As the core vehicle for preserving and transmitting traditional Chinese medicine（TCM） academic thought and clinical experience， the establishment of a robust quality evaluation system for TCM clinical case reports is a crucial component in the current standardization and modernization of TCM. Based on the practical experience of constructing the China Clinical Cases Library of Traditional Chinese Medicine by the China Association of Chinese Medicine， this study conducted a comprehensive analysis of critical challenges， including insufficient authenticity and unfocused evaluation criteria. It proposed a three-dimensional evaluation framework grounded in the structure-process-outcome logic， encompassing three dimensions of authenticity and standardization， characteristics and advantages， application and translational impact. This framework integrated 12 key evaluation indicators in a systematic manner. The model preserved the academic characteristics of TCM syndrome differentiation and treatment， while aligning with modern scientific research standards， achieving a balance between individualized TCM experience and standardized evaluation. Concurrently， this study provided theoretical foundations and methodological guidance for evaluating the quality of TCM clinical cases， contributing significantly to the inheritance of TCM knowledge， evidence-based practice， and the reform of talent evaluation mechanisms.

ObjectiveTo evaluate the efficacy and possible mechanism of Wei's Huoxue Tongluo Formula （韦氏活血通络方，WHTF） in treating atrophic-stage non-arteritic anterior ischemic optic neuropathy （NAION） with qi deficiency and blood stasis. MethodsA total of 82 atrophic-stage NAION patients with qi deficiency and blood stasis were randomly divided into a treatment group and a control group， with 41 cases in each group. The treatment group was given oral administration of WHTF twice a day plus acupoint injection of distilled water 2 ml at Taiyang （EX-HN5） once daily， while the control group received injection of compound anisodine injection 2 ml at Taiyang （EX-HN5） once daily and oral administration of WHTF placebo twice a day. Both groups received treatment for a course of 14 days. The best-corrected visual acuity （BCVA）， optic disc perfusion density （PD）， flux index （FI）， macular superficial PD， vascular density （VD）， and traditional Chinese medicine （TCM） syndrome scores were compared between groups before treatment and on day 7 and day 14 of treatment. Additionally， mean defect （MD） and mean sensitivity （MS） of visual fields were measured before treatment and on day 14， along with safety evaluation. ResultsAfter treatment， both groups showed significant improvement in BCVA， visual field MD and MS， and TCM syndrome scores （P＜0.05 or P＜0.01）. On day 14 of treatment， the TCM syndrome score in the treatment group was significantly lower than that in the control group （P＜0.05）. There was no significant improvement in optic disc PD and FI， and macular superficial PD and VD after treatment in either group （P＞0.05） except that on day 7 the macular superficial foveal PD in the control group was significantly better than that in the treatment group （P＜0.05）. During the treatment period， no serious adverse events occurred in either group. ConclusionWHTF can improve the visual function indicators including visual acuity and visual field， as well as TCM syndrome scores in atrophic-stage NAION patients with qi deficiency and blood stasis. It shows clinical safety， although it does not appear to have a significant effect on optic disc or macular blood flow.

Objective To compare the predictive performance of the deep forest ensemble model,deep forest,and random forest in medical classification.Methods This study proposed a deep forest ensemble model that integrated Sobol-MDA(Sobol-mean decrease accuracy)with the cascading structure of deep forest and feature extraction capabilities of random forest.The model was applied to both simulation and real medical data analysis.The simulation experiments covered scenarios such as unbalanced outcome variables,nonlinear relationships,noise variables,multicollinearity,and interactions.The real data analysis was conducted on parotid MRI data to compare the performance of the models in terms of area under curve(AUC)and other metrics.Results In both the simulation and real data analysis,the deep forest ensemble model demonstrated superiority,especially in complex interaction scenarios,where its predictive performance significantly outperformed deep forest and random forest models.Conclusion Deep forest ensemble model shows significant advantages in addressing complex medical classification tasks.Its predictive performance outperforms traditional models.

Objective:Exploring the efficacy and safety of the combination of programmed cell death protein 1 (PD-1) inhibitors with chemotherapy for the treatment of local recurrence at the primary tumor site of esophageal squamous cell carcinoma (ESCC) following definitive chemoradiotherapy.Methods:Seventy-six patients with local recurrence at the primary tumor site of ESCC following definitive chemoradiotherapy, who were treated at the Cancer Hospital Affiliated with Nanjing Medical University from January 2019 to January 2024. All patients received treatment with a PD-1 inhibitor combined with chemotherapy, and the short-term efficacy, long-term efficacy, and adverse reactions were observed. Univariate and multivariate Cox regression models were employed to identify the factors influencing overall survival (OS) and after-recurrence survival (ARS).Results:Among the 76 patients, 7 achieved partial response, 35 had stable disease, and 34 experienced progressive disease. The objective response rate was 9.2% (7/76), and the disease control rate was 55.3% (42/76). With a median follow-up time of 23.1 months, 33 out of 76 patients died. The median survival time was 38.5 months (95% CI: 29.6-47.3 months); the 1-year, 2-year, and 3-year OS were 94.5%, 66.6%, and 51.7%, respectively. The median ARS was 14.7 months (95% CI: 10.4-19.1 months); the 6-month, 12-month, and 24-month ARS were 85.8%, 59.6%, and 25.7%, respectively. Univariate analysis showed that the initial radiation dose, the Eastern Cooperative Oncology Group (ECOG) performance status of patients after recurrence, the recurrence-free interval (RFI), and the approach to chemotherapy treatment following local esophageal recurrence were factors affecting OS and ARS ( P＜0.05). Multivariate analysis showed that initial radiotherapy dose ( HR=0.268, 95% CI: 0.100-0.720), the ECOG performance status after recurrence ( HR=4.106, 95% CI: 1.228-13.728), and RFI ( HR=0.248, 95% CI: 0.106-0.582) were independent prognostic factors for OS. Additionally, the initial radiation dose ( HR=0.289, 95% CI: 0.098-0.853) and the ECOG performance status after recurrence ( HR=5.143,95% CI:1.404-18.838) were independent prognostic factors for ARS. The incidence of treatment-related adverse-reactions was 85.5% (65/76). Grade 3 or higher treatment-related adverse reactions primarily included anemia in 4 cases, leukopenia in 8 cases, neutropenia in 9 cases, thrombocytopenia in 2 cases, liver function abnormalities in 4 cases, and elevated troponin T in 2 cases. There were no cases of treatment-related mortality. Conclusions:The combination of PD-1 inhibitors with chemotherapy is safe and effective for local recurrence at the primary tumor site of ESCC following definitive chemoradiotherapy and can provide survival benefits for patients. This approach can be considered as a therapeutic option for local recurrence at the primary tumor site of ESCC following definitive chemoradiotherapy.

Traditional Chinese Medicine (TCM), as a treasure of the Chinese nation, plays a significant role in maintaining public health. In 2019, the Central Committee of the Communist Party of China and the State Council proposed for the first time the establishment of a TCM registration and evaluation evidence system that integrates TCM theory, "personal experience" and clinical trials (referred to as the "Three-in-One" System) to promote the inheritance and innovation of TCM. Subsequently, the National Medical Products Administration issued several guiding principles to advance the improvement and implementation of this system. Owing to the complexity of its implementation, there are still differing understandings within the TCM industry regarding the positioning of the "Three-in-One" Registration and Evaluation Evidence System, as well as the connotation and value orientation of the "personal experience." To address this, Academician WANG Qi, President of the TCM Association, China International Exchange and Promotion Association for Medical and Healthcare and TCM master, led a group of academicians, TCM masters, TCM pharmacology experts and clinical TCM experts to convene a "Seminar on Promoting the Implementation of the ′Three-in-One′ Registration and Evaluation Evidence System for Chinese Medicinals." Through extensive discussions, an expert consensus was formed, clarifying the different roles of the TCM theory, "personal experience" and clinical trials within the system. It was further emphasized that the "personal experience" is the core of this system, and its data should be derived from clinical practice scenarios. In the future, the improvement of this system will require collaborative efforts across multiple fields to promote the high-quality development of the Chinese medicinal industry.

ObjectiveTo explore the construction of a machine learning model for the diagnosis of traditional Chinese medicine （TCM） syndromes in chronic cough and the optimization of this model using the Voting ensemble algorithm. MethodsA retrospective analysis was conducted using clinical data from 921 patients with chronic cough treated at the Respiratory Department of Dongfang Hospital， Beijing University of Chinese Medicine. After standardized processing， 84 clinical features were extracted to determine TCM syndrome types. A specialized dataset for TCM syndrome diagnosis in chronic cough was formed by selecting syndrome types with more than 50 cases. The synthetic minority over-sampling technique （SMOTE） was employed to balance the dataset. Four base models， logistic regression （LR）， decision tree （dt）， multilayer perceptron （MLP）， and Bagging， were constructed and integrated using a hard voting strategy to form a Voting ensemble model. Model performance was evaluated using accuracy， recall， precision， F1-score， receiver operating characteristic （ROC） curve， area under the curve （AUC）， and confusion matrix. ResultsAmong the 921 cases， six syndrome types had over 50 cases each， phlegm-heat obstructing the lung （294 cases）， wind pathogen latent in the lung （103 cases）， cold-phlegm obstructing the lung （102 cases）， damp-heat stagnating in the lung （64 cases）， lung yang deficiency （54 cases）， and phlegm-damp obstructing the lung （53 cases）， yielding a total of 670 cases in the specialized dataset. High-frequency symptoms among these patients included cough， expectoration， odor-induced cough， throat itchiness， itch-induced cough， and cough triggered by cold wind. Among the four base models， the MLP model showed the best diagnostic performance （test accuracy： 0.9104； AUC： 0.9828）. Compared with the base models， the Voting ensemble model achieved superior performance with an accuracy of 0.9289 on the training set and 0.9253 on the test set， showing a minimal overfitting gap of 0.0036. It also achieved the highest AUC （0.9836） in the test set， outperforming all base models. The model exhi-bited especially strong diagnostic performance for damp-heat stagnating in the lung （AUC： 0.9984） and wind pathogen latent in the lung （AUC： 0.9970）. ConclusionThe Voting ensemble algorithm effectively integrates the strengths of multiple machine learning models， resulting in an optimized diagnostic model for TCM syndromes in chronic cough with high accuracy and enhanced generalization ability.

Elevated glucose metabolism is a prominent characteristic of fibroblast-like synoviocytes (FLS) in rheumatoid arthritis (RA). However, the efficacy of inhibiting a single target of glucose metabolism in FLS using small molecular inhibitors is limited for RA treatment. Herein, the synergistic inhibition of FLS' survival, proliferation, and activation by combining two glucose metabolism inhibitors, diclofenac (DC) and lonidamine (LND) was first verified. Subsequently, DC and LND were individually conjugated to cystamine-modified hyaluronic acid (HA) to prepare two polymer-prodrug conjugates. A HAP-1 peptide-modified dual polymer-prodrug conjugates-assembled nanoparticles system (^HAP-1NP_DC+LND) was further tailored in the optimal synergistic ratio for targeted and synergistic metabolic modulation of FLS to alleviate RA symptoms. Upon targeted uptake by FLS in inflamed joints, ^HAP-1NP_DC+LND released DC and LND within the intracellular reductive microenvironment, where DC hinders glucose uptake and LND suppresses glycolytic enzymes to eliminate FLS synergistically. Additionally, the secretion of lactic acid and pro-inflammatory factors from FLS were reduced, thereby disrupting the crosstalk between FLS and pro-inflammatory macrophages. Finally, ^HAP-1NP_DC+LND demonstrated promising efficacy in a mouse model of collagen-induced arthritis (CIA). Overall, this research provides valuable insights into novel therapeutic strategies for the safe and effective of treatment RA through targeted and synergistic metabolic modulation of FLS.

ObjectiveTo investigate the relationship between the composition of traditional Chinese medicine (TCM) and depression/anxiety/sleep disturbances (D/A/S) in patients with chronic pain.MethodsThis cross-sectional study was conducted at 13 tertiary hospitals across China, enrolling patients who experienced chronic pain between November 2023 and May 2024. The Patient Health Questionnaire-9, Generalized Anxiety Disorder-7, Pittsburgh Sleep Quality Index, and TCM constitution categories were used to assess the patients. The association between the TCM constitution and the D/A/S ratio was analyzed using multivariable logistic regression.ResultsA total of 1107 patients (63.2% women) were analyzed. Compared with those with a balanced constitution, patients who had qi-deficiency and yin-deficiency were at a higher risk of depression. Qi-deficiency and yin-deficiency were associated with anxiety. Sleep disturbances were common in patients with qi-deficiency constitution (odds ratio [OR]: 2.32, 95% confidence interval [CI]: 1.42–3.81), yang-deficiency constitution (OR: 1.94, 95% CI: 1.26–2.98), yin-deficiency constitution (OR: 2.03, 95% CI: 1.24–3.32), blood stasis constitution (OR: 2.07, 95% CI: 1.01–4.22), and qi-stagnation constitution (OR: 2.66, 95% CI: 1.35–5.25).ConclusionIn patients with chronic pain, specific TCM constitutions may be associated with D/A/S. Further longitudinal studies are needed to clarify the potential causal relationships between TCM constitution types and these conditions.

ObjectiveBased on ultra performance liquid chromatography-quadrupole-time-of-flight mass spectrometry（UPLC-Q-TOF-MS/MS）， the combination of serum pharmacochemistry， response profile of absorbed components in serum， network pharmacology and drug-likeness prediction was used to screen the potential active ingredients of Yanghetang against breast cancer. MethodsUPLC-Q-TOF-MS/MS was used to identify the main components in different solvent extracts of Yanghetang， and serum pharmacochemistry was applied to analyze the absorbed components from the serum of female SD rats after 0.5， 1， 2 h of administration. Combined with the response characteristic values of serum drug components obtained from UNIFI 1.8.2， the absorbed prototype components and metabolites were screened to get the absorbed components of Yanghetang with a significant patterns of elimination and growth. Network pharmacology was applied to construct a drug-component-pathway-target-disease network， and molecular docking was performed between absorbed components and key targets of breast cancer， and the drug similarity was analyzed by SwissADME. ResultsForty-two compounds were identified in Yanghetang samples extracted with different solvents， of which 16 compounds were common to the three different extraction solvents（methanol， 50% methanol and water）. The results of drug-containing serum analysis showed that there were 16 absorbed components in serum， including 5 prototypes and 11 metabolites. Network pharmacology results showed that Yanghetang against breast cancer involved 15 key targets such as proto-oncogene tyrosine-protein kinase Src（SRC）， epidermal growth factor receptor（EGFR） and phosphoinositide 3 kinase catalytic alpha polypeptide（PIK3CA）. Molecular docking results showed that 16 potential active ingredients were well combined with the predicted targets. Combined with drug likenesses， 12 compounds in the absorbed components of Yanghetang were considered to have potential for anti-breast cancer activity， mainly including α-pinene and γ-eudesmol and their metabolites， of which one was from Ephedrae Herba， one was from Rehmanniae Radix， and eight were from Cinnamomi Cortex. ConclusionThe chemical components of Yanghetang mainly include polysaccharides， monoterpene glycosides and coumarins， and its prototype components mainly undergo oxidation， hydrolysis and acetylation after entering the blood. Its anti-breast cancer mechanism may be related to the regulation of signaling pathways such as the mitogen-activated protein kinase（MAPK） and phosphatidylinositol 3-kinase/protein kinase B（PI3K/Akt）. The results of this study can lay a foundation for further exploration of Yanghetang in the treatment of breast cancer.

Objective To compare the predictive performance of the deep forest ensemble model,deep forest,and random forest in medical classification.Methods This study proposed a deep forest ensemble model that integrated Sobol-MDA(Sobol-mean decrease accuracy)with the cascading structure of deep forest and feature extraction capabilities of random forest.The model was applied to both simulation and real medical data analysis.The simulation experiments covered scenarios such as unbalanced outcome variables,nonlinear relationships,noise variables,multicollinearity,and interactions.The real data analysis was conducted on parotid MRI data to compare the performance of the models in terms of area under curve(AUC)and other metrics.Results In both the simulation and real data analysis,the deep forest ensemble model demonstrated superiority,especially in complex interaction scenarios,where its predictive performance significantly outperformed deep forest and random forest models.Conclusion Deep forest ensemble model shows significant advantages in addressing complex medical classification tasks.Its predictive performance outperforms traditional models.