Pulmonary hypertension (PH) associated with lung disease (Group 3 PH) is a serious complication that negatively impacts patient outcomes. This study aimed to assess the epidemiology and disease burden of Group 3 PH in the UK, focusing on interstitial lung disease (ILD-PH).

Retrospective cohort study.

Electronic medical records from the Clinical Practice Research Datalink Aurum, linked to Hospital Episode Statistics.

6690 incident cases of Group 3 PH, including 1561 ILD-PH cases, were identified from a sample of approximately 33 million individuals between January 2017 and December 2019.

Primary outcomes included prevalence and incidence of Group 3 PH and ILD-PH. Secondary outcomes included patient characteristics, overall survival, hospitalisations, outpatient visits and healthcare costs.

Over the 3-year period, prevalence and annual incidence were 139/million (95% CI 134 to 142) and 73/million/year (95% CI 70 to 76) for Group 3 PH and 36/million (95% CI 33 to 37.3) and 17/million/year (95% CI 16 to 19) for ILD-PH. Median overall survival was 19.3 (95% CI 17.77 to 20.8) for Group 3 PH and 15.1 months (95% CI 12.66 to 18.2) for ILD-PH. Following a PH diagnosis, all-cause inpatient visits increased by 33.6% from baseline. The all-cause annual hospitalisation rate was 1.63 (95% CI 1.6 to 1.65) for Group 3 PH and 1.36 (95% CI 1.31 to 1.4) for ILD-PH, with about half linked to PH diagnosis. Pulmonologists were the most consulted specialists, averaging 1.78 (95% CI 1.76 to 1.81) and 2.31 (95% CI 2.25 to 2.37) visits per patient per year for Group 3 PH and ILD-PH, respectively. Annual per-patient costs were £7761 (95% CI 7759 to 7762) for Group 3 PH and £7170 (95% CI 7167.17 to 7173.69) for ILD-PH.

Incidence and prevalence of Group 3 PH in the UK are consistent with other European countries. Patients had poor survival, with PH associated with half of hospital admissions, highlighting the negative impact of PH in chronic lung disease.

Observational studies suggest an association between glioblastoma (GBM) and cardiovascular diseases (CVDs), but a causal relationship remains unestablished. This study aimed to investigate the causal link between multiple CVDs and GBM risk. The inverse variance weighted method indicated that all 18 CVDs had significant causal associations with GBM (P < .05). Genetically predicted CVDs were uniformly associated with a lower risk of GBM (odds ratio < 1), identifying them as potential protective factors. Sensitivity analyses confirmed the absence of significant heterogeneity or horizontal pleiotropy, and the MR-Steiger test validated the correct causal direction. This Mendelian randomization (MR) study provides evidence that a range of CVDs are causally associated with a decreased risk of developing GBM. These findings suggest shared biological pathways and offer new insights for understanding GBM etiology. We conducted a 2-sample MR analysis using publicly available genome-wide association study data. GBM was the outcome, and 18 cardiovascular-related traits (including coronary artery disease, myocardial infarction, and venous thromboembolism) were exposures. Instrumental variables were single-nucleotide polymorphisms significantly associated with exposures (P < 5 × 10-8). The primary analysis used the inverse variance weighted method, supplemented with MR-Egger, weighted median, and weighted mode methods. Sensitivity analyses, including Cochran Q test, MR-Egger intercept test, leave-one-out analysis, and MR-Steiger directionality test, were performed to ensure robustness.

The accuracy of different risk prediction models must be directly compared using research evidence from each model. This study systematically collected, evaluated and synthesized comparative accuracy data of mortality risk models for acute coronary syndrome (ACS) patients to compare their performance.

An evidence-based approach was used to investigate ACS mortality risk prediction models. First, we searched multiple databases from 2009 to 2024, to identify multivariate predictive models for predicting ACS mortality risk. Included studies were screened, quality-assessed, and data extracted. PROBAST evaluated the risk of bias; heterogeneity was analyzed via MetaDiSc1.4 (I2 statistic). Data analysis used RevMan5.3 and MetaDiSc1.4. Sensitivity (SEN), specificity (SPE), positive/negative likelihood ratios (LR+/LR-), and area under the curve (AUC) of models were calculated for comparison.

A total of 8277 documents were retrieved, and 6 studies were finally included, involving 5 risk prediction models, a total of 24,911 patients with ACS, including 18,443 males (74.04%) and 6468 females (25.96%), with 1637 deaths. The SEN of the global registry of acute coronary events (GRACE) model was 0.78, SPE was 0.76, and AUC was 0.86; the SEN of the thrombolysis in myocardial infarction model was 0.51, SPE was 0.81, and AUC was 0.64; the SEN of the rapid emergency medicine score (REMS) model was 0.78, SPE was 0.46, and AUC was 0.41. The Acute physiology and chronic health evaluation II and REMS2 were reported separately due to non-combinable effect sizes, with SEN 0.77 to 0.95, SPE 0.22 to 0.99, and AUC 0.71-0.92. All 6 studies compared model accuracy. Pooled evidence indicated GRACE (AUC = 0.79) outperformed thrombolysis in myocardial infarction (0.59) and REMS (0.41); APACHE II (0.82) outperformed REMS (0.61) but was slightly inferior to GRACE (0.86).

The GRACE risk prediction model is highly accurate and includes comprehensive clinical research data. It allows medical staff to accurately assess the death risk of ACS patients and effectively reduce their mortality. Therefore, the study suggests that clinical nursing staff use the GRACE risk prediction model to assess the risk of death in patients with ACS.

Cardiovascular diseases (CVDs) are among the leading causes of morbidity and mortality worldwide. In the cardiovascular system, lipids serve as a primary energy source, and dysregulated lipid metabolism has been observed in many CVDs. Lipid droplets (LDs) are organelles that store lipids, including triglycerides and cholesterol. The biogenesis and lipolysis of LDs broadly influence lipid metabolism in cells in the cardiovascular system and contribute to CVDs. LDs homeostasis is modulated by lipid droplet-associated proteins (LDAPs), such as PLINs, CIDEs, BSCL2, ABHD5, and Rab18. These proteins have also been reported to be involved in various CVDs. Here, we summarize the roles of LDAPs in CVDs and discuss them in detail. To our knowledge, this is the first review to systematically elucidate the associations between LDAPs and CVDs.

Cardiovascular diseases (CVD) are the leading cause of death in developing countries, imposing a significant burden on society. Early detection of patients at higher risk of CVD events could reduce mortality. None of the models currently used for this purpose incorporates insulin resistance (IR), which can be measured using triglyceride and glucose levels. This study aims to explore the effectiveness of the triglyceride-glucose (TyG) index in predicting CVD events using machine learning models.

This study utilized data from the Mashhad Stroke and Heart Atherosclerotic Disorder (MASHAD) cohort. Patients were evaluated at baseline and monitored for over ten years for CVD events. Eleven machine learning models, including a multilayer perceptron (MLP) and a decision tree, were used to evaluate the predictive value of the TyG index in conjunction with traditional risk factors.

The study population had a CVD event prevalence rate of 10.9%. The average age was 48.08 ± 8.26 years, with 60.0% of participants being female. The mean TyG index was 8.59 ± 0.66. The MLP and AdaBoost classifier models demonstrated the highest predictive accuracy with ROC-AUC scores of 0.77 and 0.766, respectively. The TyG index was identified as the fourth most significant predictor in the AdaBoost Classifier and MLP models, although it ranked lower in other models.

This study highlights the potential benefits of incorporating the TyG index into traditional CVD risk prediction models to enhance accuracy and applicability, especially in developing countries.

Intimal hyperplasia is a pathological process that occurs due to vascular endothelial injury following PCI. TGF-β/Smad signaling-mediated endothelial-mesenchymal transition (EndMT) is particularly important in the development of intimal hyperplasia. Tanshinone ⅡA (TanⅡA), which is extracted from Salvia miltiorrhiza (also known as Danshen), has been shown to be safe and effective in reducing inflammatory factors and enhancing endothelial function in patients after PCI. To investigate the mechanism of TanⅡA in alleviating TGF-β1-induced EndMT, the EndMT model was developed by treating human umbilical vein endothelial cells (HUVECs) with TGF-β1. The effects of different concentrations of TanⅡA on HUVECs cell viability were assessed using WST-1 assay. Cell morphology was examined using fluorescent staining. A wound-healing assay was performed to evaluate the migratory ability of the cells. The expression of specific proteins, including the endothelial marker VE-Cadherin, and interstitial cell markers such as α-SMA, FSP-1, Smad2/3 (along with their phosphorylated forms), as well as the transcription factors Slug and Snail, was analyzed using PCR and Western blotting. The results of the WST-1 experiment showed that TanⅡA concentration of 20 μmol·L^-1 significantly inhibited cell proliferation. Migration experiments showed that TGF-β significantly enhanced cell migration abilities, while TanⅡA was able to reverse this effect. Western blotting analysis revealed a notable decrease in the expression level of VE-Cadherin protein, alongside a significant increase in the levels of α-SMA, FSP-1, and COL1 proteins following TGF-β induction. Co-treatment with TanⅡA and TGF-β1 resulted in a notable reduction in the levels of Smad2 and Smad3, as well as their phosphorylation, compared to the model group. These findings suggest that TanⅡA can inhibit TGF-β-induced EndMT by blocking the activation of the Smad signaling pathway.

In recent years, interest in the potential of liver enzymes has increased to serve as markers for cardiovascular disease (CVD) risk. This systematic review and meta-analysis aimed to examine the relationship between liver enzymes in blood tests and non-fatal cardiovascular disease events, focusing on retrospective and prospective cohort studies.

A methodical search was carried out across several academic databases, including Scopus, PubMed, and Web of Science. Relevant keywords were identified and categorized into two groups. Two reviewers independently reviewed the obtained papers to recognize related studies. Then, the authors independently gathered the necessary information. The quality of the papers was evaluated using the Newcastle-Ottawa Scale (NOS). Moreover, a meta-analysis was also performed on the values of the hazard ratio and odds ratio reported in some studies.

36 articles were entered into the study. Most of the studies (89.3 percent) showed that there are positive significant relationships between gamma-glutamyl transferase (GGT) enzyme and non-fatal cardiovascular disease events. While only 50.0 percent of the studies on alanine aminotransferase (ALT) and 66.7 percent of the papers on aspartate aminotransferase (AST) revealed a positive significant association with non-fatal cardiovascular disease events. The pooled value of the hazard ratio related to the GGT, ALT, and AST levels for non-fatal cardiovascular disease events was computed by 1.28 (95% CI: 1.21-1.36), 1.23 (95%CI: 1.07-1.42), and 1.35 (95%CI: 1.17-1.55).

There is a strong agreement on GGT for diagnosing cardiovascular diseases. These results can be applied as guidance for the diagnosis of cardiovascular diseases using liver enzymes as routine blood tests.

Cardiovascular diseases (CVDs) remain a leading cause of mortality worldwide. We explore the application of statistical shape modeling (SSM) as a powerful tool in cardiac anatomy assessment, facilitating innovative approaches to diagnosis and treatment. SSM uses advanced mathematical and statistical techniques to understand the geometric properties of anatomical structures across populations. By identifying significant shape parameters, it captures and quantifies subtle variations that may elude traditional approaches. We discuss its evolution, from landmark-based methods to point distribution models for establishing the point-to-point correspondence crucial for accurate shape analysis. We delve into the statistical techniques used to measure shape variability, with a focus on principal component analysis for dimensionality reduction. Key evaluation metrics in the assessment of model performance, such as compactness, generalization and specificity, are reviewed. The clinical utility of SSM across the spectrum of CVDs is examined, covering diagnosis, risk stratification, treatment optimization, follow-up and research applications. Future directions, including the development of multi-label models, integration of deep learning approaches, and spatio-temporal SSM to capture dynamic changes in cardiac geometry, are considered. Through this narrative review, we aim to underscore SSM's promise as a powerful tool in combating CVDs and advancing personalized medicine, ultimately improving patient outcomes.

Environmental factors are often detrimental; however, certain environments enhance immune resilience. Notably, children raised on traditional farms show reduced allergies and asthma prevalence. Here, we investigated how a beneficial environment, using farm dust (FD) extract, influenced lung immune function in ovalbumin-induced allergic inflammation. FD exposure reduced allergic lung inflammation and increased monocyte-derived macrophage (MDM) recruitment. Single-cell RNA sequencing revealed that FD-exposed MDMs had altered gene expression, including dampened Ccl8 and major histocompatibility complex class II expression, impairing eosinophil recruitment and antigen presentation. RNA sequencing and assay for transposase-accessible chromatin using sequencing confirmed FD-induced epigenetic reprogramming ex vivo, on bone marrow-derived macrophages. This modulation, seen in both human and murine cells, relied on histone deacetylase activity sustained by peroxisome proliferator-activated receptor γ signaling. These findings suggest that beneficial environmental exposures can reprogram immune cells and may offer a previously unidentified strategy for asthma prevention.

China accounted for 6.8% of global TB cases, and most patients are first diagnosed in general hospitals where chest X-rays (CXR) are widely used for early TB detection. To facilitate diagnosis in resource-limited settings, our study evaluates a CNN-based AI model trained on Chinese CXR data (JF CXR-1 v2), including its experimental application to CT localizer images.

This retrospective study was conducted at China-Japan Friendship Hospital, including 290 CXR images and 433 CT localizer images from TB patients diagnosed between 2017 and 2021. The AI algorithm's diagnostic performance was assessed using sensitivity, specificity, accuracy, Kappa value, and AUC from ROC analysis.

The AI algorithm demonstrated high diagnostic performance on CXR images, achieving an AUC of 0.960 with 91.7% sensitivity and 92.7% specificity in bacteriologically confirmed TB cases. On localizer images of the chest CT, while the performance was more modest (AUC 0.719), a significant correlation between CXR and CT predictions in 105 paired cases suggests potential for cross-modality application with further validation.

The algorithm shows decent diagnostic capability for the CXR samples in this study. This AI algorithm developed based on CXR can, to some extent, identify the imaging features of pulmonary TB when applied to localizer images of chest CT.