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 To investigate the therapeutic effect of Huangkui capsules on targeted drug-related proteinuria in patients with hepatocellular carcinoma (HCC). Methods A retrospective analysis was conducted on clinical data of HCC patients with targeted drug-related proteinuria from June 2023 to December 2024 at Zhongshan Hospital, Fudan University. According to the treatment plan, patients were divided into the conventional treatment group and the Huangkui combination treatment group (Huangkui capsules combined with conventional treatment), and the clinical efficacy between the two groups was compared. The logistic regression analysis was used to identify the main factors affecting treatment efficacy. Results The Huangkui combination treatment group (n＝29) showed a significantly higher overall effective rate (79.3% vs 42.3%, P＝0.005), and an earlier proteinuria improvement (median time: 3 months vs 6 months, P＝0.008) than the conventional treatment group (n＝26) . The multivariate logistic regression analysis showed angiotensin-converting enzyme inhibitor (ACEI) or angiotensin Ⅱ receptor blocker (ARB) using (OR＝0.190, 95%CI 0.045-0.808, P＝0.025), targeted drug adjustment (OR＝0.132, 95%CI 0.030-0.581, P＝0.007), and Huangkui capsules using (OR＝0.168, 95%CI 0.039-0.730, P＝0.017) were protective factors for treatment efficacy of targeted drug-related proteinuria. Conclusions On the basis of conventional treatment, additive treatment with Huangkui capsules can alleviate targeted drug-related proteinuria faster and more effectively in HCC patients.

Yanchi Tan sheep,a specialty of Ningxia,China,is famous for its excellent meat quality,but the detailed mechanisms of its muscle development and meat quality formation are still unclear.In or-der to preliminarily investigate the unique molecular mechanisms in the muscle development of the sheep,the present study was carried out to collect the longest dorsal muscle(LDM)samples from 6-month-old and 12-month-old female Tan sheep,and we screened the genes and long non-coding RNA(lncRNAs)that may play important roles in regulating the muscle development and meat quality formation through transcriptome sequencing(RNA-seq).The results showed that a total of 504 mRNAs and 1,483 lncRNAs were detected between the two groups of LDMs,with significant differences(P＜0.05).And we also pre-dicted 103 potential lncRNA-mRNA cis-regulatory relationship pairs with significant differences(P＜0.05).Skeletal muscle development-related genes such as MSTN,CDKN1A,FHL1 and skeletal muscle metabolism-related genes such as LPL and SCD were subsequently screened based on the functional anal-ysis of differentially expressed mRNA.Based on the functional analysis of differentially expressed lncRNA target genes,13 lncRNAs potentially related to muscle development(LNC.17926.1,LNC.981.1,LNC.16284.1,etc.)and 8 lncRNAs potentially related to skeletal muscle metabolism(LNC.15496.1,LNC.18149.1,etc.)were identified.5 lncRNAs and 4 mRNAs expressed in both groups were randomly selected for qPCR validation experiments,and their results were consistent with RNA-seq,which corrob-orated with the reliability of the sequencing results.This study lays a solid foundation for future investiga-tions of muscle development,meat quality formation mechanism and molecular breeding in Tan sheep,and provides a scientific basis for improving the meat production performance of other domestic sheep breeds.

This study investigated the potential pathogenicity and genetic characteristics of ST314 Salmonella Kentucky(S.Ken-tucky)isolates from a broiler slaughterhouse.Antimicrobial susceptibility testing and whole-genome sequencing(WGS)were used to determine antimicrobial resistance,virulence factors,and the presence of antimicrobial resistance genes(ARGs)and mobile genetic elements(MGEs)among the isolates.The three multidrug resistant(MDR)isolates exhibited high resistance to multiple antimicrobial agents.The F4-2S strain exhibited resistance to 14 drugs across seven categories,whereas the F4T strain showed resistance to 13 drugs in the same number of categories.In contrast,the Y23 strain was resistant to nine drugs in six categories.Notably,F4-2S dem-onstrated high homology with F4T:both possessed 13 ARGs distributed across nine categories,in addition to a wide range of virulence factors,including secretion systems and effector proteins.The presence of IncR and IncX1 plasmids significantly enhanced both the antimicrobial resistance and pathogenicity of the isolates.The genome map of Y23 revealed a chromosome alongside two plasmids.The chromosome containedonly one resistance gene but several virulence factors,including the type III secretion system(T3SS),which is crucial for bacterial invasion.The plasmid pY23-1 contained eight types of 19 ARGs.Comparative analysis indicated that pY23-1 ex-hibited high homology with pZ1323SSL0055 and pSAL-045,all of which contained multiple ARGs,thus suggesting critical roles of these genes in the evolution of bacterial resistance.In conclusion,ST314 S.Kentucky demonstrated a complex mechanism of resis-tance coupled with significant pathogenic potential.The ARGs and MGEs in the plasmid contributed to the emergence and dissemina-tion of antimicrobial resistance.The multiple virulence factors present in the chromosome may be key factors driving the increasing virulence of ST314 S.Kentucky.

Objective:To explore the effect of preoperative visits based on lean management theory in the preoperative psychological state of patients undergoing cardiopulmonary bypass surgery.Methods:According to the coin toss method,72 patients undergoing cardiopulmonary bypass surgery admitted to the Cardiothoracic Surgery Department of Chenzhou First People's Hospital from May 2024 to August 2024 were randomly divided into control group and observation group,with 36 cases in each group.The control group was given routine preoperative visits,and the observation group was given preoperative visits based on lean management theory on the basis of the control group.The psychological state,information demand level,vital signs and satisfaction of the patients were compared between the two groups 1 day before operation and when entering the room.Results:After intervention,the scores of State Anxiety Scale(S-AI)and Amsterdam Preoperative Anxiety and Information Scale(APAIS)were decreased in both groups,and the observation group were significantly lower than control group(P＜0.05).The vital signs(heart rate,systolic blood pressure and diastolic blood pressure)in observation group were lower than control group(P＜0.05).After intervention,the satisfaction of patients in observation group was significantly increased(P＜0.05).Conclusion:The implementation of preoperative visit based on lean management theory can help improve the preoperative psychological state of patients undergoing cardiopulmonary bypass surgery,eliminate anxiety,provide patients with more targeted surgical information support for patients,stabilize patients 'vital signs,and improve patients' satisfaction.

Objective To propose a dynamic early warning model based on machine learning methods and validate its predi-ctive efficacy so as to achieve precise assessment and early warning of mortality risk in patients with traumatic hypothermia.Methods Firstly,a total of 480 patients who met inclusion criteria were retrospectively selected from the eICU database and randomly divided into training and test sets at an 8∶2 ratio.Secondly,physiological parameters were extracted from these patients,and five machine learning algorithms including XGBoost,AdaBoost,LightGBM,logistic regression(LR)and random forest(RF)were employed respectively to develop dynamic mortality risk warning models for traumatic hypothermia patients,utilizing a 1-hour observation window.Thirdly,receiver operating characteristic curves(ROC)were plotted using the test set data and the effects of different warning windows on the model performance were analyzed by calculating the AUC.Finally,the interpretability of the models was analyzed using the SHapley Additive exPlanations(SHAP)algorithm to elucidate the contribution of each feature to predictive performance.Results The optimal warning window for the dynamic warning model constructed using the eICU database was 12 hours,and in case of 12-hour warning window the logistic regression model achieved the highest AUC of 0.935 and showed optimal predictive performance.The results of the interpretability analysis by the SHAP algorithm showed that body temperature was the feature that had the greatest impact on the model results,and its reduction was positively correlated with the increased risk of death.Conclusion The machine learning-based dynamic warning model for mortality risk in traumatic hypothermia patients enables real-time dynamic risk assessment,providing robust support for clinicians to identify the patient's condition changes at an early stage and references for the adjustment of clinical treatment programs.[Chinese Medical Equipment Journal,2025,46(3):9-14]

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.

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.

Objective:This study examines the impact of integrated healthcare and elderly care policy pilots on paid work among older adults.Methods:Using CHARLS panel data(2013-2020),we employed a difference-in-differences(DID)model to assess the policy effects.Results:The results indicate a 10.3%average increase in paid work among older adults in pilot areas.Mechanism analysis reveals that health status mediated this effect,while social security exhibited a suppression effect.The policy significantly increased participation in agricultural work,non-agricultural employment,and self-employed farming but reduced participation in hired agricultural labor.Heterogeneity analysis shows stronger effects in rural and central-western regions.Conclusion and Suggestions:The integrated care policy effectively promotes older adults'paid work engagement.We recommend strengthening service systems,implementing phased benefit improvements with work incentives,expanding pilot coverage in rural and central-western regions,and fostering health-labor policy coordination.

Objective To propose a dynamic early warning model based on machine learning methods and validate its predi-ctive efficacy so as to achieve precise assessment and early warning of mortality risk in patients with traumatic hypothermia.Methods Firstly,a total of 480 patients who met inclusion criteria were retrospectively selected from the eICU database and randomly divided into training and test sets at an 8∶2 ratio.Secondly,physiological parameters were extracted from these patients,and five machine learning algorithms including XGBoost,AdaBoost,LightGBM,logistic regression(LR)and random forest(RF)were employed respectively to develop dynamic mortality risk warning models for traumatic hypothermia patients,utilizing a 1-hour observation window.Thirdly,receiver operating characteristic curves(ROC)were plotted using the test set data and the effects of different warning windows on the model performance were analyzed by calculating the AUC.Finally,the interpretability of the models was analyzed using the SHapley Additive exPlanations(SHAP)algorithm to elucidate the contribution of each feature to predictive performance.Results The optimal warning window for the dynamic warning model constructed using the eICU database was 12 hours,and in case of 12-hour warning window the logistic regression model achieved the highest AUC of 0.935 and showed optimal predictive performance.The results of the interpretability analysis by the SHAP algorithm showed that body temperature was the feature that had the greatest impact on the model results,and its reduction was positively correlated with the increased risk of death.Conclusion The machine learning-based dynamic warning model for mortality risk in traumatic hypothermia patients enables real-time dynamic risk assessment,providing robust support for clinicians to identify the patient's condition changes at an early stage and references for the adjustment of clinical treatment programs.[Chinese Medical Equipment Journal,2025,46(3):9-14]