Heart failure (HF) is a global health burden characterized by high morbidity and mortality, necessitating advancements in diagnostic and therapeutic approaches. Molecular diagnostics, encompassing genomics, transcriptomics, proteomics, metabolomics, and epigenetics, offer unprecedented insights into HF pathogenesis, aiding early diagnosis, risk stratification, and personalized management. This state-of-the-art review critically examines recent developments in molecular diagnostics in HF, evaluates their translational potential, and highlights key challenges in clinical implementation. Emerging tools such as liquid biopsy, multi-omics integration, and artificial intelligence (AI)-driven platforms are explored. We propose strategies to enhance clinical translation, equity in access, and utility in guiding treatment, thereby advancing precision cardiovascular medicine

Background: Diabetes mellitus (DM), periodontal disease (PD), and cardiovascular disease (CVD) are highly prevalent global health conditions with overlapping pathophysiological mechanisms. Emerging evidence suggests a bidirectional and synergistic relationship among them, driven by chronic inflammation, immune dysregulation, oxidative stress, and microbial dysbiosis. Objective: This review synthesizes current literature on the interconnectedness of DM, PD, and CVD, emphasizing shared molecular pathways, clinical implications, and opportunities for integrated management. Methods: A systematic review and narrative synthesis of recent clinical trials, observational studies, and multi-omics investigations was conducted to explore the mechanisms linking these three conditions. A structured literature search was performed across PubMed, Scopus, and Web of Science from database inception until 30 June 2025. Key findings were contextualized within systems biology, precision medicine, and real-world clinical strategies. Results: DM exacerbates periodontal inflammation and accelerates tissue destruction via hyperglycemia-induced inflammatory mediators, while periodontitis worsens glycemic control and insulin resistance. Both conditions independently elevate cardiovascular risk, and their co-occurrence significantly amplifies the incidence of adverse cardiovascular events. Shared biomarkers such as Interleukin (IL)-6, Tumor Necrosis Factor (TNF)-α, and CRP, as well as overlapping genetic and epigenetic signatures, underscore a common inflammatory axis. Periodontal therapy has demonstrated modest but meaningful benefits on glycemic control and endothelial function, while cardiometabolic therapies (e.g., statins, Glucagon-Like Peptide (GLP-1) receptor agonists, SGLT2 inhibitors) show potential to improve periodontal outcomes. Probiotics, microbiome-targeted therapies, and AI-based risk models are emerging as future tools. Conclusions: DM, PD, and CVD form a mutually reinforcing triad mediated by systemic inflammation and metabolic dysregulation. Integrated, multidisciplinary care models and precision health strategies are essential to address this inflammatory burden and improve long-term outcomes. Further large-scale interventional trials and mechanistic human studies are needed to establish causal links and optimize combined therapeutic approaches.

Intracranial epidermoid cysts are rare, representing almost 1% of all primary tumors and when are located in the diploe result from entrapped ectodermal embryonic remnants. Because of frequent complications, imaging studies are mandatory for highlighting erosions of both outer and inner table of the calvaria and treatment preparation. We enlisted three female patients within our study, comparing imaging with histopathology aspects. Even though the bone destruction was evident, no atypia or malignant signs were highlighted in serial sections. The interesting fact that we present is that our patients are all females, opposed to what is written in literature. Although the positive diagnosis can be made by imaging, histopathological examination of these cysts is mandatory for identifying malignant behavior.

Background: Anemia is a common comorbidity in heart failure (HF) and has been associated with adverse clinical consequences. This retrospective, descriptive cohort study examined phenotype-specific differences in anemia severity, clinical presentation, comorbid burden, and in-hospital management across HF subtypes classified by left ventricular ejection fraction (LVEF). Methods: We retrospectively analyzed 443 adult patients hospitalized with concurrent HF and anemia from January 2022 to December 2024. Patients were stratified by LVEF into HFrEF (<40%), HFmrEF (40–49%), and HFpEF (≥50%). All patients included met WHO criteria for anemia. Demographic, clinical, paraclinical, and therapeutic data were extracted, and descriptive statistical methods were used to evaluate intergroup differences. No formal time-to-event analyses (e.g., Kaplan–Meier curves) were performed; instead, exploratory cumulative readmission analyses using fixed follow-up windows were conducted. In-hospital mortality was recorded and stratified by HF phenotype. Results: The cohort comprised 213 (48.0%) HFrEF, 118 (26.6%) HFmrEF, and 112 (25.3%) HFpEF patients. The distribution of anemia severity, management strategies, and comorbidity profiles varied significantly across phenotypes. Severe anemia predominated in the HFmrEF cohort (54.2%), whereas mild anemia was most common in HFpEF (52.1%) and HFrEF (52.1%). Mean hemoglobin concentrations were 8.39 ± 1.79 g/dL (HFmrEF), 9.07 ± 2.47 g/dL (HFpEF), and 8.62 ± 1.94 g/dL (HFrEF). Rates of atrial fibrillation (48.2% in HFpEF), hypertensive ECG changes (63.4% in HFpEF), and ischemic-lesion patterns (>50% in HFrEF) differed by cohort. Echocardiographically, grade III mitral regurgitation and severe pulmonary hypertension each affected 25.4% of HFmrEF patients, whereas HFpEF patients most often exhibited grade II mitral regurgitation (42.9%) and moderate pulmonary hypertension (42.9%). HFrEF patients had severe pulmonary hypertension. Intravenous (IV) iron was the primary treatment modality, with highest utilization in HFmrEF. IV iron use ranged from 69.9% (HFrEF) to 84.8% (HFmrEF), with transfusion rates of 5.6% (HFrEF)–16.1% (HFpEF). Comorbid burdens differed by phenotype: HFrEF was associated with structural heart disease, HFmrEF with vascular and hepatic pathology, and HFpEF with metabolic and degenerative comorbidities. Discharge pharmacotherapy reflected phenotype-specific treatment patterns. Conclusions: This real-world descriptive analysis highlights substantial variation in anemia burden and management across the HF spectrum. While limited to descriptive findings, our analysis highlights the heterogeneity of anemia in HF and describes observed associations across phenotypes, without implying causality. These findings should be interpreted as hypothesis-generating. These findings are observational, exploratory, and cannot establish a causal relationship between intravenous iron use and survival.

Background: Dilated cardiomyopathy (DCM) is a major contributor to heart failure-related morbidity and mortality. While type 2 diabetes mellitus (T2DM), obesity, and thyroid dysfunction are individually linked to cardiovascular disease, their combined effects on DCM remain poorly understood. Objective: To evaluate the independent and synergistic associations of diabetes (stratified by treatment), thyroid dysfunction, and obesity with the prevalence of DCM and 30-day hospital readmission. We further examined the utility of a composite Metabolic Burden Score for risk stratification. Methods: In this retrospective cohort study, electronic health record data from 1079 adult patients at a tertiary care center were analyzed. Multivariable logistic regression, including ridge regularization, was used to identify predictors of DCM. Endocrine phenotypes were stratified by diabetes and thyroid status. A Metabolic Burden Score (range: 0–3) based on diabetes, obesity, and thyroid dysfunction was developed and correlated with clinical outcomes. Results: DCM was diagnosed in 46% of the cohort. Non-insulin-treated diabetes (OR: 6.93; 95% CI: 3.78–12.73), hypothyroidism (OR: 1.78; 95% CI: 1.02–3.11), and male sex (OR: 2.33; 95% CI: 1.36–4.00) were independently associated with increased DCM risk. Obesity was not independently predictive but contributed to DCM prevalence when assessed within the Metabolic Burden Score. DCM prevalence increased across burden strata, reaching 50% in the high-risk group. Notably, the moderate-risk group had the highest 30-day readmission rate (42.8%). Conclusions: Non-insulin-treated diabetes and hypothyroidism are key metabolic drivers of DCM. A simple composite burden score offers a clinically useful tool for stratifying risk of DCM and early readmission. These findings support integrated endocrine–cardiac screening strategies to improve early identification and prevention of structural heart disease.

Artificial intelligence (AI) has increasingly become a transformative tool in cardiology, particularly in diagnosing and managing atrial fibrillation (AF), the most prevalent cardiac arrhythmia. This review aims to critically assess and synthesize current AI methodologies and their clinical relevance in AF diagnosis, risk prediction, and therapeutic guidance. It systematically evaluates recent advancements in AI methodologies, including machine learning, deep learning, and natural language processing, for AF detection, risk stratification, and therapeutic decision-making. AI-driven tools have demonstrated superior accuracy and efficiency in interpreting electrocardiograms (ECGs), continuous monitoring via wearable devices, and predicting AF onset and progression compared to traditional clinical approaches. Deep learning algorithms, notably convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have revolutionized ECG analysis, identifying subtle waveform features predictive of AF development. Additionally, AI models significantly enhance clinical decision-making by personalizing anticoagulation therapy, optimizing rhythm versus rate-control strategies, and predicting procedural outcomes for catheter ablation. Despite considerable potential, practical adoption of AI in clinical practice is constrained by challenges including data privacy, explainability, and integration into clinical workflows. Addressing these challenges through robust validation studies, transparent algorithm development, and interdisciplinary collaborations will be crucial. In conclusion, AI represents a paradigm shift in AF management, promising improvements in diagnostic precision, personalized care, and patient outcomes. This review highlights the growing clinical importance of AI in AF care and provides a consolidated perspective on current applications, limitations, and future directions.