the 48-week follow-up （P＜0.05）. Corneal curvature K1 was significantly lower in the treatment group at the 24th week of treatment compared to pre-treatment （P＜0.05）. The magnitude of adjustment in the treatment group was significantly higher at the 36-week follow-up and at the 48-week follow-up than before treatment （P＜0.05）. The scores of white/dark complexion， white coating thin pulse， fatigue and total TCM symptom scores of children in both groups at the 12th， 24th， 36th and 48th weeks of follow-up were significantly lower than those before treatment （P＜0.05）； the scores of blurred vision at the 24th and 36th weeks of follow-up were significantly lower than those before treatment （P＜0.05）； and the scores of blurred vision in the treatment group at the 48th week of follow-up were signi-ficantly lower than those before treatment （P＜0.05）. In the treatment group， the score of fatigue was higher than that of the control group at the 36-week follow-up， and the score of blurred vision was lower than that of the control group at the 48-week follow-up （P＜0.05）. No adverse reactions or obvious abnormalities of the safety indexes were observed of the two groups during the treatment. ConclusionChinese herbal medicine Mingshi Granules showed the effect of controlling the progression of low myopia， improving the best naked eye distance visual acuity， slowing down the growth of the eye axis， improving some of the TCM symptoms， with good safety.