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.

ObjectiveTo establish a mouse model of rheumatoid arthritis with interstitial lung disease （RA-ILD） in DBA/1 mice using Porphyromonas gingivalis （Pg） infection combined with collagen-induced arthritis （CIA）， and to comprehensively evaluate pathological characteristics in joints， lungs， and serum. MethodsForty DBA/1 mice were randomly divided into four groups， i.e.， Control， Pg infection （Pg）， CIA， and Pg infection combined with CIA （Pg+CIA）， with 10 mice in each group. Arthritis clinical symptoms were evaluated by recording arthritis incidence and clinical scores. Micro-CT scanning was used to assess knee joint pathology. Histopathological changes and collagen deposition in knee joints and lung tissues were analyzed using hematoxylin-eosin （HE） and Masson staining. Immunohistochemistry was performed to detect protein expression of α-smooth muscle actin （α-SMA）， typeⅠ collagen （ColⅠ）， and fibronectin （FN） in lung tissues. Real-time quantitative polymerase chain reaction（Real-time PCR）was used to measure mRNA expression levels of α-SMA， ColⅠ， FN， tumor necrosis factor-α （TNF-α）， interleukin-6 （IL-6）， and IL-1β in lung tissues. Enzyme-linked immunosorbent assay （ELISA） was used to detect serum levels of Pg， cyclic citrullinated peptide （CCP）， and immunoglobulin G （IgG）. ResultsJoint lesions： The CIA and Pg+CIA groups showed 100% arthritis incidence， with evident joint redness， swelling， and deformity. The number of affected limbs was 27 and 28， and clinical scores were 68 and 70， respectively. No obvious clinical symptoms were observed in the Pg group. Histopathological and imaging analyses showed severe joint lesions in the CIA and Pg+CIA groups， with significantly increased histopathological scores， bone mineral density， bone volume fraction， trabecular thickness， and trabecular number compared to the Control group （P<0.01）. No obvious joint pathology was observed in the Pg group. Lung lesions： The Pg+CIA group exhibited marked alveolar inflammation， interstitial inflammatory cell infiltration， and alveolar wall thickening， with pronounced blue staining of collagen fibers. Histopathological scores and collagen area ratios were significantly higher than those of the Control， Pg， and CIA groups （P<0.05）. Lung protein and mRNA expression levels of α-SMA， ColⅠ， and FN were markedly increased， and mRNA levels of IL-6， TNF-α， and IL-1β were significantly elevated compared to the Control group （P<0.05）. Serology： The Pg+CIA group showed significantly higher levels of CCP， Pg， and IgG compared with the Control， Pg， and CIA groups （P<0.05）. ConclusionDBA/1 mice subjected to Pg infection combined with CIA exhibited pronounced symptoms and pathological features of RA-ILD， along with elevated serum anti-CCP antibody levels. This model represents a novel RA-ILD mouse model， providing a valuable experimental tool for investigating RA-ILD pathogenesis and developing new therapeutics， and serves as a basis for establishing anti-cyclic citrullinated peptide antibody （ACPA）-positive RA-ILD animal models.

OBJECTIVE To explore the transformation of the dispensing and drug pickup process in traditional Chinese medicine pharmacy （TCM Pharmacy） in our hospital based on data-intelligence-driven， aiming to improve pharmacists’ work efficiency and patients’ drug pickup experience. METHODS Value stream mapping and journey mapping were used to systematically identify non-value-added links in pharmacists’ dispensing process and key pain points in patients’ drug pickup under the traditional process. An intelligent dispensing and drug pickup system for the TCM Pharmacy was developed based on the C# and Android television platforms， and a machine-learning model was adopted to predict patients’ drug pickup waiting time. A comprehensive evaluation was performed from three perspectives： system performance， prediction accuracy， and satisfaction of pharmacists and patients. RESULTS The system successfully streamlined non-value-added links such as “waiting for writing on the board” and “searching for drugs”， and realized multimodal dynamic prompts of dispensing status through auditory （number calling） and visual （television terminal） channels. The constructed model for predicting drug pickup waiting time exhibited good fitting degree and generalization ability （mean absolute error＝4.28 min， R 2 ＝0.882）. The comprehensive satisfaction scores of pharmacists and patients in the traditional mode were significantly increased from （70.99±1.74） and （73.58±1.98） to （90.02±1.30） and （88.61±2.08） in the new system， respectively （ P ＜0.01）. CONCLUSIONS The transformation of the intelligent drug dispensing and pickup system for TCM pharmacy based on data-intelligence-driven effectively improves the efficiency of pharmacists’ dispensing work， realizes process transparency and waiting time predictability， and significantly enhances patients’ drug pickup experience.

Objective To investigate the probability and dosimetric risk factors of lung injury after sequential immune checkpoint inhibitors (ICIs) and thoracic radiotherapy. Methods A retrospective analysis was conducted on 139 patients who received sequential ICIs and thoracic radiotherapy in Xuzhou Cancer Hospital and Affiliated Hospital of Xuzhou Medical University between February 2020 and February 2024. The relationships of clinical factors and lung and heart volume dose parameters with grade ≥ 2 acute lung injury (ALI) in patients with thoracic tumors were studied using univariable (χ² test, t test, nonparametric test) and multivariable (binary logistic regression analysis) methods. The thresholds of dosimetric risk factors were determined using the receiver operating characteristic curves. Clinical factors included age, gender, smoking history, type of ICIs, cycle of ICI application, and the interval between ICI application and thoracic radiotherapy. Dose parameters included total radiotherapy dose, single dose, planning target volume, maximum dose of planning target volume, average dose of planning target volume, total lung volume, heart volume, and the V₅, V₁₀, V₁₅, V₂₀, V₂₅, V₃₀, V₃₅, and V₄₀ of lung and heart. Results The incidence of grade ≥ 2 ALI in the included cases was 36% (50/139). The χ² test did not find any statistically significant clinical factors. In the univariable and binary Logistic regression analysis, lung V₁₅ and V₂₀, heart V₁₅ and V₂₀, and lung volume were independent risk factors for the occurrence of grade ≥ 2 ALI in sequential ICIs and thoracic radiotherapy. The thresholds were 18.51% for lung V₁₅, 14.43% for lung V₂₀, 32.41% for heart V₁₅, and 17.74% for heart V₂₀. Conclusion For patients who are going to receive thoracic radiotherapy after ICIs, the thresholds of lung V₁₅ and V₂₀ and heart V₁₅ and V₂₀ in the radiotherapy plan are recommended to be less than 18.51%, 14.43%, 32.41%, and 17.74%, respectively, which can effectively reduce the occurrence of grade ≥ 2 ALI.

Objective To explore the risk factors for postoperative respiratory failure (RF) in patients with esophageal cancer, construct a predictive model based on the least absolute shrinkage and selection operator (LASSO)-logistic regression, and visualize the constructed model. Methods A retrospective analysis was conducted on patients with esophageal cancer who underwent surgical treatment in the Department of Thoracic Surgery, Sun Yat-sen University Cancer Center Gansu Hospital from 2020 to 2023. Patients were divided into a RF group and a non-RF (NRF) group according to whether RF occurred after surgery. Clinical data of the two groups were collected, and LASSO-logistic regression was used to optimize feature selection and construct the predictive model. The model was internally validated by repeated sampling 1000 times based on the Bootstrap method. Results A total of 217 patients were included, among which 24 were in the RF group, including 22 males and 2 females, with an average age of (63.33±9.10) years; 193 were in the NRF group, including 161 males and 32 females, with an average age of (62.14±8.44) years. LASSO-logistic regression analysis showed that the percentage of forced expiratory volume in one second/forced vital capacity (FEV1/FVC) to predicted value (FEV1/FVC%pred) [OR=0.944, 95%CI (0.897, 0.993), P=0.026], postoperative anastomotic fistula [OR=4.106, 95%CI (1.457, 11.575), P=0.008], and postoperative lung infection [OR=3.776, 95%CI (1.373, 10.388), P=0.010] were risk factors for postoperative RF in patients with esophageal cancer. Based on the above risk factors, a predictive model was constructed, with an area under the receiver operating characteristic curve of 0.819 [95%CI (0.737, 0.901)]. The Hosmer-Lemeshow test for the calibration curve showed that the model had good goodness of fit (P=0.527). The decision curve showed that the model had good clinical net benefit when the threshold probability was between 5% and 50%. Conclusion FEV1/FVC%pred, postoperative anastomotic fistula, and postoperative lung infection are risk factors for postoperative RF in patients with esophageal cancer. The predictive model constructed based on LASSO-logistic regression analysis is expected to help medical staff screen high-risk patients for early individualized intervention.

ObjectiveTo investigate the efficacy and underlying mechanisms of Gynostemma pentaphyllum alcohol extract in improving glucose and lipid metabolism disorders in db/db mice through transcriptomics and gut microbiota analysis. MethodsEighteen db/db mice were randomly assigned to the model（DM） group， metformin（MET） group， and G. pentaphyllum alcohol extract（GP） group， with six mice in each group， based on stratification of fasting blood glucose and body weight. An additional six db/m mice were selected as the normal control（NC） group. Mice in the NC and DM groups were administered deionized water （10 mL·kg^-1） daily. The MET group received metformin （0.195 g·kg^-1） by gavage. The GP group was treated with G. pentaphyllum alcohol extract （3.9 g·kg^-1） by gavage for six weeks. Fasting blood glucose was measured every two weeks. After six weeks of intervention， serum levels of total cholesterol （TC）， triglycerides （TG）， low-density lipoprotein cholesterol （LDL-C）， high-density lipoprotein cholesterol （HDL-C）， aspartate aminotransferase （AST）， alanine aminotransferase （ALT）， creatinine （CREA）， and blood urea nitrogen （BUN） were assessed. Enzyme-linked immunosorbent assay （ELISA） was used to measure insulin （FINS）， adiponectin （ADP）， and tumor necrosis factor-α （TNF-α）. Hematoxylin-eosin （HE） staining was used to observe liver histomorphology， periodic acid-Schiff （PAS） staining was employed to assess hepatic glycogen synthesis， and Oil Red O staining was used to detect hepatic lipid deposition. Liver transcriptomic data were used to identify differentially expressed genes in the liver and conduct enrichment analysis. Real-time PCR was employed to verify the expression levels of adiponectin gene （Adipoq）， peroxisome proliferator-activated receptor gamma coactivator-1α （PGC-1α）， AMP-activated protein kinase （AMPK）， peroxisome proliferator-activated receptor α （PPARα）， glucokinase （GCK）， forkhead box （Fox）O1， FoxO3， phosphoenolpyruvate carboxykinase （PEPCK）， and glucose-6-phosphatase （G6PC）. Metagenomic sequencing was conducted to analyze changes in gut microbiota composition. ResultsCompared with the NC group， the DM group exhibited significantly elevated fasting blood glucose （P<0.01）， serum AST， ALT， TC， TG， LDL-C， and HDL-C （P<0.01）. FINS， homeostatic model assessment for insulin resistance （HOMA-IR）， and the inflammatory cytokine TNF-α were significantly increased （P<0.01）， while ADP was significantly decreased （P<0.05）. Histological analysis confirmed severe hepatic steatosis and excessive lipid accumulation in the DM group， along with markedly reduced glycogen synthesis. Compared with the DM group， the GP group showed significantly decreased fasting blood glucose （P<0.01）， reduced serum TC， LDL-C， and HDL-C levels （P<0.05）， significantly decreased serum TG and AST levels （P<0.01）， significantly reduced FINS， HOMA-IR， and TNF-α levels （P<0.01）， and significantly increased ADP （P<0.01）. Hepatic steatosis and lipid deposition were significantly alleviated， while glycogen synthesis was markedly enhanced. Transcriptomic differential and enrichment analyses suggested that the mechanisms by which G. pentaphyllum alcohol extract improved hepatic glucose and lipid metabolism in db/db mice may involve regulation of the AMPK and FoxO signaling pathways. Real-time PCR results confirmed that expression of PGC-1α， PEPCK， G6PC， FoxO1， and FoxO3 was significantly downregulated following treatment with G. pentaphyllum alcohol extract （P<0.05， P<0.01）， whereas mRNA expression of Adipoq， PPARα， GCK， and AMPK was significantly upregulated （P<0.05， P<0.01）. Metagenomic analysis showed that the relative abundance of Lactobacillus， Alistipes， and Akkermansia species was higher in the GP group than in the DM group. ConclusionG. pentaphyllum alcohol extract may improve glucose and lipid metabolism disorders in db/db mice by regulating the hepatic AMPK/PPARα pathway to suppress lipid deposition and alleviate hepatic steatosis， by inhibiting gluconeogenesis through the AMPK/PGC-1α and FoxO pathways to lower fasting blood glucose， and by increasing the abundance of beneficial gut bacteria such as Lactobacillus， Alistipes， and Akkermansia to restore gut microbiota balance.

ObjectiveTo investigate the efficacy and underlying mechanisms of Gynostemma pentaphyllum alcohol extract in improving glucose and lipid metabolism disorders in db/db mice through transcriptomics and gut microbiota analysis. MethodsEighteen db/db mice were randomly assigned to the model（DM） group， metformin（MET） group， and G. pentaphyllum alcohol extract（GP） group， with six mice in each group， based on stratification of fasting blood glucose and body weight. An additional six db/m mice were selected as the normal control（NC） group. Mice in the NC and DM groups were administered deionized water （10 mL·kg^-1） daily. The MET group received metformin （0.195 g·kg^-1） by gavage. The GP group was treated with G. pentaphyllum alcohol extract （3.9 g·kg^-1） by gavage for six weeks. Fasting blood glucose was measured every two weeks. After six weeks of intervention， serum levels of total cholesterol （TC）， triglycerides （TG）， low-density lipoprotein cholesterol （LDL-C）， high-density lipoprotein cholesterol （HDL-C）， aspartate aminotransferase （AST）， alanine aminotransferase （ALT）， creatinine （CREA）， and blood urea nitrogen （BUN） were assessed. Enzyme-linked immunosorbent assay （ELISA） was used to measure insulin （FINS）， adiponectin （ADP）， and tumor necrosis factor-α （TNF-α）. Hematoxylin-eosin （HE） staining was used to observe liver histomorphology， periodic acid-Schiff （PAS） staining was employed to assess hepatic glycogen synthesis， and Oil Red O staining was used to detect hepatic lipid deposition. Liver transcriptomic data were used to identify differentially expressed genes in the liver and conduct enrichment analysis. Real-time PCR was employed to verify the expression levels of adiponectin gene （Adipoq）， peroxisome proliferator-activated receptor gamma coactivator-1α （PGC-1α）， AMP-activated protein kinase （AMPK）， peroxisome proliferator-activated receptor α （PPARα）， glucokinase （GCK）， forkhead box （Fox）O1， FoxO3， phosphoenolpyruvate carboxykinase （PEPCK）， and glucose-6-phosphatase （G6PC）. Metagenomic sequencing was conducted to analyze changes in gut microbiota composition. ResultsCompared with the NC group， the DM group exhibited significantly elevated fasting blood glucose （P<0.01）， serum AST， ALT， TC， TG， LDL-C， and HDL-C （P<0.01）. FINS， homeostatic model assessment for insulin resistance （HOMA-IR）， and the inflammatory cytokine TNF-α were significantly increased （P<0.01）， while ADP was significantly decreased （P<0.05）. Histological analysis confirmed severe hepatic steatosis and excessive lipid accumulation in the DM group， along with markedly reduced glycogen synthesis. Compared with the DM group， the GP group showed significantly decreased fasting blood glucose （P<0.01）， reduced serum TC， LDL-C， and HDL-C levels （P<0.05）， significantly decreased serum TG and AST levels （P<0.01）， significantly reduced FINS， HOMA-IR， and TNF-α levels （P<0.01）， and significantly increased ADP （P<0.01）. Hepatic steatosis and lipid deposition were significantly alleviated， while glycogen synthesis was markedly enhanced. Transcriptomic differential and enrichment analyses suggested that the mechanisms by which G. pentaphyllum alcohol extract improved hepatic glucose and lipid metabolism in db/db mice may involve regulation of the AMPK and FoxO signaling pathways. Real-time PCR results confirmed that expression of PGC-1α， PEPCK， G6PC， FoxO1， and FoxO3 was significantly downregulated following treatment with G. pentaphyllum alcohol extract （P<0.05， P<0.01）， whereas mRNA expression of Adipoq， PPARα， GCK， and AMPK was significantly upregulated （P<0.05， P<0.01）. Metagenomic analysis showed that the relative abundance of Lactobacillus， Alistipes， and Akkermansia species was higher in the GP group than in the DM group. ConclusionG. pentaphyllum alcohol extract may improve glucose and lipid metabolism disorders in db/db mice by regulating the hepatic AMPK/PPARα pathway to suppress lipid deposition and alleviate hepatic steatosis， by inhibiting gluconeogenesis through the AMPK/PGC-1α and FoxO pathways to lower fasting blood glucose， and by increasing the abundance of beneficial gut bacteria such as Lactobacillus， Alistipes， and Akkermansia to restore gut microbiota balance.

Objective: This study investigated the value of multiparametric MRI (mpMRI) in predicting Gleason score (GS) upgrading and downgrading in radical prostatectomy (RP) compared with presurgical biopsy. Materials and Methods: Clinical and mpMRI data were retrospectively collected from 219 patients with prostate disease between January 2015 and December 2021. All patients underwent systematic prostate biopsy followed by RP. MpMRI included conventional diffusion-weighted and dynamic contrast-enhanced imaging. Multivariable logistic regression analysis was performed to analyze the factors associated with GS upgrading and downgrading after RP. Receiver operating characteristic curve analysis was used to estimate the area under the curve (AUC) to indicate the performance of the multivariable logistic regression models in predicting GS upgrade and downgrade after RP. Results: The GS after RP was upgraded, downgraded, and unchanged in 92, 43, and 84 patients, respectively. The AUCs of the clinical (percentage of positive biopsy cores [PBCs], time from biopsy to RP) and mpMRI models (prostate cancer [PCa] location, Prostate Imaging Reporting and Data System [PI-RADS] v2.1 score) for predicting GS upgrading after RP were 0.714 and 0.749, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, tPSA, PCa location, and PIRADS v2.1 score) was 0.816, which was larger than that of the clinical factors alone (P < 0.001). The AUCs of the clinical (age, percentage of PBCs, ratio of free/total PSA [F/T]) and mpMRI models (PCa diameter, PCa location, and PI-RADS v2.1 score) for predicting GS downgrading after RP were 0.749 and 0.835, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, F/T, PCa diameter, PCa location, and PI-RADS v2.1 score) was 0.883, which was larger than that of the clinical factors alone (P < 0.001). Conclusion Combining clinical factors and mpMRI findings can predict GS upgrade and downgrade after RP more accurately than using clinical factors alone.

Background: Intraoperative hypercapnia reduces the time to emergence from volatile anesthetics, but few clinical studies have explored the effect of hypercapnia on the emergence time from intravenous (IV) anesthesia. We investigated the effect of inducing mild hypercapnia during the recovery period on the emergence time after total IV anesthesia (TIVA). Methods: Adult patients undergoing transurethral lithotripsy under TIVA were randomly allocated to normocapnia group (end-tidal carbon dioxide [ETCO2] 35–40 mmHg) or mild hypercapnia group (ETCO2 50-55 mmHg) during the recovery period. The primary outcome was the extubation time. The spontaneous breathing-onset time, voluntary eye-opening time, and hemodynamic data were collected. Changes in the cerebral blood flow velocity in the middle cerebral artery were assessed using transcranial Doppler ultrasound. Results: In total, 164 patients completed the study. The extubation time was significantly shorter in the mild hypercapnia (13.9 ± 5.9 min, P = 0.024) than in the normocapnia group (16.3 ± 7.6 min). A similar reduction was observed in spontaneous breathing-onset time (P = 0.021) and voluntary eye-opening time (P = 0.008). Multiple linear regression analysis revealed that the adjusted ETCO2 level was a negative predictor of extubation time. Middle cerebral artery blood flow velocity was significantly increased after ETCO2 adjustment for mild hypercapnia, which rapidly returned to baseline, without any adverse reactions, within 20 min after extubation. Conclusions Mild hypercapnia during the recovery period significantly reduces the extubation time after TIVA. Increased ETCO2 levels can potentially enhance rapid recovery from IV anesthesia.

Objective: This study investigated the value of multiparametric MRI (mpMRI) in predicting Gleason score (GS) upgrading and downgrading in radical prostatectomy (RP) compared with presurgical biopsy. Materials and Methods: Clinical and mpMRI data were retrospectively collected from 219 patients with prostate disease between January 2015 and December 2021. All patients underwent systematic prostate biopsy followed by RP. MpMRI included conventional diffusion-weighted and dynamic contrast-enhanced imaging. Multivariable logistic regression analysis was performed to analyze the factors associated with GS upgrading and downgrading after RP. Receiver operating characteristic curve analysis was used to estimate the area under the curve (AUC) to indicate the performance of the multivariable logistic regression models in predicting GS upgrade and downgrade after RP. Results: The GS after RP was upgraded, downgraded, and unchanged in 92, 43, and 84 patients, respectively. The AUCs of the clinical (percentage of positive biopsy cores [PBCs], time from biopsy to RP) and mpMRI models (prostate cancer [PCa] location, Prostate Imaging Reporting and Data System [PI-RADS] v2.1 score) for predicting GS upgrading after RP were 0.714 and 0.749, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, tPSA, PCa location, and PIRADS v2.1 score) was 0.816, which was larger than that of the clinical factors alone (P < 0.001). The AUCs of the clinical (age, percentage of PBCs, ratio of free/total PSA [F/T]) and mpMRI models (PCa diameter, PCa location, and PI-RADS v2.1 score) for predicting GS downgrading after RP were 0.749 and 0.835, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, F/T, PCa diameter, PCa location, and PI-RADS v2.1 score) was 0.883, which was larger than that of the clinical factors alone (P < 0.001). Conclusion Combining clinical factors and mpMRI findings can predict GS upgrade and downgrade after RP more accurately than using clinical factors alone.