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Background Heart failure (HF) with mid-range ejection fraction (EF) (HFmrEF) has attracted increasing attention in recent years. However, the understanding of HFmrEF remains limited, especially among Asian patients. Therefore, analysis of a Chinese HF registry was undertaken to explore the clinical characteristics and prognosis of HFmrEF. Methods A total of 755 HF patients from a multi-centre registry were classified into three groups based on EF measured by echocardiogram at recruitment: HF with reduced EF (HFrEF) ( n  = 211), HFmrEF ( n  = 201), and HF with preserved EF (HFpEF) ( n  = 343). Clinical data were carefully collected and analyzed at baseline. The primary endpoint was all-cause mortality and cardiovascular mortality while the secondary endpoints included hospitalization due to HF and major adverse cardiac events (MACE) during 1-year follow-up. Cox regression and Logistic regression were performed to identify the association between the three EF strata and 1-year outcomes. Results The prevalence of HFmrEF was 26.6% in the observed HF patients. Most of the clinical characteristics of HFmrEF were intermediate between HFrEF and HFpEF. But a significantly higher ratio of prior myocardial infarction ( p  = 0.002), ischemic heart disease etiology ( p  = 0.004), antiplatelet drug use ( p  = 0.009), angioplasty or stent implantation ( p  = 0.003) were observed in patients with HFmrEF patients than those with HFpEF and HFrEF. Multivariate analysis showed that the HFmrEF group presented a better prognosis than HFrEF in all-cause mortality [ p  = 0.022, HR (95%CI): 0.473(0.215–0.887)], cardiovascular mortality [ p  = 0.005, HR (95%CI): 0.270(0.108–0.672)] and MACE [ p  = 0.034, OR (95%CI): 0.450(0.215–0.941)] at 1 year. However, no significant differences in 1-year outcomes were observed between HFmrEF and HFpEF. Conclusion HFmrEF is a distinctive subgroup of HF. The strikingly prevalence of ischemic history among patients with HFmrEF might indicate a key to profound understanding of HFmrEF. Patients in HFmrEF group presented better 1-year outcomes than HFrEF group. The long-term prognosis and optimal medications for HFmrEF require further investigations.

Background It remains unknown whether anticoagulation for persistent left ventricular (LV) thrombus should be continued indefinitely. Identifying patients with a high risk of thrombus unresolved may be helpful to determine the optimum anticoagulation duration. This study aimed to develop a prediction model to forecast thrombus persistence or recurrence in patients with LV thrombus. Methods We enrolled patients prospectively from 2020 to 2022 and retrospectively from 2013 to 2019 at the National Center of Cardiovascular Diseases of China. The two cohorts were then combined to derive predictive models of thrombus persistence/recurrence. The primary study comprised patients who received systemic oral anticoagulants and had imaging records available at the end of a 3-month follow-up period. The Lasso regression algorithm and the logistic regression were performed to select independent predictors. The calibration curve was generated and a nomogram risk prediction model was applied as a risk stratification tool. Results A total of 172 (64 in the prospective cohort and 108 in the retrospective cohort) patients were included, with 124 patients in a training set and 48 patients in a validation set. Six predictors were incorporated into the multivariate logistic regression prediction model. The area under the receiving operating characteristic was 0.852 in the training set and 0.631 in the validation set. Patients with protuberant thrombus and higher baseline D-dimer levels had a reduced risk of persistence/recurrence (OR 0.17, 95% CI 0.03–0.69, P  = 0.025; OR 0.67, 95% CI 0.43–0.91, P  = 0.030, separately), whereas thicker thrombus was linked to an increased rate of persistent thrombus (OR 1.11, 95% CI 1.05–1.20, P  = 0.002). Additionally, patients with diverse diagnoses or receiving different antiplatelet treatments had different rates of LV thrombus persistence/recurrence at 3 months. Conclusions This prediction model provides tools to forecast the occurrence of persistent/recurrent thrombus and allows the identification of characteristics associated with unresolved thrombus. To validate the model and determine the duration of anticoagulation in patients with persistent thrombus, prospective randomized trials are necessary.

Current risk scores of ST-segment elevation myocardial infarction (STEMI) need sophisticated algorithm and were limited for bedside use. Our study aimed to evaluate the usefulness of admission shock index (SI) for predicting the short-term outcomes in patients with STEMI. Included were 7,187 consecutive patients with STEMI. The admission SI was defined as the ratio of admission heart rate and systolic blood pressure. Patients were divided into 2 groups with SI <0.7 and ≥0.7, respectively, based on the receiver operating characteristic curve analysis. The major end points were 7- and 30-day all-cause mortality. Of 7,187 patients, 5,026 had admission SI <0.7 and 2,161 had admission SI ≥0.7. Those who presented with SI ≥0.7 had greater 7- and 30-day all-cause mortality and major adverse cardiovascular events than patients with SI <0.7. After multivariate adjustment, patients with SI ≥0.7 had a 2.2-fold increased risk of 7-day all-cause mortality (hazard ratio 2.21, 95% confidence interval [CI] 1.71 to 2.86) and 1.9-fold increased risk of 30-day all-cause mortality (hazard ratio 1.94, 95% CI 1.54 to 2.44). Moreover, admission SI ≥0.7 was also associated with 1.6- and 1.5-fold increased risk of 7- and 30-day major adverse cardiovascular events (hazard ratio 1.63, 95% CI 1.36 to 1.95 and hazard ratio 1.47, 95% CI 1.24 to 1.74, respectively). The C statistic of admission SI for predicting 7- and 30-day all-cause mortality was 0.701 and 0.686, respectively, compared with 0.744 and 0.738 from the Thrombolysis In Myocardial Infarction risk score. In conclusion, admission SI, an easily calculated index at first contact, may be a useful predictor for short-term outcomes especially for acute phase outcomes in patients with STEMI. •Patients with shock index (SI) ≥0.7 had greater short-term mortality after ST elevation myocardial infarction.•SI ≥0.7 was an independent risk factor of poor short-term outcomes after ST elevation myocardial infarction.•SI had modest predictive value for 7-day all-cause mortality.

Background Inflammatory mechanisms are important in stroke risk, and genetic variations in components of the inflammatory response have been implicated as risk factors for stroke. We tested the inflammatory gene polymorphisms and their association with ischemic stroke in a Chinese Han population. Methods A total of 1,124 ischemic stroke cases and 1,163 controls were genotyped with inflammatory panel strips containing 51 selected inflammatory gene polymorphisms from 35 candidate genes. We tested the genotype-stroke association with logistic regression model. Results We found two single nucleotide polymorphisms (SNPs) in CCL11 were associated with ischemic stroke. After adjusting for multiple testing using false discovery rate (FDR) with a 0.20 cut-off point, CCL11 rs4795895 remained statistically significant. We further stratified the study population by their hypertension status. In the hypertensive group, CCR2 rs1799864, CCR5 rs1799987 and CCL11 rs4795895 were nominally associated with increased risk of stroke. In the non-hypertensive group, CCL11 rs3744508, LTC4S rs730012, FCER1B rs569108, TGFB1 rs1800469, LTA rs909253 and CCL11 rs4795895 were associated with ischemic stroke. After correction for multiple testing, CCR2 rs1799864 and CCR5 rs1799987 remained significant in the hypertensive group, and CCL11 rs3744508, LTC4S rs730012, FCER1B rs569108, TGFB1 rs1800469, LTA rs909253 remained significant in the non-hypertensive group. Conclusions Our results indicate that inflammatory genetic variants are associated with increased risk of ischemic stroke in a Chinese Han population, particularly in non-hypertensive individuals.

Evidence on the treatment for left ventricular (LV) thrombus is limited and mainly derives from retrospective studies. The aim of R-DISSOLVE was to explore the effectiveness and safety of rivaroxaban in patients with LV thrombus. R-DISSOLVE was a prospective, interventional, single-arm study, conducted from Oct 2020 to June 2022 at Fuwai Hospital, China. Patients with a history of LV thrombus < 3 months and with systemic anticoagulation therapy < 1 month were included. The thrombus was quantitatively confirmed by contrast-enhanced echocardiography (CE) at baseline and follow-up visits. Eligible patients were assigned to rivaroxaban (20 mg once daily or 15 mg if creatinine clearance was between 30 and 49 mL/min) and its concentration was determined by detecting anti-Xa activity. The primary efficacy outcome was the rate of LV thrombus resolution at 12 weeks. The main safety outcome was the composite of ISTH major and clinically relevant non-major bleeding. A total of 64 patients with complete CE results were analyzed for efficacy outcomes. The mean LV ejection fraction was 25.4 ± 9.0%. The dose-response curve of rivaroxaban was satisfactory based on the peak and trough plasma levels and all concentrations were in the recommended treatment range according to NOAC guidelines. The incidence rate of thrombus resolution at 6 weeks was 66.1% (41/62, 95% CI 53.0–77.7%), and of thrombus resolution or reduction was 95.2% (59/62, 95% CI 86.5–99.0%). At 12 weeks, the thrombus resolution rate was 78.1% (50/64, 95% CI 66.0–87.5%) while the rate of thrombus resolution or reduction was 95.3% (61/64, 95% CI 86.9–99.0%). The main safety outcome occurred in 4 of 75 patients (5.3%) (2 ISTH major bleeding and 2 clinically relevant non-major bleeding). In patients with LV thrombus, we reported a high thrombus resolution rate with acceptable safety by rivaroxaban, which could be a potential option for further LV thrombus treatment.Trial registration This study was registered at ClinicalTrials.gov as NCT 04970381.

Background: Despite multimodality therapies, the prognosis of patients with malignant brain tumors remains extremely poor. One of the major obstacles that hinders development of effective therapies is the limited availability of clinically relevant and biologically accurate (CRBA) mouse models. Methods: We have developed a freehand surgical technique that allows for rapid and safe injection of fresh human brain tumor specimens directly into the matching locations (cerebrum, cerebellum, or brainstem) in the brains of SCID mice. Results: Using this technique, we successfully developed 188 PDOX models from 408 brain tumor patient samples (both high-and low-grade) with a success rate of 72.3% in high-grade glioma, 64.2% in medulloblastoma, 50% in ATRT, 33.8% in ependymoma, and 11.6% in low-grade gliomas. Detailed characterization confirmed their replication of the histopathological and genetic abnormalities of the original patient tumors. Conclusions: The protocol is easy to follow, without a sterotactic frame, in order to generate large cohorts of tumor-bearing mice to meet the needs of biological studies and preclinical drug testing.

Background Recent advancements in methodologies and technologies have enabled the simultaneous measurement of multiple omics data, which provides a comprehensive understanding of cellular heterogeneity. However, existing methods have limitations in accurately identifying cell types while maintaining model interpretability, especially in the presence of noise. Methods We propose a novel method called scMFG, which leverages feature grouping and group integration techniques for the integration of single-cell multi-omics data. By organizing features with similar characteristics within each omics layer through feature grouping. Furthermore, scMFG ensures a consistent feature grouping approach across different omics layers, promoting comparability of diverse data types. Additionally, scMFG incorporates a matrix factorization-based approach to enable the integrated results remain interpretable. Results We comprehensively evaluated scMFG’s performance on four complex real-world datasets generated using diverse sequencing technologies, highlighting its robustness in accurately identifying cell types. Notably, scMFG exhibited superior performance in deciphering cellular heterogeneity at a finer resolution compared to existing methods when applied to simulated datasets. Furthermore, our method proved highly effective in identifying rare cell types, showcasing its robust performance and suitability for detecting low-abundance cellular populations. The interpretability of scMFG was successfully validated through its specific association of outputs with specific cell types or states observed in the neonatal mouse cerebral cortices dataset. Moreover, we demonstrated that scMFG is capable of identifying cell developmental trajectories even in datasets with batch effects. Conclusions Our work presents a robust framework for the analysis of single-cell multi-omics data, advancing our understanding of cellular heterogeneity in a comprehensive and interpretable manner.

肝细胞癌(HCC)是最常见的恶性癌症之一，发病率和病死率一直居高不下，预后很差。叉头框(FOX)转录因子家族可调控细胞的生长、分化及组织发育，在肿瘤中具有重要的生物学作用。综述了FOX家族分子表达与HCC发生发展及预后的关系，分析了FOX在HCC进展中发挥作用的机制，提出FOX家族分子有望成为HCC治疗的新靶点。

Background Cellular communication is vital for the proper functioning of multicellular organisms. A comprehensive analysis of cellular communication demands the consideration not only of the binding between ligands and receptors but also of a series of downstream signal transduction reactions within cells. Thanks to the advancements in spatial transcriptomics technology, we are now able to better decipher the process of cellular communication within the cellular microenvironment. Nevertheless, the majority of existing spatial cell–cell communication algorithms fail to take into account the downstream signals within cells. Results In this study, we put forward SpaCcLink, a cell–cell communication analysis method that takes into account the downstream influence of individual receptors within cells and systematically investigates the spatial patterns of communication as well as downstream signal networks. Analyses conducted on real datasets derived from humans and mice have demonstrated that SpaCcLink can help in identifying more relevant ligands and receptors, thereby enabling us to systematically decode the downstream genes and signaling pathways that are influenced by cell–cell communication. Comparisons with other methods suggest that SpaCcLink can identify downstream genes that are more closely associated with biological processes and can also discover reliable ligand-receptor relationships. Conclusions By means of SpaCcLink, a more profound and all-encompassing comprehension of the mechanisms underlying cellular communication can be achieved, which in turn promotes and deepens our understanding of the intricate complexity within organisms.

Mitochondria are the powerhouse of eukaryotic cells, which regulate cell metabolism and differentiation. Recently, mitochondrial transfer between cells has been shown to direct recipient cell fate. However, it is unclear whether mitochondria can translocate to stem cells and whether this transfer alters stem cell fate. Here, mesenchymal stem cell (MSC) regulation is examined by macrophages in the bone marrow environment. It is found that macrophages promote osteogenic differentiation of MSCs by delivering mitochondria to MSCs. However, under osteoporotic conditions, macrophages with altered phenotypes, and metabolic statuses release oxidatively damaged mitochondria. Increased mitochondrial transfer of M1‐like macrophages to MSCs triggers a reactive oxygen species burst, which leads to metabolic remodeling. It is showed that abnormal metabolism in MSCs is caused by the abnormal succinate accumulation, which is a key factor in abnormal osteogenic differentiation. These results reveal that mitochondrial transfer from macrophages to MSCs allows metabolic crosstalk to regulate bone homeostasis. This mechanism identifies a potential target for the treatment of osteoporosis. The findings reveal a new pattern of cellular crosstalk in the bone marrow environment, with macrophages regulating bone metabolism crosstalk by transferring their mitochondria into mesenchymal stem cells. This study suggests that mitochondrial transfer may be a novel way to regulate homeostasis by enabling immune cells to regulate their local tissue environment.