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 form an expert consensus on the clinical application of parenteral direct thrombin inhibitors （DTIs） in patients during the perioperative period. METHODS Led by Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital （the Affiliated Hospital of UESTC）， a multidisciplinary working group was established. Through literature review and the Delphi method， clinical questions related to the rational perioperative use of parenteral DTIs were identified. A structured design was adopted using the “Population-Intervention-Comparison-Outcome” framework； systematic searches were conducted in CNKI， Medline， Embase and other databases. Relevant evidence from randomized controlled trials and cohort studies was included and synthesized. Evidence quality was assessed using the Grades of Recommendations Assessment，Development and Evaluation （GRADE） approach， and recommendations were formulated through multiple rounds of Delphi surveys and expert consensus meetings. RESULTS &CONCLUSIONS Seven recommendations （each with an expert consensus rate exceeding 90%） on the use of parenteral DTIs in perioperative patients were developed. These recommendations specify drug selection， dosing ranges， key monitoring points， and safety management strategies for parenteral DTIs in various scenarios， including the perioperative period of ventricular assist device implantation， the perioperative period of cardiac surgery， perioperative patients with lower-extremity atherosclerotic disease， the perioperative period of percutaneous coronary intervention in patients with acute coronary syndrome， the perioperative period of carotid artery stenting in patients with carotid stenosis， the perioperative period of patients with right heart thrombosis， and patients who develop related thrombosis and dysfunction after a central venous catheter insertion. In addition， warning and management pathways for perioperative bleeding and thrombotic events were proposed. This expert consensus， which is formulated based on the best available evidence， provides evidence-based guidance for standardized and individualized use of parenteral DTIs in perioperative period.

Background The quality of occupational health technical services is directly linked to the protection of workers' health rights and the efficacy of occupational disease prevention and control. However, the industry still faces critical challenges: sporadic instances of institutional non-compliance and persistent irregularities in professional practice continue to undermine overall service performance. Objective To assess the current quality status of occupational health technical services in Zhejiang Province and propose countermeasures for quality improvement, providing a scientific basis for policy optimization and service delivery quality enhancement. Methods A total of 69 occupational health technical service institutions in Zhejiang Province that obtained formal accreditation as of April 30, 2024, were sampled, including 3 public institutions and 66 private institutions (comprising 3 formerly Class-A, 28 formerly Class-B, 11 formerly Class-C, and 24 newly certified institutions). Following the Technical Protocol for Quality Monitoring of Occupational Health Technical Service in Zhejiang Province and the Technical Protocol for Proficiency Testing of Occupational Health Detection in Zhejiang Province, a quality assessment task force comprising national and provincial experts was established. Evaluation was conducted across four dimensions: qualification maintenance and compliance, standardization of technical services, authenticity of technical services, and proficiency testing, utilizing a combination of document review, on-site inspections, and technical skill assessments. Results The occupational health technical service institutions in Zhejiang Province were predominantly private entities (82.5%), with significant disparities in overall service quality. The pass rates for qualification maintenance and compliance, technical service standardization, technical service authenticity, and the excellence rate for laboratory proficiency testing were 81.5%, 80.7%, 97.3%, and 90.4%, respectively. Regarding qualification maintenance, the pass rates for "environmental conditions" (49.8%, 56.7%) and "instrumentation and equipment" (58.2%、65.6%) were significantly lower for formerly Class-C and newly certified institutions compared to other categories. In terms of technical standardization, "standardized on-site inspections" recorded the lowest pass rate (67.4%), with newly certified institutions at only 48.0%. Regarding technical service authenticity, formerly Class-C institutions exhibited issues such as missing raw chromatograms for blank samples (85.7% pass rate). In laboratory proficiency testing, public and formerly Class-A institutions achieved 100% excellence rates, but the performance of formerly Class-C and newly certified institutions was comparatively weak; specifically, the failure rate for organic analysis in formerly Class-C institutions reached 20%; the failure rate for dust testing items in newly certified institutions was 10.3%. Conclusion The overall quality of occupational health technical services in Zhejiang Province still requires significant improvement, particularly in basic institutional conditions, the standardization of on-site inspections, and laboratory proficiency in organic and dust analysis. Formerly Class-C and newly certified institutions should be the primary focus of quality management efforts. Differentiated regulatory strategies are recommended, alongside strengthening interim and ex-post supervision to gradually enhance the quality of occupational health technical services across all institutions.

OBJECTIVE To form an expert consensus addressing clinical issues regarding the use of parenteral direct thrombin inhibitors （DTIs） in special populations. METHODS Led by the Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital（the Affiliated Hospital of UESTC）， a multidisciplinary working group was formed comprising experts from multiple fields， including clinical pharmacy， cardiac surgery， obstetrics， pediatrics and evidence-based medicine. Through literature review and the Delphi method， clinical questions regarding the efficacy and safety of parenteral DTIs used in special populations were identified. A structured design was adopted using the “Population-Intervention-Comparison-Outcome” （PICO） framework；systematic searches were conducted in CJFD， PubMed， Embase and other databases. Relevant evidence from randomized controlled trials，cohort studies and systematic reviews were included and synthesized. Evidence quality was assessed using the Grading of Recommendations Assessment，Development and Evaluation （GRADE） approach， and recommendations were formulated through three rounds of Delphi surveys and expert consensus meetings. RESULTS &CONCLUSIONS Seven clinical questions were ultimately selected （with a consensus rate exceeding 90%）， resulting in the formulation of seven recommendations on the use of parenteral DTIs in special populations， including children， pregnant women， patients with hepatic or renal impairment， patients with mesenteric venous thrombosis， and individuals with thrombophilia. These recommendations clarify the preferred agents， dosing ranges， monitoring parameters， and safety management strategies for parenteral DTIs in these special populations. This expert consensus， which is formulated based on the best available evidence， provides evidence-based guidance for standardized and individualized use of parenteral DTIs in special populations.

OBJECTIVE To investigate the correlation of component content with colorimetric values during the roasting process of Ziziphus jujuba ， and to provide criteria for discriminating the roasting degree of Z. jujuba . METHODS Samples were prepared by dry stir-frying for different roasting times. The eight main components in raw Z. jujuba and the samples stir-fried for different roasting times-namely adenosine， magnoflorine， jujuboside A， spinosin， 6-feruloylspinosin， betulinic acid， oleic acid， and linoleic acid-were quantitativel y analyzed using ultra-performance liquid chromatography. The chromaticity values were determined using a UV spectrophotometer. The correlation and differences between the chromaticity values of Z. jujuba at different roasting times and their components content were analyzed by Pearson correlation analysis， linear regression analysis， principal component analysis （PCA） ， cluster heatmap analysis （CHA）， and partial least squares discriminant analysis （PLS-DA） to clarify the processing endpoint. RESULTS As the roasting time increased， the contents of linoleic acid and oleic acid decreased， while the contents of other components exhibited an increasing trend. Concurrently， the colorimetric value L* and E*ab were observed to decline， whereas the a* value demonstrated a gradual increase. Pearson correlation analysis revealed that L* and E*ab exhibited a significant negative correlation with the contents of adenosine， spinosin， 6-feruloylspinosin， jujuboside A， betulinic acid and magnoflorine （ P ＜0.05）. The results of linear regression analysis indicate that the content of six components, including adenosine， in the medicinal material can be preliminarily predicted by analyzing the colorimetric values of Z. jujuba powder. PCA and CHA successfully classified raw and stir-fried samples. The PLS-DA results indicated that L*， E*ab， a*， linoleic acid content， and oleic acid content were the main parameters that differentiated the color and quality of Z. jujuba at different roasting times. After frying for 9 to 10 minutes， the colorimetric values L* and E*ab decreased to their minimum values and stabilized， while a* remained consistently high with little variation；simultaneously， the concentrations of the six major components， excluding linoleic acid and oleic acid， reached their peak levels. CONCLUSIONS A significant correlation between the colorimetric values of Z. jujuba and the contents of six components， including adenosine， is confirmed. The optimal roasting time range is determined to be 9-10 minutes. Furthermore， the colorimetric value-component content correlation analysis method established in this study proved to be practical and applicable for discriminating the roasting degree of Z. jujuba .

Objective:To analyze the predictive role of WT1 expression levels pre-and early post-transplantation on relapse and overall survival(OS)in patients with acute myeloid leukemia(AML)undergoing allogeneic hematopoietic stem cell transplantation(allo-HSCT)during their first complete remission(CR1).Methods:A retrospective analysis was conducted on the clinical data of 107 adult AML patients who underwent allo-HSCT during their CR1 at our center between May 2012 and December 2021.The predictive role of bone marrow WT1 expression levels before transplantation and at 3 and 6 months post-transplantation on relapse and OS was explored in combination with relevant clinical factors.Results:The median follow-up time for the 107 patients was 70(range:11-117)months.Among the patients,15 cases died.Kaplan-Meier survial analysis showed that the 3-year overall survival(OS)rate was 85.0％.20 patients experienced relapse,with a median time to relapse of 8(range:0.5-44)months and a l-year cumulative relapse rate of 13.1％.The overall median value of WT1 before transplantation,3 months after transplantation,and 6 months after transplantation was 0.26％(range:0％-23.64％),with an upper quartile value of 0.74％.No statistically significant differences in WT1 expression levels were observed among the pre-transplantation,3-month post-transplantation,and 6-month post-transplantation time points(P=0.227).Univariate analysis showed that patients with WT1 levels＞0.74％at 3 months post-transplantation had a higher 1-year relapse rate(P=0.029)and lower 3-year OS rate(P＜0.001)compared to patients with WT1 levels ≤0.74％.Other significant factors affecting 1-year relapse included stem cell source(P=0.041)and chronic graft-versus-host disease(cGVHD)(P=0.013).For 3-year OS,additional influencing factors were genetic high risk(P=0.048)and stem cell source(P=0.016).Multivariate analysis revealed that WT1 level＞0.74％at 3 months post-transplantation had a trend to affect 1-year relapse rate(HR=3.309,95％CI:0.958-11.431,P=0.058),while the absence of cGVHD was an independent risk factor for 1-year relapse(HR=3.473,95％CI:0.749-16.100,P=0.037).Only WT1 level＞0.74％at 3 months post-transplantation was an independent risk factor for 3-year OS(HR=6.886,95％CI:2.402-19.738,P＜0.001).Conclusion:High WT1 expression level at 3 months post-transplantation in AML patients undergoing allo-HSCT during CR1 affects the 1-year relapse rate and 3-year OS,and is an independent risk factor affecting 3-year OS.These findings suggest that dynamic monitoring of WT1 expression levels has certain value in prognostic assessment of AML patients who received allo-HSCT during CR1.

Objective To explore the characteristics of fine motor deficits in the elderly individuals with MCI due to AD through a new wearable inertial motion capture device,and then construct a prediction model for MCI.Methods A total of 260 elderly subjects were recruited in community from November,2022 to April,2023,and based on diagnosis,they were divided into a MCI group(134 cases)and a control group(126 cases).A new wearable inertial motion capture device,which was self-designed and developed based on MEMS inertial sensor,was used to capture the fine mo-tor movements of the hands,and the obtained data were analyzed with a computerized assessment system to make the quantitative evaluation of fine motor.LASSO learning algorithm and logistic regression analysis were employed to identify the predictive factors for MCI,and then a nomo-gram was constructed based on these factors.ROC curve was plotted to evaluate the predictive ability of the model by calculating its AUC value.DC A,CIC,and Bootstrap method were applied to evaluate and validate the clinical utility and stability of the model.Results The total score of MoCA(22.18±2.84 vs 27.60±1.10)and scores of the dimensions were significantly lower in the MCI group than the control group(all P＜0.01).In the five digital assessment tasks,the MCI group showed obviously poorer fine motor performance of both hands than the control group(P＜0.05,P＜0.01).ROC curve analysis showed that the AUC value of our nomogram model in predicting MCI was 0.762(95％CI:0.705-0.819).DCA,CIC,and Bootstrap methods demonstra-ted good and relatively stable discrimination,calibration,and clinical applicability of the model.Conclusion MEMS inertial sensor motion capture technology can make digital evaluation of fine motor.For the elderly,fine motor deficits are significantly associated with risk for MCI.Our no-mogram model based on fine motion parameters shows good predictive efficacy in assessing the risk of MCI.

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

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 investigate the value of a deep learning radiomics (DLR) nomogram model based on intra-tumoral and peri-tumoral MRI features for differentiating IgG4-related ophthalmic disease (IgG4-ROD) from orbital mucosa-associated lymphoid tissue (MALT) lymphoma.Methods:This was a case-control study. The clinical and imaging data of 233 patients pathologically confirmed with either IgG4-ROD or orbital MALT lymphoma were retrospective collected between January 2020 and December 2024 from the Second Hospital of Jilin University (Center 1) and Beijing Tongren Hospital, Capital Medical University (Center 2). Patients from Center 1 ( n=158) were used as the training cohort, while those from Center 2 ( n=75) served as the validation cohort. Among the cases, 102 were IgG4-ROD (70 in training, 32 in validation) and 131 were orbital MALT lymphoma (88 in training, 43 in validation). Univariate and multivariate logistic regression analyses were used to identify independent clinical imaging predictors and build a clinical imaging model. Based on T 1WI, T 2WI, and diffusion weighted images, intra-tumoral regions were manually delineated, a 2 mm peri-tumoral margin was automatically generated, and both regions were combined as a single region of interest for radiomics feature extraction. Deep learning features were extracted using a ResNet-50 backbone, and after feature selection and dimensionality reduction, a DLR model was constructed. The clinical imaging features and DLR features were integrated to build a combined nomogram model. Model performance in differentiating IgG4-ROD from orbital MALT lymphoma was assessed using receiver operating characteristic curves, calibration curves, and decision curve analysis. The area under the curve (AUC) were compared using the DeLong test. Results:Bilateral orbital involvement ( OR=1.983, 95% CI 1.166-2.843, P=0.046) and extraocular muscle involvement ( OR=1.246, 95% CI 1.079-1.764, P=0.015) were identified as independent predictors for distinguishing IgG4-ROD from orbital MALT lymphoma and were used to construct the clinical model. Fourteen features (9 radiomics and 5 deep learning features) were selected for the DLR model, and a nomogram was developed. In the training set, the AUCs for the clinical model, DLR model, and nomogram were 0.762 (95% CI 0.712-0.812), 0.865 (95% CI 0.822-0.908), and 0.943 (95% CI 0.909-0.953), respectively. In the validation set, the AUCs were 0.733 (95% CI 0.675-0.791), 0.823 (95% CI 0.762-0.884), and 0.924 (95% CI 0.902-0.958), respectively. The nomogram showed significantly higher AUCs than those of the clinical and DLR models alone (training set: Z=3.92, 2.87, P0.001, P=0.004; validation set: Z=3.25, 2.46, P=0.001, 0.014). Calibration curves indicated good agreement between predicted and actual IgG4-ROD incidence, and decision curve analysis demonstrated the highest net benefit for the nomogram. Conclusion:A nomogram that incorporates both intra-tumoral and peri-tumoral DLR features and clinical imaging characteristics demonstrates excellent performance in distinguishing IgG4-ROD from orbital MALT lymphoma.