BACKGROUND: Based on the China-VHD database, this study sought to develop and validate a Valvular Heart Disease- specific Age-adjusted Comorbidity Index (VHD-ACI) for predicting mortality risk in patients with VHD. METHODS & RESULTS: The China-VHD study was a nationwide, multi-centre multi-centre cohort study enrolling 13,917 patients with moderate or severe VHD across 46 medical centres in China between April-June 2018. After excluding cases with missing key variables, 11,459 patients were retained for final analysis. The primary endpoint was 2-year all-cause mortality, with 941 deaths (10.0%) observed during follow-up. The VHD-ACI was derived after identifying 13 independent mortality predictors: cardiomyopathy, myocardial infarction, chronic obstructive pulmonary disease, pulmonary artery hypertension, low body weight, anaemia, hypoalbuminaemia, renal insufficiency, moderate/severe hepatic dysfunction, heart failure, cancer, NYHA functional class and age. The index exhibited good discrimination (AUC, 0.79) and calibration (Brier score, 0.062) in the total cohort, outperforming both EuroSCORE II and ACCI (P < 0.001 for comparison). Internal validation through 100 bootstrap iterations yielded a C statistic of 0.694 (95% CI: 0.665-0.723) for 2-year mortality prediction. VHD-ACI scores, as a continuous variable (VHD-ACI score: adjusted HR (95% CI): 1.263 (1.245-1.282), P < 0.001) or categorized using thresholds determined by the Yoden index (VHD-ACI ≥ 9 vs. < 9, adjusted HR (95% CI): 6.216 (5.378-7.184), P < 0.001), were independently associated with mortality. The prognostic performance remained consistent across all VHD subtypes (aortic stenosis, aortic regurgitation, mitral stenosis, mitral regurgitation, tricuspid valve disease, mixed aortic/mitral valve disease and multiple VHD), and clinical subgroups stratified by therapeutic strategy, LVEF status (preserved vs. reduced), disease severity and etiology. CONCLUSION The VHD-ACI is a simple 13-comorbidity algorithm for the prediction of mortality in VHD patients and providing a simple and rapid tool for risk stratification.

OBJECTIVE: To develop and validate a user-friendly risk score for older mitral regurgitation (MR) patients, referred to as the Elder-MR score. METHODS: The China Senile Valvular Heart Disease (China-DVD) Cohort Study functioned as the development cohort, while the China Valvular Heart Disease (China-VHD) Study was employed for external validation. We included patients aged 60 years and above receiving medical treatment for moderate or severe MR (2274 patients in the development cohort and 1929 patients in the validation cohort). Candidate predictors were chosen using Cox's proportional hazards model and stepwise selection with Akaike's information criterion. RESULTS: Eight predictors were identified: age ≥ 75 years, body mass index < 20 kg/m², NYHA class III/IV, secondary MR, anemia, estimated glomerular filtration rate < 60 mL/min per 1.73 m², albumin < 35 g/L, and left ventricular ejection fraction < 60%. The model displayed satisfactory performance in predicting one-year mortality in both the development cohort (C-statistic = 0.73, 95% CI: 0.69-0.77, Brier score = 0.06) and the validation cohort (C-statistic = 0.73, 95% CI: 0.68-0.78, Brier score = 0.06). The Elder-MR score ranges from 0 to 15 points. At a one-year follow-up, each point increase in the Elder-MR score represents a 1.27-fold risk of death (HR = 1.27, 95% CI: 1.21-1.34, P < 0.001) in the development cohort and a 1.24-fold risk of death (HR = 1.24, 95% CI: 1.17-1.30, P < 0.001) in the validation cohort. Compared to EuroSCORE II, the Elder-MR score demonstrated superior predictive accuracy for one-year mortality in the validation cohort (C-statistic = 0.71 vs. 0.70, net reclassification improvement = 0.320, P < 0.01; integrated discrimination improvement = 0.029, P < 0.01). CONCLUSIONS The Elder-MR score may serve as an effective risk stratification tool to assist clinical decision-making in older MR patients.

Objective: To analyze and compare therapy responses, outcomes, and incidence of severe hematologic adverse events of flumatinib and imatinib in patients newly diagnosed with chronic phase chronic myeloid leukemia (CML) . Methods: Data of patients with chronic phase CML diagnosed between January 2006 and November 2022 from 76 centers, aged ≥18 years, and received initial flumatinib or imatinib therapy within 6 months after diagnosis in China were retrospectively interrogated. Propensity score matching (PSM) analysis was performed to reduce the bias of the initial TKI selection, and the therapy responses and outcomes of patients receiving initial flumatinib or imatinib therapy were compared. Results: A total of 4 833 adult patients with CML receiving initial imatinib (n=4 380) or flumatinib (n=453) therapy were included in the study. In the imatinib cohort, the median follow-up time was 54 [interquartile range (IQR), 31-85] months, and the 7-year cumulative incidences of CCyR, MMR, MR(4), and MR(4.5) were 95.2%, 88.4%, 78.3%, and 63.0%, respectively. The 7-year FFS, PFS, and OS rates were 71.8%, 93.0%, and 96.9%, respectively. With the median follow-up of 18 (IQR, 13-25) months in the flumatinib cohort, the 2-year cumulative incidences of CCyR, MMR, MR(4), and MR(4.5) were 95.4%, 86.5%, 58.4%, and 46.6%, respectively. The 2-year FFS, PFS, and OS rates were 80.1%, 95.0%, and 99.5%, respectively. The PSM analysis indicated that patients receiving initial flumatinib therapy had significantly higher cumulative incidences of CCyR, MMR, MR(4), and MR(4.5) and higher probabilities of FFS than those receiving the initial imatinib therapy (all P<0.001), whereas the PFS (P=0.230) and OS (P=0.268) were comparable between the two cohorts. The incidence of severe hematologic adverse events (grade≥Ⅲ) was comparable in the two cohorts. Conclusion: Patients receiving initial flumatinib therapy had higher cumulative incidences of therapy responses and higher probability of FFS than those receiving initial imatinib therapy, whereas the incidence of severe hematologic adverse events was comparable between the two cohorts.
Adult ; Humans ; Adolescent ; Imatinib Mesylate/adverse effects* ; Incidence ; Antineoplastic Agents/adverse effects* ; Retrospective Studies ; Pyrimidines/adverse effects* ; Leukemia, Myelogenous, Chronic, BCR-ABL Positive/drug therapy* ; Treatment Outcome ; Benzamides/adverse effects* ; Leukemia, Myeloid, Chronic-Phase/drug therapy* ; Aminopyridines/therapeutic use* ; Protein Kinase Inhibitors/therapeutic use*

Humans ; Child ; Primary Myelofibrosis/genetics* ; Prognosis ; Mutation

Schizophrenia is a serious mental disease. The diagnosis of schizophrenia so far relies heavily on subjective evidence, including self-reported experiences by patients, manifestations described by relatives, and abnormal behaviors assessed by psychiatrists. The diagnosis, monitoring of the disease progression and therapy efficacy assessment are challenging due to the lack of established laboratory biomarkers. Based on the current literature, clinical consensus, guidelines, and expert recommendations, this review highlighted evidence-based potential laboratory biomarkers for the diagnosis of schizophrenia, including genetic biomarkers, neurotransmitters, neurodevelopmental-related proteins, and intestinal flora, and discussed the potential future directions for the application of these biomarkers in this field, aiming to provide an objective basis for the use of these biomarkers in the early and accurate diagnosis, treatment, and prognosis and rehabilitation assessment of schizophrenia.

Humans ; Infectious Mononucleosis/complications* ; Lymphocytosis ; Lymphohistiocytosis, Hemophagocytic/etiology*

Aim To investigate the effect of Exendin4 on proliferation, migration, adhesion and senescence of endothelial progenitor cells (EPCs) in type I diabetic mice and its possible mechanism. Methods EPCs from 6-month diabetic mice were isolated and cultured by density gradient centrifugation. Cells were treated with different concentrations of Exendin-4 (1, 5, 10, 25 μmol · L

OBJECTIVE: To observe the effect of electroacupuncture (EA) at sensitized acupoints on choline acetyltransferase positive (ChAT METHODS: A total of 79 male SD rats were randomized into five groups, i.e. a normal group (20 rats), a normal plus sensitized acupoint group (5 rats), a model group (34 rats), an EA RESULTS: The EB extravasating areas were distributed in the segments from T CONCLUSION The segmental dominance (acupoints) from T
Acupuncture Points ; Animals ; Cholinergic Neurons ; Colon ; Electroacupuncture ; Male ; Rats ; Rats, Sprague-Dawley

Background: Artificial intelligence-assisted image recognition technology is currently able to detect the target area of an image and fetch information to make classifications according to target features.This study aimed to use deep neural networks for computed tomography(CT)diagnosis of perigastric metastatic lymph nodes(PGMLNs)to simulate the recognition of lymph nodes by radiologists,and to acquire more accurate identification results.Methods: A total of 1371 images of suspected lymph node metastasis from enhanced abdominal CT scans were identified and labeled by radiologists and were used with 18,780 original images for faster region-based convolutional neural networks(FR-CNN)deep learning.The identification results of 6000 random CT images from 100 gastric cancer patients by the FR-CNN were compared with results obtained from radiologists in terms of their identification accuracy.Similarly,1004 CT images with metastatic lymph nodes that had been post-operatively confirmed by pathological examination and 11,340 original images were used in the identification and learning processes described above.The same 6000 gastric cancer CT images were used for the verification,according to which the diagnosis results were analyzed.Results: In the initial group,precision-recall curves were generated based on the precision rates,the recall rates of nodule classes of the training set and the validation set; the mean average precision(mAP)value was 0.5019.To verify,the results of the initial learning group,the receiver operating characteristic curves was generated,and the corresponding area under the curve(AUC)value was calculated as 0.8995.After the second phase of precise learning,all the indicators were improved,and the mAP and AUC values were 0.7801 and 0.9541,respectively.Conclusion: Through deep learning,FR-CNN achieved high judgment effectiveness and recognition accuracy for CT diagnosis of PGMLNs.

Using CBBR as the parent core constructed in our lab, we designed and synthesized 15 novel compounds with diverse structures for evaluation of anti-bacterial activities. Structure activity relationship studies revealed that ① ring C was essential for the activity; ② 7,8- or 8,13-disubstituted CBBR derivatives showed ideal activities, weaker or similar to those corresponding to 7-, 8-, or 13-monosubstituted CBBR derivatives. Among those, compound 9a showed the most potential activity against MRSA/VISA isolates with MIC values of 1-2 μg·mL^-1, much better than Lev. 9a also displayed higher stability in the plasma and liver microsomes. Molecular docking indicated that 9a might target bacterial DNA Topo IV ParE subunit, indicating a mode of action distinct from current antibacterial drugs on market. The results provided key scientific evidence for developing such compounds into a new family of anti-MRSA drugs.