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.

Objective: To investigate the characteristics of malignant tumor incidence and mortality in cancer registration areas of Sichuan Province in 2021. Methods: Cancer registration data from 142 registries in Sichuan Province in 2021 were collected via the China Cancer Registry Platform. Crude incidence and crude mortality were calculated. The Chinese population-standardized incidence and world population-standardized incidence were standardized using the age structure of the standard population from the Fifth National Population Census in 2000 and Segi's world standard population. Descriptive analyses examined the distribution of rates by genders, urban/rural areas, and ages, and the ranking of leading cancer sites. Results: In 2021, there were 248 600 new malignant tumor cases reported in Sichuan Province, with a crude incidence of 296.37/100 000. The Chinese population-standardized incidence and world population-standardized incidence were 164.67/100 000 and 160.47/100 000, respectively. There were 158 673 malignant tumor deaths, with a crude mortality of 189.16/100 000. The Chinese population-standardized mortality and world population-standardized mortality were 92.47/100 000 and 92.00/100 000, respectively. The Chinese population-standardized incidence and mortality in males were higher than in females (179.56/100 000 vs. 151.62/100 000, 125.09/100 000 vs. 60.35/100 000). The Chinese population-standardized incidence and mortality in urban areas were higher than in rural areas (175.74/100 000 vs. 157.54/100 000, 93.63/100 000 vs. 91.82/100 000). Both the crude incidence and crude mortality increased with age. The top ten malignant tumors by crude incidence were lung cancer, colorectal cancer, liver cancer, breast cancer, esophageal cancer, gastric cancer, cervical cancer, prostate cancer, thyroid cancer, and corpus uteri cancer, accounting for 76.33% of all new cases. The top ten by crude mortality were lung cancer, liver cancer, esophageal cancer, colorectal cancer, gastric cancer, breast cancer, pancreatic cancer, cervical cancer, prostate cancer, and brain tumors, accounting for 82.39% of all cancer deaths. Conclusions In registration areas of Sichuan Province, the incidence and mortality of malignant tumors are relatively low. Key populations such as males, urban residents, and the elderly require focused prevention and control efforts. Comprehensive measures should be prioritized for malignant tumors including lung cancer, liver cancer, esophageal cancer, gastric cancer, colorectal cancer, and breast cancer.

This paper summarizes Professor SUN Yuanzheng's academic thought and clinical experience in treating diabetic retinopathy (DR) with yuan-primary and luo-connecting meridian-regulating acupuncture. Professor SUN considers that the fundamental cause of DR lies in visceral dysfunction, with stagnation in the ocular collateral vessels and impairment of the mysterious orifices being the core pathogenesis. He proposes the treatment model of "regulating viscera, unblocking ocular collaterals, and opening mysterious orifices" based on yuan-primary and luo-connecting meridian-regulating acupuncture. Yuan-primary and luo-connecting points are used to balance qi, blood, yin, and yang of the viscera. Baihui (GV20) is stimulated with transcranial repetitive acupuncture to activate the yang qi of the governor vessel and invigorate qi in the twelve meridians, improving systemic microcirculation. Combining this with stimulation of ocular areas, Fengchi (GB20), and periocular points, the stasis in the ocular collaterals are cleared and the mysterious orifices are unblocked, addressing both the root and manifestations of DR.
Humans ; Diabetic Retinopathy/physiopathology* ; Acupuncture Therapy/history* ; Meridians ; Acupuncture Points ; Qi

Objective To observe the value of 18F-prostate specific membrane antigen(PSMA)-1007 PET/MRI for diagnosing seminal vesicle invasion(SVI)of prostatic cancer(PCa).Methods Totally 92 male patients with PCa who underwent radical prostatectomy were retrospectively enrolled and divided into positive group(n=26)and negative group(n=66)based on postoperative pathology showed SVI or not.PET/MRI parameters,including maximum standard uptake value(SUVmax),minimum apparent diffusion coefficient(ADCmin),mean apparent diffusion coefficient(ADCmean),SUVmax/ADCmin,SUVmax/ADCmean,PSMA tumor volume(PSMA-TV)and total lesion PSMA(TL-PSMA)were compared between groups.The receiver operating characteristic curve was drawn,and the efficacy of each parameter for diagnosing SVI was analyzed.Results Among 92 cases of PCa,18F-PSMA-1007 PET/MRI showed 30 cases with SVI and 62 cases without SVI,with accuracy of 73.91％,sensitivity of 61.54％,specificity of 78.79％,positive predictive value of 53.33％and negative predictive value of 83.87％.Significant differences of ADCmin,PSMA-TV and TL-PSMA were found between groups(all P＜0.05).The area under the curve(AUC)of SUVmax,ADCmin,ADCmean,SUVmax/ADCmin,SUVmax/ADCmean,PSMA-TV and TL-PSMA for diagnosing SVI of PCa was 0.554,0.341,0.396,0.603,0.581,0.755 and 0.705,respectively.The AUC of PSMA-TV was higher than other parameters except for TL-PSMA,with sensitivity of 84.60％and specificity of 56.10％.Conclusion 18 F-PSMA-1007 PET/MRI was helpful for diagnosing SVI of PCa.

In January 2023, a patient with soft tissue degloving defect of right index, middle, ring and little fingers was treated in the Department of Hand Surgery, Suzhou Ruihua Orthopaedic Hospital. Seven free flaps from a leg were harvested to reconstruct the defected wound of fingers in primary surgery. Flap thinning and plastic surgery were performed in stage-II surgery. Over the 22 months of postoperative follow-up, the flaps in right index, middle, ring and little fingers survived well with the colour and texture close to proximal skin. There was no obvious swelling of the flaps and sensation of the flaps recovered to S 3. The donor sites healed well and the donor leg walked normally.

Objective To control the stepwise release of vascular endothelial growth factor A(VEGF-A)within the microcapsules,and to analyze the effects of the microcapsules on cellular angiogenic capability.Methods VEGF-A encapsulated poly(lactic-co-gly-colic acid)(PLGA)microcapsules were prepared using a method combining dual-channel coaxial injection and continuous flow technol-ogy.The release and degradation performance of the microcapsules were characterized using a phosphate-buffered saline(PBS)soaking method.The biocompatibility of the microcapsules was assessed through the CCK-8 method and Calcein-AM/PI staining method.The impact of microcapsule extract on cellular angiogenesis ability was examined by conducting cell scratch assays and tubule formation ex-periments.Results The microcapsules were round in shape,with their particle diameter measuring in the range of hundreds of mi-crometers.Microcapsules with a molecular weight(Mw)-12 ku can release a large amount of VEGF-A in the initial phase,while Mw-30 ku ones had the capacity to provide a stable,long-term,low-dose release of VEGF-A.Microcapsules of Mw-12 ku exhibited outstanding potential for enhancing the healing of cell scratch wounds in the initial phase.Moreover,within the 0-12 day period,the two types of microcapsule extracts significantly enhanced the ability of cells to form tubules in vitro.Conclusion This study successfully regulated the release profile of VEGF-A by adjusting the molecular weight of PLGA,achieving an initial rapid and substantial release of VEGF-A followed by a sustained slow release over time,while maintaining its biological activity throughout the process.

Objective To develop and validate a machine learning-based predictive model to assess the in-hospital mortality risk of patients with myocardial infarction(MI)complicated by heart failure(HF)undergoing percutaneous coronary intervention(PCI).Methods This retrospective study analyzed MI patients with HF who underwent PCI at The First Affiliated Hospital of Xinjiang Medical University from January 2019 to January 2023.Patient data,including demographic characteristics,vital signs,laboratory test results,imaging parameters and medication use,were collected and randomly divided into a training set(70％)and a validation set(30％).The extreme gradient boosting(XGBoost)model was used to identify variables significantly associated with in-hospital mortality,and the Shapley additive explanations(SHAP)model was applied to assess feature importance.A predictive model was then constructed using univariate and multivariate Logistic regression analyses.Model performance was evaluated using receiver operating characteristic(ROC)curves,area under the curve(AUC)values,calibration curves,and decision curve analysis.Finally,a nomogram was developed for intuitive risk assessment.Results A total of 1 214 MI patients with HF were included in the study,with a median age of 64 years.The in-hospital mortality rate was 7.41％(90 deaths).XGBoost feature selection identified ten key predictive variables:age,myoglobin,albumin,fasting blood glucose,N-terminal pro-B-type natriuretic peptide(NT-proBNP),diabetes mellitus,creatinine,cystatin C,procalcitonin,and left ventricular ejection fraction.Based on these variables,a Logistic regression model was developed,with seven final predictors:age,diabetes mellitus,creatinine,fasting blood glucose,cystatin C,NT-proBNP,and albumin.The model demonstrated high predictive accuracy,with AUC value of 0.869(95％CI:0.84-0.89)in the training set and 0.827(95％CI:0.79-0.85)in the validation set.The calibration curve indicated that the predicted probabilities were consistent with the actual observed outcomes,and decision curve analysis showed that the model had a high net benefit across various decision thresholds.Conclusion This study developed a machine learning-based predictive model incorporating Logistic regression to assess the in-hospital mortality risk of MI patients with HF undergoing PCI.The model demonstrated high predictive performance and clinical utility.The nomogram derived from this model provides an intuitive tool for individualized risk assessment,aiding clinicians in the early identification of high-risk patients,optimizing intervention strategies,and improving patient outcomes.

Objective To construct and validate machine learning-based models to predict the risk of acute kidney injury(AKI)following percutaneous coronary intervention(PCI)in patients with acute ST-segment elevation myocardial infarction(STEMI).Methods A total of 2 315 STEMI patients who underwent PCI between January 2020 and June 2023 were included;306(13.2％)of them developed AKI.Baseline variables were screened using LASSO regression,with the optimal λ value selected via 10-fold cross-validation to identify AKI-associated features.Subsequently,eight distinct machine learning models were constructed and evaluated for their predictive performance.SHAP value analysis was employed to assess the impact of key variables on model predictions.Results LASSO regression identified seven variables significantly associated with AKI,including age,multivessel disease,preoperative creatinine,heart failure,white blood cell count,hemoglobin,and albumin levels.Among all the models,the light gradient boosting machine(LGBM)and extreme gradient boosting(XGB)demonstrated the best predictive performance,with training set AUCs being 0.899(95％CI:0.877-0.921)and 0.893(95％CI:0.868-0.918),and validation set AUCs being 0.809(95％CI:0.763-0.856)and 0.871(95％CI:0.833-0.909),respectively.SHAP analysis revealed that albumin,age,preoperative creatinine,and white blood cell count were the primary contributors to AKI risk.Conclusion This study successfully developed and validated machine learning-based predictive models capable of effectively identifying the risk of AKI following PCI in STEMI patients,thus providing valuable support for clinical decision-making.

In January 2023, a patient with soft tissue degloving defect of right index, middle, ring and little fingers was treated in the Department of Hand Surgery, Suzhou Ruihua Orthopaedic Hospital. Seven free flaps from a leg were harvested to reconstruct the defected wound of fingers in primary surgery. Flap thinning and plastic surgery were performed in stage-II surgery. Over the 22 months of postoperative follow-up, the flaps in right index, middle, ring and little fingers survived well with the colour and texture close to proximal skin. There was no obvious swelling of the flaps and sensation of the flaps recovered to S 3. The donor sites healed well and the donor leg walked normally.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.