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Tissue engineering is a rapidly developing field with many potential clinical applications in tissue and organ regeneration. The development of a mature and stable vasculature within these engineered tissues (ET) remains a significant obstacle. Currently, several growth factors (GFs) have been identified to play key roles within in vivo angiogenesis, including vascular endothelial growth factor (VEGF), platelet derived growth factor (PDGF), FGF and angiopoietins. In this article we attempt to build on in vivo principles to review the single, dual and multiple GF release systems and their effects on promoting angiogenesis. We conclude that multiple GF release systems offer superior results compared to single and dual systems with more stable, mature and larger vessels produced. However, with more complex release systems this raises other problems such as increased cost and significant GF–GF interactions. Upstream regulators and pericyte-coated scaffolds could provide viable alternative to circumnavigate these issues.

Background The triglyceride–glucose (TyG) index has been proposed as a potential predictor of adverse prognosis of cardiovascular diseases (CVDs). However, its prognostic value in patients with coronary heart disease (CHD) and hypertension remains unclear. Methods A total of 1467 hospitalized patients with CHD and hypertension from January 2021 to December 2021 were included in this prospective and observational clinical study. The TyG index was calculated as Ln [fasting triglyceride level (mg/dL) × fasting plasma glucose level (mg/dL)/2]. Patients were divided into tertiles according to TyG index values. The primary endpoint was a compound endpoint, defined as the first occurrence of all-cause mortality or total nonfatal CVDs events within one-year follow up. The secondary endpoint was atherosclerotic CVD (ASCVD) events, including non-fatal stroke/transient ischemic attack (TIA) and recurrent CHD events. We used restricted cubic spline analysis and multivariate adjusted Cox proportional hazard models to investigate the associations of the TyG index with primary endpoint events. Results During the one-year follow-up period, 154 (10.5%) primary endpoint events were recorded, including 129 (8.8%) ASCVD events. After adjusting for confounding variables, for per standard deviation (SD) increase in the TyG index, the risk of incident primary endpoint events increased by 28% [hazard ratio (HR) = 1.28, 95% confidence interval (CI) 1.04–1.59]. Compared with subjects in the lowest tertile (T1), the fully adjusted HR for primary endpoint events was 1.43 (95% CI 0.90–2.26) in the middle (T2) and 1.73 (95% CI 1.06–2.82) in highest tertile (T3) ( P for trend = 0.018). Similar results were observed in ASCVD events. Restricted cubic spline analysis also showed that the cumulative risk of primary endpoint events increased as TyG index increased. Conclusions The elevated TyG index was a potential marker of adverse prognosis in patients with CHD and hypertension.

Ventricular septal rupture (VSR) is a mechanical complication of acute myocardial infarction (AMI), and its mortality has not decreased significantly in recent decades. However, no clinical model has been developed to predict short-term mortality in patients with post-infarction VSR (PIVSR). This study aimed to develop a nomogram to predict the 30-day mortality by using the clinical characteristics of hospitalized patients with PIVSR. The least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression analysis was used to construct a nomogram by R. The model was evaluated by the area under the curve (AUC), calibration curve and decision curve analysis (DCA). The bootstrap method was used to validate the model internally. As a result, a nomogram was constructed by using six variables, including CRRT, mechanical ventilation, PPCI, WBC, PASP and methods of treatment. The AUC of the prediction model was 0.96 (0.93, 0.98). The prediction model was well calibrated. The DCA showed that if the threshold probability was between 15% and 95%, the nomogram model would provide a net benefit. The well-constructed and evaluated nomogram can be beneficial to clinicians to predict the risk of death within 30 days in patients with PIVSR.

Kaempferol exhibits various biological activities, including antioxidant and anti-inflammatory effects. Its role in modulating lipid metabolism and inhibiting inflammatory responses to suppress the progression of atherosclerosis has been confirmed. However, its impact on macrophage pyroptosis and the underlying mechanisms remain unclear. This study aims to investigate the effects of kaempferol (Kae) on lipopolysaccharide (LPS)-induced macrophage pyroptosis and its potential mechanisms. In the experiments, we used the CCK8 assay to evaluate cell viability, ROS detection kits to measure intracellular reactive oxygen species (ROS) levels, Western Blot to detect the expression of proteins such as NOD-like receptor family pyrin domain-containing 3 (NLRP3), nuclear factor erythroid 2-related factor 2 (NRF2), gasdermin D (GSDMD), and heme oxygenase-1 (HO-1), and immunofluorescence to observe NRF2 nuclear translocation. The results showed that kaempferol alleviated LPS-induced cell viability decline and lactate dehydrogenase (LDH) release, inhibited excessive ROS generation, and suppressed NLRP3 inflammasome activation by increasing glutathione (GSH) and HO-1 levels, thereby reducing the expression of inflammatory factors. Additionally, kaempferol promoted NRF2 nuclear translocation, and the application of the NRF2 inhibitor ML385 reversed its antioxidant and anti-inflammatory effects. In vivo experiments further confirmed that kaempferol inhibited oxidative stress and reduced macrophage pyroptosis by activating the NRF2 pathway.

This study investigates how dynamic fluctuations in matrix stiffness affect the behavior of cardiac fibroblasts (CFs) within a three-dimensional (3D) hydrogel environment. Using hybrid hydrogels with tunable stiffness, we created an in vitro model to mimic the varying stiffness of the cardiac microenvironment. By manipulating hydrogel stiffness, we examined CF responses, particularly the expression of α-smooth muscle actin (α-SMA), a marker of myofibroblast differentiation. Our findings reveal that increased matrix stiffness promotes the differentiation of CFs into myofibroblasts, while matrix softening reverses this process. Additionally, we identified the role of focal adhesions and integrin β1 in mediating stiffness-induced phenotypic switching. This study provides significant insights into the mechanobiology of cardiac fibrosis and suggests that modulating matrix stiffness could be a potential therapeutic strategy for treating cardiovascular diseases.

The purpose of this study was to investigate whether heart rate variability (HRV), a predictor of autonomic function, is associated with arterial stiffness in hypertensive patients. A total of 1,132 essential hypertension patients were included in this retrospective study. The standard deviation of normal-to-normal intervals (SDNN), an indicator of HRV, was selected to assess autonomic function. Arterial stiffness was evaluated by measuring the brachial-ankle pulse wave velocity (baPWV). Patients were categorized into tertiles based on their SDNN values. Participants in the lowest tertile of SDNN were older and exhibited higher levels of triglycerides and fasting blood glucose compared to those in the highest tertile of SDNN. Multivariate linear regression analyses indicated that SDNN had an independent negative correlation with baPWV (β=−3.60; 95% confidence interval [CI] =−4.78 ~ −2.41) after adjusting for all covariates. Consistently, multiple logistic regression analyses revealed a negative relationship between SDNN and the elevated baPWV (>75th percentile) (odds ratio [OR]=0.97; 95% CI=0.95 ~ 0.99). Evaluations utilizing restricted cubic splines confirmed that the relationships between SDNN and baPWV displayed an L-shaped curve (non-linear, P< 0.001). Subgroup analyses indicated that more pronounced associations between SDNN and baPWV were observed in younger individuals (under 65 years) ( P for interaction< 0.05). This study demonstrated that the SDNN is independently and negatively associated with baPWV in hypertensive patients, particularly in those under 65 years of age. These findings suggest a potential relationship between arterial stiffness and autonomic nervous system function.

Background The early and accurate diagnosis of acute myocardial infarction (AMI) remains a critical challenge in clinical practice. While inflammation is a cornerstone of AMI pathogenesis, translating specific inflammatory pathways into clinically actionable tools requires an integrative analytical approach. The discovery of reliable molecular biomarkers reflecting these pathways is crucial for advancing diagnostic precision. This study aims to bridge this gap by employing a multiomics and machine learning strategy to identify and validate a robust diagnostic signature derived from lipopolysaccharide (LPS)‐related immune responses. Methods We performed an integrated transcriptomic analysis using five Gene Expression Omnibus (GEO) datasets. Differential expression, functional enrichment, and weighted gene coexpression network analysis (WGCNA) were conducted to pinpoint AMI‐associated modules. A core diagnostic gene panel was refined through a consensus machine learning–driven feature selection pipeline incorporating LASSO, support vector machine–recursive feature elimination (SVM–RFE), and random forest algorithms. The immune microenvironment was profiled using CIBERSORT, and patient stratification was achieved via consensus clustering. The clinical utility of the final signature was evaluated by constructing and validating a machine learning–based diagnostic model and a corresponding nomogram. Results Our multiomics workflow identified an eight‐gene LPS‐related signature (S100A9, SERPINA1, S100A12, MCEMP1, MAFB, C5AR1, RNASE2, and VSIG4) with high diagnostic accuracy across independent cohorts. A machine learning–optimized neural network model (NNET) and LASSO regression both achieved an area under the curve (AUC) of 0.859. Immune deconvolution revealed significant shifts in neutrophil and macrophage subsets, correlating strongly with signature genes. Unsupervised learning delineated two distinct AMI subtypes, with Cluster A exhibiting a hyperinflammatory phenotype characterized by the upregulation of this signature. Conclusion This study establishes a novel, machine learning–curated multiomics signature that captures TLR4 axis–related innate immune activation in AMI. The derived diagnostic model and nomogram offer a direct path for clinical translation, potentially aiding in rapid patient stratification. Furthermore, the identification of immune‐endotypic subtypes paves the way for biomarker‐guided personalized therapeutic strategies, moving beyond a one‐size‐fits‐all approach to AMI management. Prospective validation in real‐world clinical settings is warranted.

Pyrogenic carbon (PyC), generated by fire, acts as a stable carbon deposit in natural ecosystems and is widely used to reconstruct fire history. Fuel type and burning temperature are the two major factors that influence PyC properties and exert variable effects on soil carbon pools, especially for peatlands. However, whether analysis of PyC can identify these two factors remains unclear. To address this knowledge gap, we selected typical peatland plant litters of seven shrub and seven herb plants in the Great Khingan Mountains, China. The properties of PyC produced at 250°C (low-intensity burning) and 600°C (high-intensity burning) without oxygen were evaluated. The results showed that the effects of burning temperature and plant type on δ13C-PyC were not significant. The differences in the initial compositions of herbs and shrubs led to more aromatic and carboxylic compounds in shrub PyC than in herb PyC. A high burning temperature led to less labile components (e.g. aliphatic compounds and acids) and higher thermal stability of high-temperature PyC compared to that of low-temperature PyC. Our results also indicate that several typical PyC chemical composition indicators (e.g. Fourier-transform infrared spectroscopy 1515/1050 ratio and 1720/1050 ratio) can potentially identify PyC sources.

Objective Impaired right ventricular (RV)-pulmonary arterial (PA) coupling, calculated by measuring the tricuspid annular plane systolic excursion (TAPSE) to pulmonary artery systolic pressure (PASP), can be used as an early indicator of right ventricular dysfunction (RVD) in patients with heart failure with a reduced ejection fraction (HFrEF). Patients suffering from HFrEF experience improvements in left ventricular (LV) function through the administration of sacubitril/valsartan therapy. In addition, the albumin-bilirubin (ALBI) score was associated with the fluid overload status and adverse clinical outcomes in patients with heart failure. This study aimed to assess whether angiotensin receptor-neprilysin inhibitor (ARNI) affects the TAPSE /PASP in patients with HFrEF, and whether there is a correlation between changes in the ALBI score and ARNI treatment. Methods A retrospective observational study was conducted on 305 patients with HFrEF and RVD who were hospitalized between June 2020 and December 2021. One year after treatment, laboratory test results, ALBI score, transthoracic echocardiography (TTE), New York Heart Association classification, Minnesota Living with Heart Failure Questionnaire scores and changes in relevant variables were reevaluated. Results Compared to before sacubitril/valsartan treatment, the ALBI was found to be significantly reduced after one year of follow-up (-2.42 ± 0.37 vs. -2.51 ± 0.32, p  < 0.001). Additionally, A significant improvement was demonstrated in the following echocardiography parameters assessing RV function after 1 year of treatment with sacubitril/valsartan: TAPSE (15 ± 1 vs. 18 ± 2 mm, p  < 0.001), PASP (45 ± 8 vs. 40 ± 9 mmHg, p  < 0.001), pulmonary artery diastolic pressure (PADP) (22 ± 4 vs. 19 ± 4 mmHg, p  < 0.001), RV-PA coupling (0.35 ± 0.08 vs. 0.48 ± 0.12, p  < 0.001), and RV s’(8.7 ± 2.2 vs. 9.5 ± 2.6 cm/s, p  < 0.001). Multivariate analysis showed that the improvement of RV-PA coupling was associated with baseline PASP ( r: -0.45, p  < 0.001) and PADP ( r: -0.45, p  < 0.001). Conclusions Sacubitril/valsartan improves RV-PA conjugation in patients with RVD and HFrEF, and has a positive impact on the ALBI score by improving liver function in patients with HFrEF.

Objective This study aimed to develop and validate a machine learning (ML)-based model for predicting 6-month all-cause mortality in patients diagnosed with infective endocarditis (IE), using retrospective data from a single tertiary care center. Additionally, key prognostic features were identified through model interpretability analysis. Methods A cohort of 444 patients with IE was retrospectively assessed and randomly divided into a training set ( n  = 310) and a test set ( n  = 134). Following feature selection, a Cox proportional hazards model and four ML-based survival models: Random Survival Forest, Extremely Randomized Survival Trees (EST), eXtreme Gradient Boosting, and Support Vector Machine were established. Model performance was assessed using the concordance index (C-index), time-dependent area under the curve (AUC), and Kaplan–Meier survival analysis. The model demonstrating optimal performance underwent interpretability analysis using Shapley additive explanations and an exploratory online tool was developed to demonstrate its functionality. Results Nine predictors were selected for model construction: hemoglobin concentration, presence of severe valvular regurgitation, occurrence of septic shock, New York Heart Association (NYHA) classification, serum albumin level, intracerebral hemorrhage, neutrophil percentage, history of coronary heart disease, and receipt of surgical intervention. The EST model demonstrated the highest predictive performance in the test set (C-index: 0.852), with the smallest discrepancy in performance between the training and test datasets, indicating favorable generalizability. Time-dependent AUCs at 30, 90, and 180 days were 0.957, 0.925, and 0.855, respectively. Surgical intervention was modeled as a protective factor, associated with lower mortality risk, while intracerebral hemorrhage, higher NYHA class, severe valvular regurgitation, and septic shock were associated with increased risk. Conclusions The EST model demonstrated a high predictive accuracy for 6-month mortality in patients with IE, outperforming both conventional and other ML models. Its robust performance and interpretability suggest that, following rigorous external and multicenter validation, it may serve as a hypothesis-generating tool for risk stratification. It is not yet validated for direct clinical application and should currently be considered exploratory.