Medical institutions, with their clinical practice foundation and abundant human use experience data, have become important carriers for the inheritance and innovation of traditional Chinese medicine(TCM) and the "cradles" of the preparation of new TCM. To effectively promote the transformation of new TCM originating from the TCM clinical practice in medical institutions and establish an effective evaluation index system for the transformation of new TCM conforming to the characteristics of TCM, consensus experts adopted the literature research, questionnaire survey, Delphi method, etc. By focusing on the policy and technical evaluation of new TCM originating from the TCM clinical practice in medical institutions, a comprehensive evaluation from the dimensions of drug safety, efficacy, feasibility, and characteristic advantages was conducted, thus forming a comprehensive evaluation system with four primary indicators and 37 secondary indicators. The expert consensus reached aims to encourage medical institutions at all levels to continuously improve the high-quality research and development and transformation of new TCM originating from the TCM clinical practice in medical institutions and targeted at clinical needs, so as to provide a decision-making basis for the preparation, selection, cultivation, and transformation of new TCM for medical institutions, improve the development efficiency of new TCM, and precisely respond to the public medication needs.
Medicine, Chinese Traditional/standards* ; Humans ; Consensus ; Drugs, Chinese Herbal/therapeutic use* ; Surveys and Questionnaires

BACKGROUND: The clinical impact of post-percutaneous coronary intervention (PCI) quantitative flow ratio (QFR) in patients treated with PCI for chronic total occlusion (CTO) was still undetermined. METHODS: All CTO vessels treated with successful anatomical PCI in patients from PANDA III trial were retrospectively measured for post-PCI QFR. The primary outcome was 2-year vessel-oriented composite endpoints (VOCEs, composite of target vessel-related cardiac death, target vessel-related myocardial infarction, and ischemia-driven target vessel revascularization). Receiver operator characteristic curve analysis was conducted to identify optimal cutoff value of post-PCI QFR for predicting the 2-year VOCEs, and all vessels were stratified by this optimal cutoff value. Cox proportional hazards models were employed to calculate the hazard ratio (HR) with 95% CI. RESULTS: Among 428 CTO vessels treated with PCI, 353 vessels (82.5%) were analyzable for post-PCI QFR. 31 VOCEs (8.7%) occurred at 2 years. Mean value of post-PCI QFR was 0.92 ± 0.13. Receiver operator characteristic curve analysis shown the optimal cutoff value of post-PCI QFR for predicting 2-year VOCEs was 0.91. The incidence of 2-year VOCEs in the vessel with post-PCI QFR < 0.91 (n = 91) was significantly higher compared with the vessels with post-PCI QFR ≥ 0.91 (n = 262) (22.0% vs. 4.2%, HR = 4.98, 95% CI: 2.32-10.70). CONCLUSIONS Higher post-PCI QFR values were associated with improved prognosis in the PCI practice for coronary CTO. Achieving functionally optimal PCI results (post-PCI QFR value ≥ 0.91) tends to get better prognosis for patients with CTO lesions.

Objective:To explore the quantitative indexes of dual energy spectrum CT and related clinical data to establish a predictive model for predicting pneumoconiosis.Methods:In April 2024, the information of 203 pneumoconiosis patients diagnosed by the occupational disease appraisal expert group in the Third People's Hospital of Xinjiang Uygur Autonomous Region (Occupational Disease Hospital of Xinjiang Autonomous Region) from January 2022 to December 2023 was retrospectively analyzed. Another 207 non-pneumoconiosis patients with dust exposure history were selected as control group. The measurement data between the two groups were compared using T test or Wilcoxon in dependent quality test, count date asing chi-square or Fishers test, the energy spectrum related indicators and clinical indicators of the patients were compared between groups, and potential factors for diagnosis of pneumoconiosis were screened through univariate analysis, and independent risk factors were further determined by multivariate logistic regression. Based on the results of regression analysis, the machine learning model was constructed, and the reciver operating characteristic curve (ROC) was drawn to evaluate the efficiency of the model, and the Area under cruve (AUC) value, sensitivity and specificity were calculated.Results:Smoking, lung tissue mass, silicon dioxide (SiO 2) equivalent total mass and SiO 2 equivalent concentration were the risk factors for pneumoconiosis ( P<0.05) . Multivariate logistic regression analysis showed that smoking, lung tissue mass, total lung SiO 2 equivalent total volume and total lung SiO 2 equivalent total mass were independent predicators of the diagnosis of pneumoconiosis ( OR=0.53, 0.99, 1.13, 0.85, P<0.05) . Logistic regression machine learning was used to establish a predictive model, and the training set AUC was 0.74, and the verification set AUC was 0.72, indicating that the model had good accuracy and certain ability to diagnose pneumoconiosis. Conclusion:The machine learning prediction model established by the quantitative analysis index of dual energy spectrum CT and clinical related indexes has a good diagnostic performance for the diagnosis of pneumoconiosis.

Objective:To explore the quantitative indexes of dual energy spectrum CT and related clinical data to establish a predictive model for predicting pneumoconiosis.Methods:In April 2024, the information of 203 pneumoconiosis patients diagnosed by the occupational disease appraisal expert group in the Third People's Hospital of Xinjiang Uygur Autonomous Region (Occupational Disease Hospital of Xinjiang Autonomous Region) from January 2022 to December 2023 was retrospectively analyzed. Another 207 non-pneumoconiosis patients with dust exposure history were selected as control group. The measurement data between the two groups were compared using T test or Wilcoxon in dependent quality test, count date asing chi-square or Fishers test, the energy spectrum related indicators and clinical indicators of the patients were compared between groups, and potential factors for diagnosis of pneumoconiosis were screened through univariate analysis, and independent risk factors were further determined by multivariate logistic regression. Based on the results of regression analysis, the machine learning model was constructed, and the reciver operating characteristic curve (ROC) was drawn to evaluate the efficiency of the model, and the Area under cruve (AUC) value, sensitivity and specificity were calculated.Results:Smoking, lung tissue mass, silicon dioxide (SiO 2) equivalent total mass and SiO 2 equivalent concentration were the risk factors for pneumoconiosis ( P<0.05) . Multivariate logistic regression analysis showed that smoking, lung tissue mass, total lung SiO 2 equivalent total volume and total lung SiO 2 equivalent total mass were independent predicators of the diagnosis of pneumoconiosis ( OR=0.53, 0.99, 1.13, 0.85, P<0.05) . Logistic regression machine learning was used to establish a predictive model, and the training set AUC was 0.74, and the verification set AUC was 0.72, indicating that the model had good accuracy and certain ability to diagnose pneumoconiosis. Conclusion:The machine learning prediction model established by the quantitative analysis index of dual energy spectrum CT and clinical related indexes has a good diagnostic performance for the diagnosis of pneumoconiosis.

Background: Triglyceride glucose (TyG) and TyG-body mass index (TyG-BMI) are reliable surrogate indices of insulin resistance and used for risk stratification and outcome prediction in patients with atherosclerotic cardiovascular disease (ASCVD). Here, we inserted estimated average glucose (eAG) into the TyG (TyAG) and TyG-BMI (TyAG-BMI) as derived parameters and explored their clinical significance in cardiovascular risk prediction. Methods: This was a population-based cohort study of 9,944 Chinese patients with ASCVD. The baseline admission fasting glucose and A1C-derived eAG values were recorded. Cardiovascular events (CVEs) that occurred during an average of 38.5 months of follow-up were recorded. We stratified the patients into four groups by quartiles of the parameters. Baseline data and outcomes were analyzed. Results: Distribution of the TyAG and TyAG-BMI indices shifted slightly toward higher values (the right side) compared with TyG and TyG-BMI, respectively. The baseline levels of cardiovascular risk factors and coronary severity increased with quartile of TyG, TyAG, TyG-BMI, and TyAG-BMI (all P<0.001). The multivariate-adjusted hazard ratios for CVEs when the highest and lowest quartiles were compared from low to high were 1.02 (95% confidence interval [CI], 0.77 to 1.36; TyG), 1.29 (95% CI, 0.97 to 1.73; TyAG), 1.59 (95% CI, 1.01 to 2.58; TyG-BMI), and 1.91 (95% CI, 1.16 to 3.15; TyAG-BMI). The latter two showed statistical significance. Conclusion This study suggests that TyAG and TyAG-BMI exhibit more information than TyG and TyG-BMI in disease progression among patients with ASCVD. The TyAG-BMI index provided better predictive performance for CVEs than other parameters.

Background: Triglyceride glucose (TyG) and TyG-body mass index (TyG-BMI) are reliable surrogate indices of insulin resistance and used for risk stratification and outcome prediction in patients with atherosclerotic cardiovascular disease (ASCVD). Here, we inserted estimated average glucose (eAG) into the TyG (TyAG) and TyG-BMI (TyAG-BMI) as derived parameters and explored their clinical significance in cardiovascular risk prediction. Methods: This was a population-based cohort study of 9,944 Chinese patients with ASCVD. The baseline admission fasting glucose and A1C-derived eAG values were recorded. Cardiovascular events (CVEs) that occurred during an average of 38.5 months of follow-up were recorded. We stratified the patients into four groups by quartiles of the parameters. Baseline data and outcomes were analyzed. Results: Distribution of the TyAG and TyAG-BMI indices shifted slightly toward higher values (the right side) compared with TyG and TyG-BMI, respectively. The baseline levels of cardiovascular risk factors and coronary severity increased with quartile of TyG, TyAG, TyG-BMI, and TyAG-BMI (all P<0.001). The multivariate-adjusted hazard ratios for CVEs when the highest and lowest quartiles were compared from low to high were 1.02 (95% confidence interval [CI], 0.77 to 1.36; TyG), 1.29 (95% CI, 0.97 to 1.73; TyAG), 1.59 (95% CI, 1.01 to 2.58; TyG-BMI), and 1.91 (95% CI, 1.16 to 3.15; TyAG-BMI). The latter two showed statistical significance. Conclusion This study suggests that TyAG and TyAG-BMI exhibit more information than TyG and TyG-BMI in disease progression among patients with ASCVD. The TyAG-BMI index provided better predictive performance for CVEs than other parameters.

Background: Triglyceride glucose (TyG) and TyG-body mass index (TyG-BMI) are reliable surrogate indices of insulin resistance and used for risk stratification and outcome prediction in patients with atherosclerotic cardiovascular disease (ASCVD). Here, we inserted estimated average glucose (eAG) into the TyG (TyAG) and TyG-BMI (TyAG-BMI) as derived parameters and explored their clinical significance in cardiovascular risk prediction. Methods: This was a population-based cohort study of 9,944 Chinese patients with ASCVD. The baseline admission fasting glucose and A1C-derived eAG values were recorded. Cardiovascular events (CVEs) that occurred during an average of 38.5 months of follow-up were recorded. We stratified the patients into four groups by quartiles of the parameters. Baseline data and outcomes were analyzed. Results: Distribution of the TyAG and TyAG-BMI indices shifted slightly toward higher values (the right side) compared with TyG and TyG-BMI, respectively. The baseline levels of cardiovascular risk factors and coronary severity increased with quartile of TyG, TyAG, TyG-BMI, and TyAG-BMI (all P<0.001). The multivariate-adjusted hazard ratios for CVEs when the highest and lowest quartiles were compared from low to high were 1.02 (95% confidence interval [CI], 0.77 to 1.36; TyG), 1.29 (95% CI, 0.97 to 1.73; TyAG), 1.59 (95% CI, 1.01 to 2.58; TyG-BMI), and 1.91 (95% CI, 1.16 to 3.15; TyAG-BMI). The latter two showed statistical significance. Conclusion This study suggests that TyAG and TyAG-BMI exhibit more information than TyG and TyG-BMI in disease progression among patients with ASCVD. The TyAG-BMI index provided better predictive performance for CVEs than other parameters.

In recent years,with the development of the bio-psycho-social medical model,more and more attention has been paid to the relationship between psycho-emotional factors and liver.According to traditional Chinese medicine theory,the liver is mainly responsible for catharsis and regulating emotion,which is closely related to emotion.Epidemiological studies have shown that all kinds of liver diseases are accompanied by different degrees of mental disorders,and mental and emotional abnormalities may promote the occurrence of liver diseases and affect the prognosis.Liver and emotion have a common pathogenesis in pathology,involving the dysfunction of nervous,endocrine and immune systems.Based on the basic theory of traditional Chinese medicine and modern medical research,this review analyzes the correlation between emotions and liver.At the same time,neurotransmitters,inflammatory cytokines,brain-derived neurotrophic factor(BDNF),soluble epoxide hydrolase(sEH),intestinal microecology,hypothalamic-pituitary-adrenal(HPA)axis and hypothalamic-pituitary-thyroid(HPT)axis,which summarizes the potential mechanisms of liver disease complicated with emotional disorders,and provides certain reference value for future theoretical research and clinical treatment.

Objective To investigate the incidence rate,clinical characteristics,and prognosis of neonatal stroke in Shenzhen,China.Methods Led by Shenzhen Children's Hospital,the Shenzhen Neonatal Data Collaboration Network organized 21 institutions to collect 36 cases of neonatal stroke from January 2020 to December 2022.The incidence,clinical characteristics,treatment,and prognosis of neonatal stroke in Shenzhen were analyzed.Results The incidence rate of neonatal stroke in 21 hospitals from 2020 to 2022 was 1/15 137,1/6 060,and 1/7 704,respectively.Ischemic stroke accounted for 75％(27/36);boys accounted for 64％(23/36).Among the 36 neonates,31(86％)had disease onset within 3 days after birth,and 19(53％)had convulsion as the initial presentation.Cerebral MRI showed that 22 neonates(61％)had left cerebral infarction and 13(36％)had basal ganglia infarction.Magnetic resonance angiography was performed for 12 neonates,among whom 9(75％)had involvement of the middle cerebral artery.Electroencephalography was performed for 29 neonates,with sharp waves in 21 neonates(72％)and seizures in 10 neonates(34％).Symptomatic/supportive treatment varied across different hospitals.Neonatal Behavioral Neurological Assessment was performed for 12 neonates(33％,12/36),with a mean score of(32±4)points.The prognosis of 27 neonates was followed up to around 12 months of age,with 44％(12/27)of the neonates having a good prognosis.Conclusions Ischemic stroke is the main type of neonatal stroke,often with convulsions as the initial presentation,involvement of the middle cerebral artery,sharp waves on electroencephalography,and a relatively low neurodevelopment score.Symptomatic/supportive treatment is the main treatment method,and some neonates tend to have a poor prognosis.

Humans ; Liver Cirrhosis ; Liver Diseases ; Liver Neoplasms ; Carcinoma, Hepatocellular