To explore the advantages of traditional Chinese medicine （TCM） and integrative TCM-Western medicine approaches in the treatment of diabetic microvascular complications （DMC）， refine key pathophysiological insights and treatment principles， and promote academic innovation and strategic research planning in the prevention and treatment of DMC. The 38th session of the Expert Salon on Diseases Responding Specifically to Traditional Chinese Medicine， hosted by the China Association of Chinese Medicine， was held in Beijing， 2024. Experts in TCM， Western medicine， and interdisciplinary fields convened to conduct a systematic discussion on the pathogenesis， diagnostic and treatment challenges， and mechanism research related to DMC， ultimately forming a consensus on key directions. Four major research recommendations were proposed. The first is addressing clinical bottlenecks in the prevention and control of DMC by optimizing TCM-based evidence evaluation systems. The second is refining TCM core pathogenesis across DMC stages and establishing corresponding "disease-pattern-time" framework. The third is innovating mechanism research strategies to facilitate a shift from holistic regulation to targeted intervention in TCM. The fourth is advancing interdisciplinary collaboration to enhance the role of TCM in new drug development， research prioritization， and guideline formulation. TCM and integrative approaches offer distinct advantages in managing DMC. With a focus on the diseases responding specifically to TCM， strengthening evidence-based support and mechanism interpretation and promoting the integration of clinical care and research innovation will provide strong momentum for the modernization of TCM and the advancement of national health strategies.

Objective:This multicenter retrospective study aimed to analyze the clinicopathological features of duodenal adenocarcinoma (DA) and identify prognostic factors for postoperative survival.Methods:Demographic characteristics, clinicopathological features, treatment outcomes and survival of DA patients undergoing surgical treatment at 18 Chinese medical centers from January 2012 to December 2023 were retrospectively analyzed.Results:Among the 2 056 DA patients included, 46.8% (963) had extra-ampullary DA (EA-DA), and 53.2% (1 093) had peri-ampullary DA (PA-DA). The 1-, 3-, and 5-year overall survival (OS) rates for patients who underwent radical surgery were 93.2%, 71.0%, and 57.2%, respectively. The median overall survival was 76 months, and the median progression-free survival (PFS) was 65 months. No differences in survival were observed between the laparotomy group and minimally invasive surgery (MIS) group either before or after propensity score matching (OS: 76 vs. 75 months before PSM, P=0.986; OS: 75 vs. 75 months after PSM, P=0.602). Furthermore, there were no significant differences between-group in operation time and postoperative complications ( P＞0.05). The MIS group experienced less intraoperative blood loss and shorter hospital stays. The multivariate Cox regression analysis revealed that advanced age ( HR=1.43,95% CI:1.18-1.73), elevated carbohydrate antigen 19-9 levels ( HR=1.24,95% CI:1.02-1.51), perineural invasion ( HR=1.44,95% CI:1.14-1.81), vascular invasion ( HR=1.35,95% CI:1.07-1.71), advanced T stage (T3-4 vs. T1-2: HR=1.86,95% CI:1.49-2.31), regional lymph node metastasis ( HR=1.93,95% CI:1.58-2.36), preoperative biliary drainage ( HR=1.26,95% CI:1.04-1.53), intraoperative blood loss ( HR=1.34,95% CI:1.11-1.62), clinically significant postoperative pancreatic fistulas ( HR=1.53,95% CI:1.12-2.09), and postoperative hemorrhage ( HR=1.62,95% CI:1.14-2.29) were independent risk factors for poor prognosis after surgery (all P＜0.05). Conclusions:Radical surgery is associated with favorable overall survival among DA patients, and no difference in survival is observed between EA-DA and PA-DA patients. MIS is a reliable alternative for DA treatment.

Objective:To address the critical issue of missing dynamic border molding information in edentulous direct digital impression technology, this study explores innovative digital solutions and conducts preliminary application validation.Methods:Based on the myostatic line theory, a methodology was established: intraoral scanner (IOS) high-frequency video was utilized to dynamically capture functional molding data of soft tissues, integrated with a self-developed mobility gradient recognition algorithm to achieve dynamic threshold segmentation between the muscle dynamic zone and myostatic zone, termed "optical digital molding technology". Ten edentulous patients with well-fitting complete dentures, treated at the Department of Prosthodontics, Peking University School and Hospital of Stomatology from January 2024 to December 2024, were enrolled. The standard deviation between the muscle static line (generated by mobility gradient algorithm with thresholds of 0.3-0.7 mm) and the denture border curve was analyzed to optimize the dynamic threshold, followed by single-case clinical validation.Results:Among the mobility thresholds of 0.3-0.7 mm, the 0.5 mm threshold yielded the smallest standard deviation between the myostatic line and denture border. Clinical validation demonstrated that dentures designed with this threshold exhibited no displacement during dynamic functional tests, with marginal sealing meeting clinical standards.Conclusions:The optical digital border molding technique for edentulous soft tissue boundaries translates the myostatic line theory into quantifiable parameters for the first time. Based on data from 10 cases, a mobility threshold of 0.5 mm is recommended for clinical application.

In the field of substance detection,ion dissociation techniques have become crucial for enhancing qualitative accuracy.By applying external energy to induce dissociation of ions in the substance being analyzed,the internal structural information can be obtained,thereby improving qualitative capabilities.Current research on ion dissociation techniques primarily focuses on tandem mass spectrometry,which typically requires a vacuum environment.However,research on ambient ion dissociation techniques is less developed,with some progress made in the field of tandem ion mobility spectrometry.Recently,the development of field-induced dissociation(FID)in this area has enabled ambient dissociation of various explosive and volatile alcohol ions.Nevertheless,the limitation imposed by the maximum breakdown field of air restricts the energy of the electric field,making it challenging to dissociate ions with high energy requirements,such as those of drugs.To address this issue,in this work,an ambient heat-induced dissociation(HID)technique based on high temperatures was proposed,in which an ambient ion heat-induced dissociation unit was developed and integrated into a home-made ion trap mass spectrometer.Experiments were conducted on four representative drug samples,e.g.methamphetamine,heroin,cocaine,and ketamine.The parent ions mass spectra,low vacuum collision-induced dissociation(CID)mass spectra and ambient HID mass spectra for each sample were obtained.By analyzing and comparing the fragmentation products from ambient and low vacuum dissociation,the feasibility of the ambient HID technique was verified.This technique provided a method for ion dissociation in single mass analyzers without tandem mass spectrometry capability and offered a new research direction for the future development of tandem ion mobility spectrometry.

Aim To clarify the mechanism by which Qishen Yixin Granules improved microcirculation vas-cular endothelial dysfunction(VED)in mice,through activating the Nrf2/HO-1 signaling pathway to regulate oxidative stress.Methods C57 mice were randomly divided into six groups:blank group,model group,pos-itive drug group,and low-,medium-,and high-dose groups of Qishen Yixin Granules.The VED model was established by long-term infusion of Ang Ⅱ combined with a high-fat diet.Each treatment group received the corresponding drug intervention.After four weeks of drug intervention,cardiac function was assessed by echocardiography.Carstairs staining was used to ob-serve the formation of microthrombi in myocardial tis-sue.The micro vascular ischemia was evaluated by Hei-denhain staining.The ultrastructure of endothelial cells was observed by electron microscopy.The levels of EMPs,ROS,NO,ET-1,TF,TM,VWF,and TXA2 in serum were measured by ELISA.The expression levels of MDA,SOD,and GSH-Px in mouse heart tissue were determined by chemical methods.Cardiac microvascu-lar density and the expression of Nrf2,Keap1,and HO-1 proteins were detected by Immunohistochemical stai-ning.The protein expressions of Keap1,cytoplasmic Nrf2,nuclear Nrf2,and HO-1 in myocardial tissue were detected by Western blot.Results Qishen Yixin Granules could effectively improve the cardiac function of mice,alleviate the damage of endothelial cells and endothelial function.They could up-regulate serum NO levels and the activities of antioxidant enzymes SOD and GSH-Px,while down-regulating the expression of ROS and vascular inflammatory injury factors such as ET-1,VWF,TXA2,TF,TM,and EMPs.Qishen Yixin Granules also increased the positive counts of CD34,Nrf2,and HO-1,as well as microvessel density.Fur-thermore,they inhibited the expression of MDA,Keap1,and cytoplasmic Nrf2 protein in myocardial tis-sue,while increasing the expression of nuclear proteins HO-1 and Nrf2.Conclusions Qishen Yixin Granules may inhibit oxidative stress and inflammatory response by regulating the Nrf2/HO-1 signaling pathway,thereby improving vascular endothelial damage and cardiac function in VED mice.

Mitochondria are organelles in eukaryotic cells that play a crucial role in cellular energy me-tabolism,oxidative stress,heat production,and signal transduction.Mitochondrial transplantation(MT)is currently one of the most advanced techniques for treating mitochondrial dysfunction and anti-aging re-search.This study aimed to explore the feasibility and effectiveness of xenogeneic MT by transplanting mitochondria from yak(Bos grunniens),domestic cattle(Bos taurus),and horse(Equus caballus)into mice(Mus musculus).The results demonstrated that mitochondria from yak,domestic cattle,and horse could be successfully transplanted into mice and maintained in various tissues and organs of the mice for at least 14 days,as confirmed by confocal imaging,digital PCR,and DNA sequencing.MT mice exhibi-ted positive biological effects,including increased ATP content and mitochondrial DNA copy number(P＜0.05),with the maximum effect observed on day 7,which was sustained until day 14.Reactive oxygen species(ROS)levels in MT mice significantly increased at 2 hours post-injection(P＜0.05),then grad-ually decreased towards baseline levels by day 7 and day 14(P＞0.05).These findings support the effec-tiveness of xenogeneic MT and suggest that the effects can be maintained for up to 14 days.This study provides scientific evidence for future clinical applications.

Animal experiments are an essential component of life sciences and medical research. However, the external validity and reliability of individual animal studies are frequently challenged by inherent limitations such as small sample sizes, high design heterogeneity, and poor reproducibility, which impede the effective translation of research findings into clinical practice. Systematic reviews and meta-analysis represent a key methodology for integrating existing evidence and enhancing the robustness of conclusions. Currently, however, the application of systematic reviews and meta-analysis in the field of animal experiments lacks standardized guidelines for their conduct and reporting, resulting in inconsistent quality and, to some extent, diminishing their evidence value. To address this issue, this paper aims to systematically delineate the reporting process for systematic reviews and meta-analysis of animal experiments and to propose a set of standardized recommendations that are both scientific and practical. The article's scope encompasses the entire process, from the preliminary preparatory phase [including formulating the population， intervention， comparison and outcome （PICO） question, assessing feasibility, and protocol pre-registration] to the key writing points for each section of the main report. In the core methods section, the paper elaborates on how to implement literature searches, establish eligibility criteria, perform data extraction, and assess the risk of bias, based on the Preferred Reporting Items for Systematic Reviews and Meta-analysis （PRISMA) statement, in conjunction with relevant guidelines and tools such as Animal Research： Reporting of in Vivo Experiments （ARRIVE) and a risk of bias assessment tool developed by the Systematic Review Centre for Laboratory Animal Experimentation (SYRCLE). For the presentation of results, strategies are proposed for clear and transparent display using flow diagrams and tables of characteristics. The discussion section places particular emphasis on how to scientifically interpret pooled effects, thoroughly analyze sources of heterogeneity, evaluate the impact of publication bias, and cautiously discuss the validity and limitations of extrapolating findings from animal studies to clinical settings. Furthermore, this paper recommends adopting the Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology to comprehensively grade the quality of evidence. Through a modular analysis of the entire reporting process, this paper aims to provide researchers in the field with a clear and practical guide, thereby promoting the standardized development of systematic reviews and meta-analysis of animal experiments and enhancing their application value in scientific decision-making and translational medicine.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.