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Pulmonary embolism (PE) is a serious cardiovascular condition and the third leading cause of cardiovascular mortality worldwide. However, its clinical presentation is often non-specific, making timely detection challenging. Biomarkers are commonly used to support early diagnosis and risk stratification. Molecular biomarkers provide information related to coagulation, inflammation, and cardiac injury. Electrocardiography (ECG) reflects cardiac functional changes caused by right ventricular (RV) stress and dilation secondary to increased pulmonary vascular resistance. Individually, these biomarkers have limited diagnostic accuracy. A promising approach to improving PE management involves integrating multimodal clinical data using Artificial Intelligence (AI). AI-based models can detect subtle patterns in ECG signals and molecular biomarker profiles that may be missed by conventional analysis. Combining these data sources may enhance diagnostic accuracy, refine risk assessment, and support personalized treatment. Despite ongoing challenges, including data quality, interpretability, and ethical considerations, AI-driven integration of ECG and molecular biomarkers represents a significant step forward in PE diagnosis and management. Further validation in large, prospective clinical studies is required.

Residual congestion (RC) at discharge predicts adverse outcomes in heart failure with reduced ejection fraction (HFrEF). Its impact on the implementation of guideline-directed medical therapies (GDMT) remains unclear. N-terminal pro-B-type natriuretic peptide (NT-proBNP) trajectory during hospitalisation reflects RC and may be associated with GDMT implementation. The aim was to assess whether discharge NT-proBNP and a fall in NT-proBNP < 30% during hospitalisation (ΔNT-proBNP < 30%) predict GDMT underuse in acute HFrEF. In this prospective observational study, NT-proBNP was measured at hospital admission and 48–72 h before discharge. Provision of individual GDMT drug classes was assessed and GDMT underuse was defined as prescription of <3 key GDMT drug classes at discharge. 391 HFrEF patients (mean age, 69.9 ± 13.1years, 67.3% male) were included. ΔNT-proBNP < 30% was identified in 108 (27.6%). Higher discharge NT-proBNP was independently associated with lower likelihood of prescribing ACE-inhibitors, sacubitril/valsartan, eplerenone/spironolactone, or empagliflozin/dapagliflozin. ΔNT-proBNP < 30% was associated with 17% higher odds of GDMT underuse (95% confidence interval, 1.10–1.31, p < 0.001), regardless of clinical characteristics or in-hospital management. Patients with ΔNT-proBNP < 30% were discharged on lower doses of titratable GDMT medications. In-hospital NT-proBNP burden and trajectory, as markers of RC, are associated with GDMT underutilisation at discharge in acute HFrEF. Addressing RC may impact treatment quality in acute HFrEF.

Background: Studies suggested irisin’s involvement in insulin sensitivity, conversion of white adipose tissue into brown which is more metabolically active. Studies have been shown negative correlation of irisin levels with presence of diabetes mellitus (DM). Association of irisin levels with body mass index (BMI) and lipid profile could be useful in monitoring metabolic disorders and potential complications in DM. Methods: This cross-sectional study enrolled patients which were divided in 3 groups based on diabetes status and BMI: participants without DM, patients with DM, and a normal BMI (<25 kg/m²) and patients with DM and an increased BMI (⩾25 kg/m²). Irisin levels were measured from blood samples and correlation was made with parameters of lipid profile. We wanted to find differences in irisin concentration in comparing groups and to examine the correlation of irisin and metabolic parameters. The relationships between irisin levels and metabolic parameters, including lipid profile and the triglyceride-glucose (TyG) index, were assessed using Pearson’s, and Spearman’s correlation analysis, depending on data distribution. Results: Irisin levels were significantly lower in patients with DM compared to non-diabetic individuals, regardless to BMI (patients without diabetes: median 25.47 ng/ml, IQR (22.27-27.54), with diabetes and BMI < 25 kg/m²: 22.16 ng/ml, IQR (19.29-23.76) and patients with diabetes and BMI ⩾ 25 kg/m²: mean ± SD (21.77 ± 5.72) ng/ml, P = .004). Additionally, we report a 1.15-fold decrease in irisin levels in group with diabetes, and BMI < 25 kg/m² compared to non-diabetic individuals and a 1.18-fold decrease in group with diabetes and BMI ⩾ 25 kg/m² compared to non-diabetic individuals. Additionally, lower irisin levels were correlated with higher triglycerides (r = −.343, P = .024), lower HDL cholesterol (r = .363, P = .017), and higher TyG index (r = −.315, P = .04), indicating a potential link between irisin and metabolic dysregulation. No significant association was observed between irisin levels and BMI. Conclusion: Our findings suggest that irisin may serve as a biomarker for monitoring metabolic dysregulation in diabetes, particularly in relation to lipid metabolism and insulin resistance. Further research is needed to clarify its role in metabolic disease progression and potential therapeutic implications. Plain Language Summary Irisin in Type 2 Diabetes and Obesity: A Biomarker of Metabolic and Lipid Dysregulation This study looked at a hormone called irisin, which is linked to how the body uses insulin and how it stores fat. Irisin may help convert white fat, which stores energy, into brown fat, which burns energy. Previous research suggests that people with type 2 diabetes tend to have lower levels of irisin, and it may also be connected to cholesterol levels and body weight. Our goal was to see how irisin levels differ in people with and without diabetes, and whether body weight (measured by body mass index-BMI) plays a role. We also looked at how irisin is related to cholesterol, triglycerides (a type of fat in the blood), and the TyG index, which gives an idea of how the body handles sugar and fat. We studied three groups of people: (1) People without diabetes, (2) People with diabetes and a normal body weight, (3) People with diabetes and a higher body weight. We took blood samples and measured irisin levels. We found that people with type 2 diabetes had lower levels of irisin, no matter what their body weight was. Specifically, irisin levels were about 13% to 15% lower in people with diabetes compared to those without. Lower irisin levels were also linked with higher triglycerides, lower “good” cholesterol (HDL), and higher TyG index values, suggesting that lower irisin might be a sign of poor metabolic health. However, irisin levels did not seem to be directly related to body weight alone. Our results suggest that irisin could be a helpful marker for tracking metabolic problems in people with diabetes, especially when it comes to fat and sugar metabolism. In the future, irisin might even become a target for new treatments. More studies are needed to better understand its role and how it might be used to improve care for people with metabolic disorders like diabetes.

Right ventricular (RV) dysfunction is common and linked to poor outcomes across conditions such as heart failure (HF), acute coronary syndromes, pulmonary embolism, and pulmonary hypertension. While imaging, electrocardiogram (ECG), and invasive tests remain central to RV assessment, circulating biomarkers offer a rapid, non-invasive, and reliable alternative. These biomarkers reflect key pathophysiological processes, including myocardial injury, stress, fibrosis, inflammation, congestion, and multiorgan involvement. High-sensitivity troponins and natriuretic peptides (BNP, NT-proBNP) are already widely used, while emerging biomarkers—such as CA125, copeptin, galectin-3, and others—may enhance diagnostic accuracy and risk stratification. Some, like CA125 and NT-proBNP, have shown promise in guiding post-discharge therapy. However, challenges remain regarding the specificity of biomarkers for RV dysfunction and their role across different clinical contexts. This review provides an integrated overview of RV dysfunction, with a focus on the diagnostic and therapeutic potential of both established and novel biomarkers.

Background and Objectives: Acute pulmonary thromboembolism (PTE) is one of the leading causes of cardiovascular mortality. Recent insights into PTE pathophysiology emphasize the complex interplay of multiple mechanisms, particularly the roles of thrombosis and inflammation. Materials and Methods: This retrospective, single-center observational study included 138 participants: 69 adult patients diagnosed with PTE via computed tomography pulmonary angiography (CTPA) and 69 matched healthy controls. Upon admission, a standard 12-lead electrocardiogram (ECG) was performed, and Daniel’s score was calculated. Peripheral blood samples were collected to assess inflammatory biomarkers and hemogram-derived ratios (SII, NLR, dNLR, NPR, PLR, LMR). CTPA scans were analyzed not only for diagnostic purposes and PTE localization but also for inflammatory changes. PTE severity was classified according to the 2019 ESC guidelines. Results: Patients with PTE had significantly higher Daniel’s ECG scores, initial values of inflammatory biomarkers (WBC, neutrophils, IL-6, CRP) and hemogram-derived ratios (SII, NLR, dNLR, NPR) compared to controls. In multivariate analysis, older age (OR = 1.05; p = 0.038), higher Daniel’s ECG score (OR = 1.24; p < 0.001), and higher dNLR (OR = 1.40; p = 0.001) were found as an independent predictors of PTE severity. Ground-glass opacity (GGO) was the most common parenchymal and pleural inflammatory finding relating to CTPA (48.4%), but these findings did not show significant predictive value for PTE severity. Conclusions: Daniel’s ECG score and dNLR, both readily available and cost-effective biomarkers demonstrated independent predictive value for assessing PTE severity.

Background: Hepatocellular carcinoma (HCC) frequently develops in patients with chronic hepatitis B and C. Early detection is critical, but current methods, including ultrasound and AFP, have suboptimal accuracy. Objectives: This study aimed to evaluate the predictive performance of protein induced by vitamin K absence or antagonist-II (PIVKA-II) and alpha-fetoprotein (AFP) testing, alone and in combination, for HCC development. Methods: A retrospective cohort study at a single university center included 242 CHB and 181 CHC patients. Data on demographics, clinical status, laboratory parameters, and imaging were collected, with fibrosis and steatosis assessed by FibroScan®. Serum AFP and PIVKA-II were measured, but measurements of PIVKA-II in patients receiving vitamin K antagonists were excluded from the analysis. HCC diagnosis and staging followed clinical guidelines. Cox regression and ROC analyses identified independent predictors and evaluated biomarker accuracy for HCC detection. Results: HCC incidence was comparable between cohorts (5.0% in CHB vs. 5.5% in CHC). Both AFP and PIVKA-II independently predicted HCC development in multivariate models adjusted for age and sex. The combined biomarker score (AFP × PIVKA-II) showed superior predictive accuracy with hazard ratios of 1.38 (CHB) and 1.36 (CHC). ROC analyses demonstrated high discriminative ability for PIVKA-II (AUC ~0.81) and AFP (AUC ~0.83) in both cohorts. Additional independent predictors were chronic alcohol abuse, cirrhosis, and higher liver stiffness measurements. Specific viral factors such as HBeAg positivity and HCV subgenotype 1b were also associated with increased HCC risk. Conclusions: AFP and PIVKA-II are independent, valuable biomarkers for HCC risk in chronic hepatitis B and C. Combined use improves early detection, aiding timely treatment. These results support adding PIVKA-II to AFP in surveillance, but larger studies are needed to confirm the findings and refine cut-off values.

Background and Objectives: Chronic inflammation (CIn) is common among peritoneal dialysis (PD) patients and contributes to adverse outcomes. However, the prognostic value of the neutrophil-to-lymphocyte ratio (NLR) and fibrinogen-to-albumin ratio (FAR) in PD remains uncertain. Methodology: In this prospective cohort study, 65 PD patients were followed for 18 months. Baseline demographic, clinical and laboratory data were collected and inflammatory indices were calculated. The composite outcome was all-cause mortality or transfer to hemodialysis (HD). Logistic regression analyses were used to identify independent predictors of outcomes. Results: Over the 18-month follow-up, 23 patients (35.4%) died and 13 (20.0%) transferred to HD. Patients with adverse outcomes had higher baseline FAR, C-reactive protein (CRP) and serum glucose (Glc) levels and lower triglycerides (TG). In multivariate analysis, higher FAR (OR 5.28, 95% CI 1.16–24.12), CRP (OR 1.28, 95% CI 1.02–1.62) and PTH (OR 1.01, 95% CI 1.00–1.01) were independently associated with adverse outcomes, while NLR showed marginal significance. In the mortality-only model, FAR (OR 3.99, 95% CI 1.17–13.61) and PTH remained significant predictors. Conclusions: FAR demonstrated a significant prognostic association with mortality and composite adverse outcomes in PD patients, whereas NLR had limited predictive value. Albumin-based inflammatory indices such as FAR may complement established markers for risk stratification. Larger multicenter studies are warranted to validate these findings.

Background Hyperglycemia has detrimental effect on ischemic myocardium, but the impact of acute hyperglycemia on the myocardium in asymptomatic diabetic patients has not been fully elucidated. Thus, this follow-up study was aimed to investigate the effects and reversibility of acute hyperglycemia on regional contractile function of left ventricle (LV) in diabetic patients without cardiovascular disease. Methods The two-dimensional speckle tracking echocardiography (2D-STE), including multilayer strain analysis, was used for evaluation of global and regional LV function in asymptomatic, normotensive patients with uncomplicated diabetes, with acute hyperglycemia ( ≥ 11.1 mmol/l) (Group A, n = 67), or with optimal metabolic control (fasting plasma glucose < 7 mmol/l and HbA1c < 7%) (Group B, n = 20), while 20 healthy individuals served as controls (Group C). In group A, after 72 h of i.v. continuous insulin treatment (at the time euglycemia was achieved) (second examination) and after 3 months following acute hyperglycemia (third examination) 2D-STE was repeated. Results Global longitudinal strain (GLS) (− 19.6 ± 0.4%) in Group A was significantly lower in comparison to both groups B (− 21.3 ± 0.4%; p < 0.05) and C (− 21.9 ± 0.4%; p < 0.01) at baseline, while we could not detect the differences between groups B and C. Peak systolic longitudinal endocardial (Endo), mid-myocardial (Mid) and epicardial (Epi) layer strain were significantly lower in group A at baseline compared to both groups B and C. Deterioration in peak systolic circumferential strain was observed at basal LV level, in all three layers (Endo, Mid and Epi) and in mid-cavity LV level in Epi layer in group A in comparison to group C. Moreover, in group A, after euglycemia was achieved (at second and third examination) GLS, as well as peak longitudinal and circumferential strain remain the same. Conclusion Acute hyperglycemia in asymptomatic diabetic patients has significant negative effects on systolic LV myocardial mechanics primarily by reducing GLS and multilayer peak systolic longitudinal and circumferential strain which was not reversible after three months of good glycemic control.

Estrogen signaling plays an important role in pituitary development and function. In sensitive rat or mice strains of both sexes, estrogen treatments promote lactotropic cell proliferation and induce the formation of pituitary adenomas (dominantly prolactin or growth-hormone-secreting ones). In male patients receiving estrogen, treatment does not necessarily result in pituitary hyperplasia, hyperprolactinemia or adenoma development. In this review, we comprehensively analyze the mechanisms of estrogen action upon their application in male animal models comparing it with available data in human subjects. Sex-specific molecular targets of estrogen action in lactotropic (PRL) cells are highlighted in the context of their proliferative and secretory activity. In addition, putative effects of estradiol on the cellular/tumor microenvironment and the contribution of postnatal pituitary progenitor/stem cells and transdifferentiation processes to prolactinoma development have been analyzed. Finally, estrogen-induced morphological and hormone-secreting changes in pituitary thyrotropic (TSH) and adrenocorticotropic (ACTH) cells are discussed, as well as the putative role of the thyroid and/or glucocorticoid hormones in prolactinoma development, based on the current scarce literature.

Nanoparticles (NPs), a distinct class of particles ranging in size from 1 to 100 nm, are one of the most promising technologies of the 21st century, and titanium dioxide NPs (TiO2 NPs) are among the most widely produced and used NPs globally. The increased application of TiO2 NPs raises concerns regarding their global safety and risks of exposure. Many animal studies have reported the accumulation of TiO2 NPs in female reproductive organs; however, evidence of the resultant toxicity remains ambiguous. Since the surface area and chemical modifications of NPs can significantly change their cytotoxicity, we aimed to compare the toxic effects of pristine TiO2 powder with surface-modified TiO2 powders with salicylic acid (TiO2/SA) and 5-aminosalicylic acid (TiO2/5-ASA) on the ovaries, oviducts, and uterus on the 14th day following acute oral treatment. The results, based on alterations in food and water intake, body mass, organ-to-body mass ratio, hormonal status, histological features of tissues of interest, and antioxidant parameters, suggest that the modification with 5-ASA can mitigate some of the observed toxic effects of TiO2 powder and encourage future investigations to create NPs that can potentially reduce the harmful effects of TiO2 NPs while preserving their positive impacts.