Cardiac fibrosis(CF) is a cardiac pathological process characterized by excessive deposition of extracellular matrix(ECM). When the heart is damaged by adverse stimuli, cardiac fibroblasts are activated and secrete a large amount of ECM, leading to changes in cardiac fibrosis, myocardial stiffness, and cardiac function declines and accelerating the development of heart failure. There is a close relationship between heart yin deficiency and cardiac fibrosis, which have similar pathogenic mechanisms. Heart Yin deficiency, characterized by insufficient Yin fluids, causes the heart to lose its nourishing function, which acts as the initiating factor for myocardial dystrophy. The deficiency of body fluids leads to stagnation of blood flow, resulting in blood stasis and water retention. Blood stasis and water retention accumulate in the heart, which aligns with the pathological manifestation of excessive deposition of ECM, as a tangible pathogenic factor. This is an inevitable stage of the disease process. The lingering of blood stasis combined with water retention eventually leads to the generation of heat and toxins, triggering inflammatory responses similar to heat toxins, which continuously stimulate the heart and cause the ultimate outcome of CF. Considering the syndrome of heart Yin deficiency, traditional Chinese medicine capable of nourishing Yin, activating blood, and promoting urination can reduce myocardial cell apoptosis, inhibit fibroblast activation, and lower the inflammation level, showing significant advantages in combating CF.
Humans ; Fibrosis/drug therapy* ; Animals ; Yin Deficiency/metabolism* ; Myocardium/metabolism* ; Medicine, Chinese Traditional ; Drugs, Chinese Herbal/therapeutic use*

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 investigate the role of the neutrophil-to-lymphocyte ratio(NLR)in predicting the in-hospital mortality risk of patients with acute aortic dissection(AAD)in multicenter hospitals.Methods A multicenter retrospective cohort study was conducted.Clinical data were collected from 2642 AAD patients who were hospitalized in five teaching hospitals:Tongji Hospital Affiliated to Tongji Medical College of Huazhong University of Science and Technology,Henan Provincial People's Hospital,Fuwai Central China Cardiovascular Hospital,the Third Affiliated Hospital of Xinxiang Medical University,and the Second Affiliated Hospital of Chongqing Medical University between August 2010 and December 2021.According to the quartiles of serum NLRlevels,the patients were divided into four groups:first quartile(Q1,n=660),second quartile(Q2,n=661),third quartile(Q3,n=661),and fourth quartile(Q4,n=660).The clinical characteristics and biochemical indicators of each group were compared.Partial correlation analysis was used to assess the relationship between NLR and cardiovascular parameters.Restricted cubic splines,Kaplan-Meier survival analysis,and Cox regression models were employed to evaluate the association between NLR levels and in-hospital mortality risk in AAD patients.Results The median age of all patients was 54[interquartile range(IQR):46-63]years,including 2096 males and 546 females.Compared with Q1-Q3 groups,patients inQ4group had a lower incidence of smoking history and diabetes history,and were more likely to have DeBakey type Ⅰ AAD(P＜0.05).Additionally,the levels of aspartate aminotransferase,high-density lipoprotein cholesterol,creatinine,and D-dimer in Q4 group were higher,while the levels of triglycerides and C-reactive protein(CRP)were lower(P＜0.01).The results of partial correlation analysis showed that the plasma NLR level was positively correlated with D-dimer(r=0.43,P＜0.01)and creatinine(r=0.16,P＜0.01).The restricted cubic spline function in the Cox model revealed a significant non-linear relationship between the plasma NLR level and clinical outcomes in AAD patients(P＜0.01).Kaplan-Meier survival analysis indicated that patients in Q4 group had the highest in-hospital mortality rate compared with Q1-Q3 groups(P＜0.0001).Furthermore,multivariate Cox regression analysis demonstrated that compared with Q1 group,the hazard ratio(HR)of NLR in Q4 group was 1.77(95％CI 1.33-2.37,P＜0.001),which was an independent risk factor for the primary endpoint events.Conclusion A higher plasma NLR level is significantly associated with the occurrence of cardiovascular events in AAD patients,and this association remains significant even after adjusting for potential confounding factors such as the multicenter visiting hospitals.

BACKGROUND: Severe stroke has high rates of mortality and morbidity. This study aimed to investigate the clinical course, causes of worsening, and outcomes of severe ischemic stroke. METHODS: This prospective, multicenter cohort study enrolled adult patients admitted ≤30 days after ischemic stroke from nine hospitals in China between September 2017 and December 2019. Severe stroke was defined as a score of ≥15 on the National Institutes of Health Stroke Scale (NIHSS). Clinical worsening was defined as an increase of 4 in the NIHSS score from baseline. Unfavorable functional outcome was defined as a modified Rankin scale score ≥3 at 3 months and 1 year after stroke onset, respectively. We performed Logistic regression to explore baseline features and reperfusion therapies associated with clinical worsening and functional outcomes. RESULTS: Among 4201 patients enrolled, 854 patients (20.33%) had severe stroke on admission. Of 3347 patients without severe stroke on admission, 142 (4.24%) patients developed severe stroke in hospital. Of 854 patients with severe stroke on admission, 33.95% (290/854) experienced clinical worsening (median time from stroke onset: 43 h, Q1-Q3: 20-88 h), with brain edema (54.83% [159/290]) as the leading cause; 24.59% (210/854) of these patients died by 30 days, and 81.47% (677/831) and 78.44% (633/807) had unfavorable functional outcomes at 3 months and 1 year respectively. Reperfusion reduced the risk of worsening (adjusted odds ratio [OR]: 0.24, 95% confidence interval [CI]: 0.12-0.49, P  <0.01), 30-day death (adjusted OR: 0.22, 95% CI: 0.11-0.41, P  <0.01), and unfavorable functional outcomes at 3 months (adjusted OR: 0.24, 95% CI: 0.08-0.68, P <0.01) and 1 year (adjusted OR: 0.17, 95% CI: 0.06-0.50, P  <0.01). CONCLUSIONS: Approximately one-fifth of patients with ischemic stroke had severe neurological deficits on admission. Clinical worsening mainly occurred in the first 3 to 4 days after stroke onset, with brain edema as the leading cause of worsening. Reperfusion reduced the risk of clinical worsening and improved functional outcomes. REGISTRATION ClinicalTrials.gov , NCT03222024.
Humans ; Male ; Female ; Prospective Studies ; Ischemic Stroke/mortality* ; Aged ; Middle Aged ; Aged, 80 and over ; Stroke ; Brain Ischemia

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 effect of Xiongcan Yishen Formula on ferroptosis in testicular tissue of male rats with late-onset hypogonadism(LOH).Methods A total of 48 male Sprague-Dawley rats aged 6 months were randomly divided into model group,low-dose and high-dose Xiongcan Yishen Formula groups and propionate testosterone group,with 12 rats in each group.An additional 6 male Sprague-Dawley rats aged 8 weeks were set as the normal group.Except for the normal group,the rats were intraperitoneally injected with Cyclophosphamide at a dose of 20 mg/(kg·d)for 5 consecutive days to establish the LOH model,while the normal group received an equal volume of physiological saline for 5 days.The normal group and model group were given equal volumes of distilled water by gavage,while the low-dose and high-dose Xiongcan Yishen Formula groups were administered concentrated decoction at doses of 10.4 g and 41.6 g/kg/d respectively,and the propionate testosterone group received intramuscular injections of 5.21 mg/kg/d propionate testosterone,all for 28 consecutive days.ELISA was used to detect serum testosterone levels in rats,HE staining and transmission electron microscopy were used to observe the gross morphology of testicular tissue and the ultrastructure of Leydig cells,and RT-qPCR and Western blotting were used to detect the expression of ferroptosis-related genes and proteins in testicular tissue.Results The LOH model rats exhibited pathological changes such as atrophy of seminiferous tubules,structural disorder,and reduced spermatocytes in the lumen,as well as ultrastructural changes in Leydig cells including altered nuclear morphology,increased mitochondrial density,and reduced cristae.After intervention with Xiongcan Yishen Formula and propionate testosterone,the pathological changes in testis and the ultrastructure of Leydig cells were improved.Compared with the normal group,serum testosterone levels in the model group were significantly decreased(P＜0.05),the expression of ROS,ACSL4 mRNA and protein in testicular tissue was significantly increased,while the expression of FTH1,GPX4,and SLC7A11 mRNA and protein was significantly decreased(P＜0.05).Compared with the model group,ser-um testosterone levels in the low-dose and high-dose Xiongcan Yishen Formula groups and the propionate testosterone group were significantly increased(P＜0.05),and the expression of ROS,ACSL4 mRNA and protein was significantly decreased(P＜0.05);the high-dose Xiongcan Yishen Formula group showed significantly increased expression of FTH1,GPX4,and SLC7A11 mRNA and protein(P＜0.05).Conclusion Ferroptosis in testicular tissue is increased in LOH rats,and Xiongcan Yishen For-mula can elevate serum testosterone levels and improve pathological changes and ultrastructure in testicular tissue of LOH rats,possibly related to the inhibition of ferroptosis in testicular tissue of LOH rats.

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.