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: 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: 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.

The neurophysiological mechanisms by which exercise improves cognitive function have not been fully elucidated. A comprehensive and systematic review of current domestic and international neurophysiological evidence on exercise improving cognitive function was conducted from multiple perspectives. At the molecular level, exercise promotes nerve cell regeneration and synaptogenesis and maintains cellular development and homeostasis through the modulation of a variety of neurotrophic factors, receptor activity, neuropeptides, and monoamine neurotransmitters, and by decreasing the levels of inflammatory factors and other modulators of neuroplasticity. At the cellular level, exercise enhances neural activation and control and improves brain structure through nerve regeneration, synaptogenesis, improved glial cell function and angiogenesis. At the structural level of the brain, exercise promotes cognitive function by affecting white and gray matter volumes, neural activation and brain region connectivity, as well as increasing cerebral blood flow. This review elucidates how exercise improves the internal environment at the molecular level, promotes cell regeneration and functional differentiation, and enhances the brain structure and neural efficiency. It provides a comprehensive, multi-dimensional explanation of the neurophysiological mechanisms through which exercise promotes cognitive function.
Animals ; Humans ; Brain/physiology* ; Cognition/physiology* ; Exercise/physiology* ; Nerve Regeneration/physiology* ; Neuronal Plasticity/physiology*

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

OBJECTIVES: To investigate the clinical features of children with STAT gene mutations, and to explore corresponding immunotherapy strategies. METHODS: A retrospective analysis was performed for the clinical data of 10 children with STAT gene mutations who were admitted to the Department of Pediatrics of the First Affiliated Hospital of Guangzhou Medical University, from October 2015 to October 2024. Exploratory immunotherapy was implemented in some refractory cases, and the changes in symptoms, imaging manifestations, and cytokine levels were assessed after treatment. RESULTS: For the 10 children, the main clinical manifestations were recurrent rash since birth (7/10), cough (8/10), wheezing (5/10), expectoration (4/10), and purulent nasal discharge (4/10). Genotyping results showed that there was one child with heterozygous loss-of-function (LOF) mutation in the STAT1 gene, four children with heterozygous LOF mutation in the STAT3 gene, and five children with heterozygous gain-of-function (GOF) mutation in the STAT3 gene. Two children with LOF mutation in the STAT3 gene showed decreased interleukin-6 levels and improved clinical symptoms and imaging findings after omalizumab treatment. Three children with GOF mutation in the STAT3 gene achieved effective disease control after treatment with methylprednisolone (0.5 mg/kg per day). Two children with GOF mutation in the STAT3 gene received treatment with JAK inhibitor and then showed some improvement in symptoms. CONCLUSIONS STAT gene mutation screening should be considered for children with recurrent rash and purulent respiratory tract infections. Targeted immunotherapy may improve prognosis in patients with no response to conventional treatment.
Humans ; Male ; Immunotherapy ; Female ; Child, Preschool ; Child ; Gain of Function Mutation ; Retrospective Studies ; Infant ; Loss of Function Mutation ; STAT Transcription Factors/genetics*

OBJECTIVE: To elucidate how spinal manipulative therapy (SMT) exerts its analgesic effects through regulating brain function in lumbar disc herniation (LDH) patients by utilizing resting-state functional magnetic resonance imaging (rs-fMRI). METHODS: From September 2021 to September 2023, we enrolled LDH patients (LDH group, n=31) and age- and sex-matched healthy controls (HCs, n=28). LDH group underwent rs-fMRI at 2 distinct time points (TPs): prior to the initiation of SMT (TP1) and subsequent to the completion of the SMT sessions (TP2). SMT was administered once every other day for 30 min per session, totally 14 treatment sessions over a span of 4 weeks. HCs did not receive SMT treatment and underwent only one fMRI scan. Additionally, participants in LDH group completed clinical questionnaires on pain using the Visual Analog Scale (VAS) and the Japanese Orthopedic Association (JOA) score, whereas HCs did not undergo clinical scale assessments. The effects on the brain were jointly characterized using the amplitude of low-frequency fluctuations (ALFF) and regional homogeneity (ReHo). Correlation analyses were conducted between specific brain regions and clinical scales. RESULTS: Following SMT treatment, pain symptoms in LDH patients were notably alleviated and accompanied by evident activation of effects in the brain. In comparison to TP1, TP2 exhibited the most significant increase in ALFF values for Temporal_Sup_R and the most notable decrease in ALFF values for Paracentral_Lobule_L (voxelwise P<0.005; clusters >30; FDR correction). Additionally, the most substantial enhancement in ReHo values was observed for the Cuneus_R, while the most prominent reduction was noted for the Olfactory_R (voxelwise P<0.005; clusters >30; FDR correction). Moreover, a comparative analysis revealed that, in contrast to HCs, LDH patients at TP1 exhibited the most significant increase in ALFF values for Temporal_Pole_Sup_L and the most notable decrease in ALFF values for Frontal_Mid_L (voxelwise P<0.005; clusters >30; FDR correction). Furthermore, the most significant enhancement in ReHo values was observed for Postcentral_L, while the most prominent reduction was identified for ParaHippocampal_L (voxelwise P<0.005; clusters >30; FDR correction). Notably, correlation analysis with clinical scales revealed a robust positive correlation between the Cuneus_R score and the rate of change in the VAS score (r=0.9333, P<0.0001). CONCLUSIONS Long-term chronic lower back pain in patients with LDH manifests significant activation of the "AUN-DMN-S1-SAN" neural circuitry. The visual network, represented by the Cuneus_R, is highly likely to be a key brain network in which the analgesic efficacy of SMT becomes effective in treating LDH patients. (Trial registration No. NCT06277739).
Humans ; Magnetic Resonance Imaging ; Intervertebral Disc Displacement/diagnostic imaging* ; Male ; Female ; Brain/diagnostic imaging* ; Adult ; Manipulation, Spinal/methods* ; Middle Aged ; Lumbar Vertebrae/physiopathology* ; Pain Management ; Rest ; Case-Control Studies