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.

Background: High-mobility group box protein 1 (HMGB1) is a chromatin-binding protein involved in arthritis, ischemia, sepsis, atherosclerosis, neurodegenerative disorders, meningitis, and cancer. HMGB1 exhibits dual roles in cancer, acting as either a tumor suppressor or oncoprotein depending on context. Objective: This research aimed to elucidate HMGB1’s functional significance in cutaneous squamous cell carcinoma (cSCC). Methods: We overexpressed HMGB1 in cSCC cell lines using recombinant adenovirus and examined its effects on cell proliferation, colony formation, and cell migration. Results: Immunohistochemical analysis revealed elevated HMGB1 expression levels in cSCC tissue relative to normal epidermis. To assess the influence of HMGB1, we employed recombinant adenoviruses expressing HMGB1 to transduce SCC cell lines (SCC12 and SCC13). Enhanced HMGB1 expression significantly promoted cellular proliferation and colony formation capacity.Notably, HMGB1 overexpression elevated the levels of proliferation regulators, including P63, SOX2, CDK4 and CDK6. Furthermore, HMGB1 overexpression substantially enhanced tumor invasiveness, accompanied by upregulation of epithelial-mesenchymal transition (EMT) biomarkers. Mechanistically, overexpression of HMGB1 enhanced transforming growth factor-β signaling by increasing phosphorylation of SMAD2/3, the key mediators of EMT. Conclusion These data imply that HMGB1 acts as a tumor-promoting factor in cSCC.

Objective:To establish three machine learning prediction models based on CT imaging characteristics of papillary thyroid carcinoma (PTC) , and use SHAP (shapley additive explanations) analysis to investigate the contribution of each CT image features in the best model.Methods:CT imaging features in 426 cases of 440 PTCs confirmed pathologically from Jan. 2016 to Jan. 2021 at the affiliated Hangzhou First People’s Hospital of Westlake University Medical School were retrospectively analyzed. compared with 467 cases of 528 nodular goiter (NG) , evaluating the distribution of four CT characteristics: cookie bite sign, enhanced range of narrowing/blur (ERNB) , microcalcifications, and irregular shape. We split the data into 8∶2 ratio for training and testing sets, then constructed three machine learning models using XGBoost, RF, and SVM. Based on AUC, accuracy, F1 score, and other metrics, we selected the best model. Lastly, we used SHAP values to assess each CT feature’s contribution and positive/negative effects on the model.Results:Among 440 PTC and 528 NG nodules, CT features like cookie bite sign, ERNB, microcalcifications, and irregular shape occurred in 326 and 30 ( χ 2=483.05, P<0.001) , 363 and 106 ( χ 2=374.45, P<0.001) , 158 and 53 ( χ 2=94.24, P<0.001) , and 354 and 52 ( χ 2=491.34, P<0.001) nodules, respectively. The machine learning models built using XGBoost, RF, and SVM had AUC, accuracy, and F1 scores ranging from 0.884~0.925, 0.867~0.873, and 0.844~0.854 respectively on the training set. On the test set, the scores ranged from 0.869~0.923, 0.845~0.871, and 0.803~0.845. Among them, the XGBoost model demonstrated the highest diagnostic performance on the test set. Among the four CT features, irregular shape had the highest absolute SHAP value, positively contributing to PTC diagnosis. Conclusion:XGBoost model showed the highest PTC diagnostic performance. Irregular shape had the greatest positive impact on PTC diagnosis.

Objective To understand the molecular epidemiological characteristics and hypervirulence evolution trend of ST11 carbapenem-resistant Klebsiella pneumoniae(CRKP)isolated from medical institutions in Shanghai,and provide scientific basis for formulating the prevention and control measures of drug-resistant organisms.Methods A total of 201 strains of CRKP isolated from 12 medical institutions in Shanghai from 2021 to 2022 were collected.Antimicrobial susceptibility testing and whole genome sequencing were performed.The concatenated data was used for multilocus sequencing typing(MLST),serum typing(wzi typing),as well as analysis of resistance and viru-lence genes.Results All 201 CRKP strains were multidrug-resistant organisms(MDROs).These strains were al-most completely resistant to carbapenems,cephalosporins,and quinolones.Drug resistance gene analysis showed that 93.03％of CRKP strains carried KPC gene.201 CRKP strains were divided into 6 ST types and 10 capsule wzi types,with ST11-KL64(n=104)being the dominant type,followed by ST15-KL19(n=54).52.24％(n=105)of CRKP carried rpmA/rpmA2+iucA+iutA+iroN genes.Conclusion CRKP isolated from medical institutions in Shanghai is mainly ST11-KL64 type with severe multidrug resistance,and more than half of the strains are hyper-virulent carbapenem-resistant Klebsiella pneumoniae(hv-CRKP).It is necessary to continuously strengthen the monitoring of the molecular characteristics of CRKP,so as to prevent outbreaks of healthcare-associated infection.

Objective:To establish three machine learning prediction models based on CT imaging characteristics of papillary thyroid carcinoma (PTC) , and use SHAP (shapley additive explanations) analysis to investigate the contribution of each CT image features in the best model.Methods:CT imaging features in 426 cases of 440 PTCs confirmed pathologically from Jan. 2016 to Jan. 2021 at the affiliated Hangzhou First People’s Hospital of Westlake University Medical School were retrospectively analyzed. compared with 467 cases of 528 nodular goiter (NG) , evaluating the distribution of four CT characteristics: cookie bite sign, enhanced range of narrowing/blur (ERNB) , microcalcifications, and irregular shape. We split the data into 8∶2 ratio for training and testing sets, then constructed three machine learning models using XGBoost, RF, and SVM. Based on AUC, accuracy, F1 score, and other metrics, we selected the best model. Lastly, we used SHAP values to assess each CT feature’s contribution and positive/negative effects on the model.Results:Among 440 PTC and 528 NG nodules, CT features like cookie bite sign, ERNB, microcalcifications, and irregular shape occurred in 326 and 30 ( χ 2=483.05, P<0.001) , 363 and 106 ( χ 2=374.45, P<0.001) , 158 and 53 ( χ 2=94.24, P<0.001) , and 354 and 52 ( χ 2=491.34, P<0.001) nodules, respectively. The machine learning models built using XGBoost, RF, and SVM had AUC, accuracy, and F1 scores ranging from 0.884~0.925, 0.867~0.873, and 0.844~0.854 respectively on the training set. On the test set, the scores ranged from 0.869~0.923, 0.845~0.871, and 0.803~0.845. Among them, the XGBoost model demonstrated the highest diagnostic performance on the test set. Among the four CT features, irregular shape had the highest absolute SHAP value, positively contributing to PTC diagnosis. Conclusion:XGBoost model showed the highest PTC diagnostic performance. Irregular shape had the greatest positive impact on PTC diagnosis.

Objective:To investigate the clinical efficacy of pancreatic cancer surgery with arterial resection.Methods:The clinicopathological and follow-up data of 135 patients undergoing pancreatectomies with arterial resection in Pancreas Center, the First Affiliated Hospital of Nanjing Medical University from Sep 2013 to Dec 2023 were retrospectively analyzed.Results:There were 77 males and 58 females, with age [ M( IQR)] of 63 (14) years old. Among the 135 patients, 122 (90.4%) were distal pancreatectomies, 8 (5.9%) were pancreaticoduodenectomies, 4 (3.0%) were total pancreatectomies and 1 (0.7%) was resection for local recurrence after distal pancreatectomy. There were 120 (88.9%) celiac axis resections, 11 (8.1%) hepatic artery resections, 1 (0.7%) superior mesenteric artery resection and 3 (2.2%) other artery resections. Simultaneous portal vein-superior mesenteric vein or organ resection accounted for 26.7% (36/135) and 29.6% (40/135),respectively. The median blood loss was 300 (300) ml and the median operation time was 275 (105) minutes. The 90-day mortality rate was 7.4% (10/135). The overall morbidity rate was 70.4% (95/135) while the major morbidity rate was 18.5% (25/135). Postoperative hemorrhage occurred in 8.9% (12/135), clinically relevant postoperative pancreatic fistula in 57.0% (77/135), bile leak in 0.74% (1/135), delayed gastric emptying in 9.6% (13/135), liver failure in 3.7% (5/135) and transient liver enzyme elevation in 44.4% (60/135). All of the 135 cases were confirmed as pancreatic cancer histologically, including 54.6% (71/130) moderately differentiated, 45.4% (59/130) poorly differentiated and no for well differentiated. The median tumor size was 4.5 (2.3) cm. The median number of harvested lymph nodes was 14 (13) and the percentage of N0, N1 and N2 according to AJCC 8th staging system was 27.1% (36/133), 52.6% (70/133) and 20.3% (27/133), respectively. The R 0 resection was achieved in 40 of 123 cases (32.5%), whose margins of specimens were assessed circumferentially based on the 1mm rule. The median overall survival time (MST) after surgery was 22.5 months, and the median progress-free survival time was 16.1 months. The overall survival rate at 1-, 2- and 5-year was 71.5%, 45.1% and 11.3%, respectively. The MST of patients who received no adjuvant therapy, chemotherapy after surgery was 8.4 months, 25.3 months, respectively. Conclusions:Pancreatectomy with arterial resection is generally safe and feasible. Survival outcome improves significantly when combined with adjuvant chemotherapy.

Objective:To explore the mediating effect of work engagement between growth mindset and prone ventilation knowledge, beliefs, and practices among neonatal intensive care unit (NICU) nurses.Methods:A convenience sampling method was used to select 360 NICU nurses from 13 tertiary hospitals in Shandong Province between August and December 2023. The study used General Information Surveys, Growth Mindset Assessment Scales, a simplified Work Engagement Scale, and an ICU Staff Acute Respiratory Distress Syndrome Prone Ventilation Knowledge, Beliefs, and Practices Questionnaire. Pearson correlation analysis was used to explore the relationship between growth mindset, work engagement, and prone ventilation knowledge, beliefs, and practices. The mediating model was constructed using the PROCESS plugin for SPSS software.Results:A total of 370 questionnaires were distributed, and 360 valid questionnaires were collected, with an effective response rate of 97.3% (360/370). Statistical analysis showed that NICU nurses' growth mindset positively predicted work engagement ( β=0.357, P<0.01) and prone ventilation knowledge, beliefs, and practices ( β=0.736, P<0.01). Work engagement positively predicted prone ventilation knowledge, beliefs, and practices ( β=0.107, P<0.01). Growth mindset had a positive indirect effect on prone ventilation knowledge, beliefs, and practices through work engagement, with an indirect effect of 0.039. Conclusions:NICU nurses' growth mindset positively predicts their prone ventilation knowledge, beliefs, and practices, and can also have an indirect effect through work engagement. Nursing managers should improve NICU nurses' growth mindset using management strategies, optimize resource allocation, and enhance work engagement to improve NICU nurses' prone ventilation knowledge, beliefs, and practices.

Objective To understand the molecular epidemiological characteristics and hypervirulence evolution trend of ST11 carbapenem-resistant Klebsiella pneumoniae(CRKP)isolated from medical institutions in Shanghai,and provide scientific basis for formulating the prevention and control measures of drug-resistant organisms.Methods A total of 201 strains of CRKP isolated from 12 medical institutions in Shanghai from 2021 to 2022 were collected.Antimicrobial susceptibility testing and whole genome sequencing were performed.The concatenated data was used for multilocus sequencing typing(MLST),serum typing(wzi typing),as well as analysis of resistance and viru-lence genes.Results All 201 CRKP strains were multidrug-resistant organisms(MDROs).These strains were al-most completely resistant to carbapenems,cephalosporins,and quinolones.Drug resistance gene analysis showed that 93.03％of CRKP strains carried KPC gene.201 CRKP strains were divided into 6 ST types and 10 capsule wzi types,with ST11-KL64(n=104)being the dominant type,followed by ST15-KL19(n=54).52.24％(n=105)of CRKP carried rpmA/rpmA2+iucA+iutA+iroN genes.Conclusion CRKP isolated from medical institutions in Shanghai is mainly ST11-KL64 type with severe multidrug resistance,and more than half of the strains are hyper-virulent carbapenem-resistant Klebsiella pneumoniae(hv-CRKP).It is necessary to continuously strengthen the monitoring of the molecular characteristics of CRKP,so as to prevent outbreaks of healthcare-associated infection.

Objective To identify the key factors affecting the risk of medical surges and their coupling relation5 ships,providing strategic support for medical institutions to optimize risk management and emergency governance.Methods 17 influencing factors were determined based on WSR theory,and an expert scoring method was employed to assess the impact strength among the factors.The DEMATEL method was applied to calculate the centrality,cau5 sality,influence,and being influenced degrees of the influencing factors.The ISM method was used to construct a hierarchical structure of the influencing factors related to medical surge risks,thereby revealing the connections and interaction mechanisms among these factors.Results Seven critical influencing factors were identified,including the crisis decision-making capacity and leadership effectiveness of emergency managers,the completeness of the emer5 gency system and dynamic execution capabilities,and the cross-departmental coordination mechanism and com5 mand collaboration efficiency.Deep driving factors and coupling pathways were also revealed.Conclusion The risk of medical surges exhibits multi-factorial coupling cascade effects;attention should be directed towards the construc5 tion of mid-to-deep level mechanisms such as information systems,institutional frameworks,and organizational management,to enhance targeted capabilities and systemic resilience in risk governance.

Objective Exploring the components of complex scenarios of healthcare surges triggered by major epidemics to provide a theorical basis for building resilience in healthcare organizations.Methods A hybrid analysis method is used to summarize macro-meso-micro multi-level and multi-source heterogeneous information,extract the elements of complex scenarios of medical surge and evaluate the rationality.Fault Tree Analysis method is used to clarify the logical relationship between various scenario elements and construct scenario reasoning paths.Results 10 scenario states,11 disaster-bearing,24 emergency management and 23 scenario results are summarized and extracted to form the key elements of complex surge scenarios.Among them,M4 expansion and coordinated scheduling of key positions,B2 conventional drug inventory emergency/insufficient core treatment drugs,B emergency medical material transportation breakage,S3 disease symptom spectrum shift to severe disease,R13 prevention and control awareness laxity,and M5 media information dissemination management are the key driving factors that promote a major turning point in the scenario.The most positive scenario result is the orderly operation of the medical service system,and the most negative scenario result is the paralysis of the medical service system.Conclusion Medical institutions need to improve emergency plans based on the complex evolution scenarios of medical surges and agile governance capabilities targeting key turning points,focus on dynamically expanding and scheduling personnel in key positions,strengthen material rotation and reserve mechanisms,maintain smooth emergency logistics channels,and improve efficient management of media and public opinion,so as to comprehensively improve overall resilience.