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.

ObjectiveMigraine is a leading neurological disorder and the fourth most common cause of years lived with disability worldwide, affecting nearly 116 million individuals. Although pharmacological treatments are available, their efficacy is often limited by side effects and variable response rates. Repetitive transcranial magnetic stimulation (rTMS) over the dorsolateral prefrontal cortex (DLPFC) offers a safe, non-invasive alternative for migraine management. However, the neurophysiological mechanisms, particularly how rTMS modulates local cortical excitability and distributed pain-related circuits, remain poorly understood. Elucidating these mechanisms is essential for optimizing treatment protocols and improving clinical outcomes. MethodsThis study employed concurrent transcranial magnetic stimulation and electroencephalography (TMS-EEG) to investigate neuroplastic and neurocircuitry mechanisms of DLPFC-rTMS in migraine. Study 1 compared 30 migraineurs and 28 healthy controls to identify abnormalities in TMS-evoked potentials (TEPs) and significant current density (SCD) within sensory-discriminative regions including the primary somatosensory cortex (S1) and posterior insula (pINS), cognitive-affective regions including the anterior insula (aINS) and midcingulate cortex (MCC), and a descending modulatory region, the periaqueductal gray (PAG). Study 2 used a single-blind, crossover, sham-controlled design in 34 healthy participants. Each participant received both active (10 Hz, 80% RMT, 1 500 pulses) and sham DLPFC-rTMS in counterbalanced order. TMS-EEG and cold pain tolerance were assessed before and after each session. ResultsIn Study 1, migraineurs showed a significantly less negative N120 amplitude compared to healthy controls (P=0.027, Cohen’s d=0.60), indicating local intracortical disinhibition. No group differences were observed for N40, P60, or P180 components. At the source level, migraineurs exhibited significantly higher SCD in the S1, pINS, aINS, and MCC (allQ<0.05), but not in the ventroposterior thalamus (vpTHAL), mediodorsal thalamus (mdTHAL), or PAG. In Study 2, active rTMS significantly reduced SCD from pre- to post-stimulation in the S1, aINS, and MCC (all Q<0.05). Sham stimulation also reduced SCD in the S1 (Q<0.05) but not in the aINS or MCC. Although no significant group-level analgesic effect was observed between active and sham conditions (P=0.107), correlation analyses revealed that greater SCD reductions in the S1 and MCC were significantly associated with higher post-rTMS pain tolerance (R=-0.487 and -0.495, both Q<0.01) and larger improvements in pain tolerance(R=-0.487 and -0.451, both Q<0.05). No such correlations were found following sham stimulation, suggesting that the behavioural relevance of neural changes is specific to active rTMS. ConclusionThis study provides novel evidence that migraineurs exhibit both local neuroplastic abnormalities (reduced N120 amplitude) and hyperactivity in key pain-processing regions (S1, pINS, aINS, MCC). A single session of DLPFC-rTMS reduced hyperactivity in the aINS, MCC, and S1. Notably, greater reductions in the S1 and MCC were associated with improved pain tolerance. These findings identify distinct cortical circuitries, particularly within the cognitive-affective pain network, that may serve as potential biomarkers for optimizing rTMS treatment in migraine and other chronic pain conditions. Future studies should validate these results in patient populations experiencing spontaneous migraine attacks and explore multi-session or accelerated rTMS protocols.

Objective: To establish a prediction model for high-risk cardiovascular disease (CVD) among residents aged 35 to 75 years, so as to provide the basis for improving CVD prevention and control measures. Methods: Permanent residents aged 35 to 75 years were selected from Dongcheng District, Beijing Municipality using the stratified random sampling method from 2018 to 2023. Demographic information, lifestyle, waist circumference and blood biochemical indicators were collected through questionnaire surveys, physical examinations and laboratory tests. Influencing factors for high-risk CVD among residents aged 35 to 75 years were identified using a multivariable logistic regression model, and a prediction model for high-risk CVD was established. The predictive effect was evaluated using the receiver operating characteristic (ROC) curve. Results: A total of 6 968 individuals were surveyed, including 2 821 males (40.49%) and 4 147 females (59.51%), and had a mean age of (59.92±9.33) years. There were 1 155 high-risk CVD population, with a detection rate of 16.58%. Multivariable logistic regression analysis showed that gender, age, smoking, central obesity, systolic blood pressure, fasting blood glucose, triglyceride and low-density lipoprotein cholesterol were influencing factors for high-risk CVD among residents aged 35 to 75 years (all P<0.05). The area under the ROC curve of the established prediction model was 0.849 (95%CI: 0.834-0.863), with a sensitivity of 0.693 and a specificity of 0.863, indicating good discrimination. Conclusion The model constructed by eight factors including demographic characteristics, lifestyle and blood biochemical indicators has good predictive value for high-risk CVD among residents aged 35 to 75 years.

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 relationship between hyperuricemia (HUA) and erectile dysfunction (ED) remains inadequately understood. Given that HUA is often associated with various metabolic disorders, this study aims to explore the multivariate linear impacts of metabolic parameters on erectile function in ED patients with HUA. A cross-sectional analysis was conducted involving 514 ED patients with HUA in the Department of Andrology, Jiangsu Province Hospital of Chinese Medicine (Nanjing, China), aged 18 to 60 years. General demographic information, medical history, and laboratory results were collected to assess metabolic disturbances. Sexual function was evaluated using the 5-item version of the International Index of Erectile Function (IIEF-5) questionnaire. Based on univariate analysis, variables associated with IIEF-5 scores were identified, and the correlations between them were evaluated. The effects of these variables on IIEF-5 scores were further explored by multiple linear regression models. Fasting plasma glucose ( β = -0.628, P < 0.001), uric acid ( β = -0.552, P < 0.001), triglycerides ( β = -0.088, P = 0.047), low-density lipoprotein cholesterol ( β = -0.164, P = 0.027), glycated hemoglobin (HbA1c; β = -0.562, P = 0.012), and smoking history ( β = -0.074, P = 0.037) exhibited significant negative impacts on erectile function. The coefficient of determination ( R ²) for the model was 0.239, and the adjusted R ² was 0.230, indicating overall statistical significance ( F -statistic = 26.52, P < 0.001). Metabolic parameters play a crucial role in the development of ED. Maintaining normal metabolic indices may aid in the prevention and improvement of erectile function in ED patients with HUA.
Humans ; Male ; Erectile Dysfunction/metabolism* ; Hyperuricemia/metabolism* ; Adult ; Middle Aged ; Cross-Sectional Studies ; Glycated Hemoglobin/metabolism* ; Blood Glucose/metabolism* ; Uric Acid/blood* ; Young Adult ; Triglycerides/blood* ; Adolescent ; Cholesterol, LDL/blood* ; Penile Erection/physiology* ; Surveys and Questionnaires

OBJECTIVE: To observe the efficacy and safety of 21-day venetoclax (VEN) plus azacitidine (AZA) (21-day VA) in newly diagnosed unfit acute myeloid leukemia (AML) patients in the real-world. METHODS: The clinical data of patients with unfit-AML who received 21-day VA regimen from December 2020 to July 2024 in our center and completed at least 1 cycle of therapeutic effect assessment was retrospectively collected to analyze the safety, efficacy and its influencing factors. RESULTS: A total of 59 patients were enrolled in our study, with a median age of 67(48-87) years old. After 1 cycle of therapy, the composite complete remission (cCR) rate was 74.5%, 54.2% of cases were negative for minimal residual disease (MRD). Among them, the MRD negative rate of patients with NPM1 mutation was significantly higher than that of patients without NPM1 mutation ( P =0.032). The median follow-up of patients was 19(2-38) months, the best cCR and MRD negative rates were 78% and 64.4%, respectively, the median overall survival (OS) time was 12 months, and the median progression free survival (PFS) time was 5 months. Multivariate Cox regression analysis showed less than 4 cycles of VA chemotherapy were independent risk factor for PFS and OS ( P < 0.05). After achieving remission, anemia and thrombocytopenia improved with the increase of the number of chemotherapy cycle. CONCLUSION In real-world, 21-day VA regimen still shows significant efficacy in the treatment of newly diagnosed unfit-AML, without adversely affecting remission rate and MRD negative rate of the first cycle.
Humans ; Leukemia, Myeloid, Acute/drug therapy* ; Aged ; Middle Aged ; Bridged Bicyclo Compounds, Heterocyclic/therapeutic use* ; Sulfonamides/therapeutic use* ; Azacitidine/therapeutic use* ; Aged, 80 and over ; Male ; Female ; Retrospective Studies ; Nucleophosmin ; Antineoplastic Combined Chemotherapy Protocols/therapeutic use* ; Remission Induction ; Mutation ; Treatment Outcome

BACKGROUND: The association between uric acid-albumin ratio (UAR) with different diseases has been evaluated before. However, the association between UAR with spontaneous reperfusion (SR) in patients with ST-segment elevation myocardial infarction (STEMI) has not been explored. METHODS: STEMI patients admitted to our department and underwent primary coronary angiography between 1^st November 2018 and 31^st December 2020 were retrospectively enrolled. The patients were divided into the SR group and the non-SR group according to the index coronary angiography results. The association between UAR and SR was evaluated by uni-variable and multi-variable logistic analysis. Receiver operating characteristic curve analysis was used to determine the optimum cut-off level of UAR in predicting SR. RESULTS: Three hundred and fifty-seven patients were finally enrolled in our study, 55 patients were divided into the SR group and 302 patients were divided into the non-SR group. In uni-variable analysis, patients with SR were older (P = 0.032), with higher red blood cell distribution width (P < 0.001) and red blood cell distribution width-to-platelet ratio (P < 0.001), higher level of C-reactive protein (P = 0.046), higher level of uric acid (P < 0.001) compared with patients without SR. Patients with SR had a lower level of platelets (P = 0.008), lower level of on-admission B-type natriuretic peptide (P < 0.001). As for the level of UAR, STEMI patients with SR had significantly higher levels of UAR compared with STEMI patients without SR [11.1 (8.9-13.4) vs. 8.3 (6.6-10.0), P < 0.001]. Further multi-variable logistic analysis reveals that UAR was the independent risk factor of SR in different models after adjusting different variables. Receiver operating characteristic analysis showed that UAR had good predictive value in SR (AUC = 0.75, 95% CI: 0.702-0.794, P < 0.01). CONCLUSIONS Our study shows that UAR is an independent risk factor for predicting SR in STEMI patients.

OBJECTIVE: To evaluate the efficacy and safety of Shexiang Tongxin Dropping Pill (STDP) in treating stable angina patients with phlegm-heat and blood-stasis syndrome by exercise duration and metabolic equivalents. METHODS: This multicenter, randomized, double-blind, placebo-controlled clinical trial enrolled stable angina patients with phlegm-heat and blood-stasis syndrome from 22 hospitals. They were randomized 1:1 to STDP (35 mg/pill, 6 pills per day) or placebo for 56 days. The primary outcome was the exercise duration and metabolic equivalents (METs) assessed by the standard Bruce exercise treadmill test after 56 days of treatment. The secondary outcomes included the total angina symptom score, Chinese medicine (CM) symptom scores, Seattle Angina Questionnaire (SAQ) scores, changes in ST-T on electrocardiogram and adverse events (AEs). RESULTS: This trial enrolled 309 patients, including 155 and 154 in the STDP and placebo groups, respectively. STDP significantly prolonged exercise duration with an increase of 51.0 s, compared to a decrease of 12.0 s with placebo (change rate: -11.1% vs. 3.2%, P<0.01). The increase in METs was significantly greater in the STDP group than in the placebo group (change: -0.4 vs. 0.0, change rate: -5.0% vs. 0.0%, P<0.01). The improvement of total angina symptom scores (25.0% vs. 0.0%), CM symptom scores (38.7% vs. 11.8%), reduction of nitroglycerin consumption (100.0% vs. 11.3%), and all domains of SAQ, were significantly greater with STDP than placebo (all P<0.01). The changes in Q-T intervals at 28 and 56 days from baseline were similar between the two groups (both P>0.05). Twenty-five participants (16.3%) with STDP and 16 (10.5%) with placebo experienced AEs (P=0.131), with no serious AEs observed. CONCLUSION STDP could improve exercise tolerance in patients with stable angina and phlegm-heat and blood stasis syndrome, with a favorable safety profile. (Registration No. ChiCTR-IPR-15006020).
Humans ; Double-Blind Method ; Drugs, Chinese Herbal/adverse effects* ; Male ; Female ; Middle Aged ; Angina, Stable/physiopathology* ; Aged ; Syndrome ; Treatment Outcome ; Placebos ; Tablets