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 investigate the effect and mechanism of transplantation of human umbilical cord mesenchymal stem cell （hUC-MSC） with overexpression of the Numb gene in the treatment of cholestatic liver fibrosis （CLF）. MethodsThe technique of lentiviral transfection was used to induce the overexpression of the Numb gene in hUC-MSC （hUC-MSC^Numb-OE）， and hUC-MSC transfected with empty vector （hUC-MSC^OE-EV） was used as negative control. Bile duct ligation （BDL） was performed to establish a rat model of CLF， and then the rats were randomly divided into BDL group， hUC-MSC group， hUC-MSC^OE-EV group， and hUC-MSC^Numb-OE group， while a sham-operation group was also established. The rats in the intervention groups were given a single splenic injection of the corresponding cells after BDL， and samples were collected at the end of week 4. Related indicators were measured， including serum biochemistry， liver histopathology， the content of hydroxyproline （Hyp） in the liver， hepatic stellate cell activation， ductular reaction， liver regeneration， and the expression levels of key molecules in the Numb-p53 signaling axis. A one-way analysis of variance was used for comparison of continuous data between multiple groups， and the least significant difference t-test was used for further comparison between two groups. ResultsCompared with the BDL group， the hUC-MSC group and the hUC-MSC^OE-EV group had significant reductions in the levels of serum biochemical parameters （aspartate aminotransferase， gamma-glutamyl transpeptidase， total bile acid， total bilirubin， and direct bilirubin）， liver fibrosis markers （the content of Hyp and the expression levels of alpha-smooth muscle actin， tumor necrosis factor-α， and transforming growth factor-beta 1）， and ductular reaction markers （the expression levels of CK7 and CK19） （all P <0.05）， and compared with the hUC-MSC^OE-EV group， the hUC-MSC^Numb-OE group had significantly greater improvements in the above indicators （all P <0.05）. In addition， compared with the hUC-MSC^OE-EV group， the hUC-MSC^Numb-OE group had significant improvements in the expression levels of liver regeneration-related markers （albumin and hepatocyte nuclear factor 4α） and the molecules associated with the Numb-p53 signaling axis （Numb， pNumb， Mdm2， and p53） （all P <0.05）. ConclusionOverexpression of the Numb gene can enhance the therapeutic effect of hUC-MSC on CLF， possibly by activating the Numb-PTB^L-p53-HNF4α axis， promoting the hepatic differentiation of hUC-MSCs and subsequently enhancing liver regeneration.

ObjectiveTo investigate the effects of jatrorrhizine on endogenous metabolites and metabolic pathways in the mouse model of ulcerative colitis. MethodsThirty male C57BL/6J mice were randomly divided into the normal group， the model group， the low-dose and high-dose jatrorrhizine groups （0.04， 0.16 g·kg^-1）， and the mesalazine group （0.52 g·kg^-1）The mouse model of ulcerative colitis was established with 3% dextran sulfate sodium （DSS） and treated with different doses of jatrorrhizine by gavage. The changes in body weight， colon length， disease activity index （DAI）， and colonic histopathology were analyzed to evaluate the therapeutic effects of jatrorrhizine. UPLC-Q-TOF/MS was employed to determine the serum and fecal levels of metabolites in mice. Metabolomics methods were used to screen the differential metabolites， on the basis of which the potential therapeutic mechanism of jatrorrhizine on DSS-induced ulcerative colitis in mice was investigated. ResultsAfter intervention with jatrorrhizine， the model mice showed significantly decreased DAI（P<0.05，P<0.01）， recovered colon length，（P<0.05，P<0.01） and alleviated histopathology of the colon. The metabolomics study screened out 13 differential metabolites in the serum and 8 differential metabolites in the feces. The pathway enrichment analysis predicted three potential metabolic pathways： Biosynthesis of unsaturated fatty acids， phenylalanine， tyrosine and tryptophan biosynthesis， and phenylalanine metabolism. ConclusionJatrorrhizine may treat ulcerative colitis by regulating the biosynthesis and metabolism of amino acids and the synthesis of unsaturated fatty acids.

ObjectiveTo explore the potential categories and characteristics of the public hospice care demand in Hainan Province， and analyze different potential types of influencing factors， so as to provide reference for relevant departments to improve the public awareness and demand of hospice care. MethodsUsing convenience sampling method， select 6484 cities of the public as the survey object， using the general data questionnaire， the hospice care demand questionnaire of the potential profile analysis， and analyze the influencing factors of the public hospice care demand category. ResultsThe characteristics of the hospice care demand in Hainan Province were divided into three potential categories： low demand group （14.19%）， medium demand group （49.99%） and high demand group （35.82%）. Multivariate analysis showed that gender， age， education level， cultural belief， and life-death education experience were the main influencing factors of public hospice care demand （p<0.05）. Males， those aged 41-60 years， and those with high school education or below had relatively lower hospice care demand， while those with life-death education experience had relatively higher demand. ConclusionRelevant departments should focus on hospice care knowledge popularization and demand enhancement for males， middle-aged groups， and people with low education levels， while strengthening universal life-death education through stratified and classified publicity strategies and educational interventions to improve different populations’ awareness and acceptance of hospice care.

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.

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: To explore the application value and clinical effect of 3D printing combined with customized bone plate in the treatment of acetabular fracture. METHODS: From June 2020 to June 2022, 11 patients with acetabular fractures underwent preoperative planning using 3D printing technology and were treated with customized bone plates including 8 males and 3 females, aged 25 to 66 years old. The fractures were classified according to Letournel-Judet:4 posterior wall fractures, 2 T-type fractures, 2 transverse posterior wall fractures, 2 double column fractures, and 1 anterior column with posterior semi-transverse fractures. The operative time, intraoperative blood loss, intraoperative fluoroscopy times, postoperative drainage volume, postoperative fracture healing time, and hip function score were recorded and analyzed. RESULTS: The operation time of 11 patients was 80 to 150 min, intraoperative blood volume was 150 to 700 ml, fluoroscopy frequency was 2 to 6, postoperative drainage flow was 60 to 195 ml, and the fracture healing time was 2.5 to 6.0 months. Fracture reduction was evaluated according to Matta score:anatomical reduction in 3 cases and satisfactory reduction in 8 cases. Eleven patients were followed up for 7 to 18 months. The hip Merle d'Aubigne function scores were excellent in 6 cases, good in 3 cases, fair in 1 case and poor in 1 case. Incision fat liquefaction occurred in 1 case and obturator nerve traction in 1 case. CONCLUSION The application of 3D printing technology combined with customized bone plates in the treatment of acetabular fracture is effective. In addition, the printed model can provide the operator with the results of the three-dimensional shape of the fracture, which is convenient for surgical reduction and effectively improves the efficiency of surgery.
Humans ; Female ; Male ; Middle Aged ; Acetabulum/surgery* ; Printing, Three-Dimensional ; Adult ; Aged ; Bone Plates ; Fractures, Bone/surgery* ; Fracture Fixation, Internal/methods*

OBJECTIVE: To summarize the early clinical results and safety of Stand-alone OLIF application of lumbar lesions, and explored its surgical indications. METHODS: Total of 92 cases of lumbar spine lesions treated with Stand-alone OLIF at two medical centers from October 2014 to December 2018 were retrospectively analyzed, including 30 males and 62 females with an average age of (61.20±12.94) years old ranged from 32 to 83 years old. There were 20 cases of lumbar spinal stenosis, 15 cases of lumbar disc degeneration, 11 cases of lumbar degenerative spondylolisthesis, 6 cases of discogenic low back pain, 7 cases of giant lumbar disc herniation, 13 cases of primary lumbar discitis, 6 cases of adjacent vertebral disease after lumbar internal fixation surgery, and 14 cases of degenerative lumbar scoliosis. Pre-operative dual energy X-ray bone density examination 31 cases' T-values ranged from -1 to -2.4, 8 cases' T-values ranged from -2.5 to -3.5, and the rest had normal bone density. The number of fusion segments: 68 cases of single segment, 9 cases of two segment, 12 cases of three segment , and 3 cases of four segment. Fusion site:L_1,2 1 case, L_2,3 4 cases, L_3,4 10 cases, L_4,5 53 cases, L_2,3-L_3,4 3 cases, L_3,4-L_4,5 6 cases, L_1,2L_2,3L_3,4 1 case, L_1,2L_3,4L_4,5 1 case, L_2,3L_3,4L_4,5 10 cases, L_1,2L_2,3L_3,4L_4,5 3 cases. The clinical results and imaging results of this group of cases were observed, as well as the complications. RESULTS: The surgical time ranged from 40 to 140 minutes with an average of (60.92±27.40) minutes. The intraoperative bleeding volume was 20 to 720 ml with an average of (68.22±141.60) ml. The patients had a follow-up period of 6 to 84 months with an average of (38.50±12.75) months. The height of the intervertebral space recovered from (9.23±1.94) mm in preoperative to (12.68±2.01) mm in postoperative, and (9.11±1.72) mm at the last follow-up, there was a statistically significant difference(F=6.641, P=0.008);there was also a statistically significant difference between the postoperative and preoperative height of the intervertebral space(t=9.27, P<0.000 1);and there was also a statistically significant difference (t=10.06, P<0.000 1) between the last follow-up and postoperative height of the intervertebral space. At the last follow-up, cage subsidence grading was as follows:level 0 in 69 cases (76 segments), levelⅠin 17 cases (43 segments), level Ⅱin 5 cases (14 segments), and level Ⅲ in 1 case (1 segment);according to the number of segments, normal subsidence accounts for 56.72%, abnormal subsidence accounts for 43.28%. Bone mineral desity of normal subsidence groups was -0.50±0.07 whinch was better than that the abnormal subsidence groups -2.10±0.43, and the difference was statistically significant(χ²=2.275, P=0.014). As well as there was a statistically significant difference in the patient's VAS of backache from (6.28±2.11) in preoperative to (1.48±0.59) in last follow-up(t=8.56, P<0.05). The ODI recovered from (36.30±7.52)% before surgery to (10.20±2.50)% at the last follow-up, with a statistically significant difference (t=7.79, P<0.000 1). Complications involved 4 cases of intraoperative vascular injury, 21 cases of endplate injury, and 4 cases of combined vertebral fractures. The incision skin has no necrosis or infection. There were 4 cases of left sympathetic chain injury, 4 cases of transient left hip flexion weakness, 2 cases of left thigh anterolateral numbness with quadriceps femoris weakness, and 1 case of incomplete intestinal obstruction;8 cases were treated with posterior pedicle screw fixation due to fusion cage settlement accompanied by stubborn lower back pain, and 6 cases were treated with fusion cage settlement and lateral displacement. According to the actual number of cases, there were 38 complications, with an incidence rate of 41.3%. CONCLUSION The application of Stand alone OLIF in lumbar spine disease fusion has achieved good early results, with obvious clinical advantages, but also there are high probability of complications. It is recommended to choose carefully. It is necessary to continuously summarize and gradually clarify and complete the surgical indications and specific case selection criteria.
Humans ; Male ; Female ; Middle Aged ; Spinal Fusion/methods* ; Lumbar Vertebrae/injuries* ; Aged ; Adult ; Retrospective Studies ; Aged, 80 and over