Search Results (345)

Background and Objectives: Identification of obstructive coronary artery disease (CAD) in patients presenting with a dilated cardiomyopathy phenotype is clinically important because it may influence etiologic classification, prognosis, and subsequent management. However, real-world angiography-based data on the prevalence and angiographic severity of obstructive CAD in this setting remain limited, particularly in regional single-center cohorts. Materials and Methods: This retrospective single-center cohort study included consecutive patients presenting with a newly diagnosed dilated cardiomyopathy (DCM) phenotype of initially unknown etiology who underwent first-time diagnostic invasive coronary angiography at St. Marina University Hospital, Varna, Bulgaria, between January 2010 and December 2025. Obstructive CAD was defined as stenosis >70% in a major epicardial coronary artery with a reference vessel diameter >2.5 mm. Patients were categorized as having non-obstructive or no significant coronary stenoses, single-vessel disease, two-vessel disease, or three-vessel disease. Additional descriptive analysis assessed severe occlusive coronary lesions, defined as chronic total occlusions or subtotal stenoses. The primary endpoint was the prevalence of obstructive CAD. Secondary analyses included angiographic disease extent, vessel-level coronary involvement, severe occlusive lesions, and coronary revascularization. Results: A total of 107 patients were included. Mean age was 55.4 ± 10.9 years, and 97 patients (90.7%) were male. Obstructive CAD was identified in 19 patients (17.8%; 95% confidence interval [CI], 11.7–26.1%), whereas 88 patients (82.2%) had non-obstructive or no significant coronary stenoses. Among patients with obstructive CAD, 3 (15.8%) had single-vessel disease, 4 (21.1%) had two-vessel disease, and 12 (63.2%) had three-vessel disease. Severe occlusive coronary lesions were present in 10 of 19 patients (52.6%); 8 (42.1%) had at least one chronic total occlusion and 5 (26.3%) had at least one subtotal stenosis. Conclusions: In this retrospective single-center cohort of patients presenting with a dilated cardiomyopathy phenotype and referred for coronary angiography, obstructive CAD was present in fewer than one in five patients. In contrast, most had non-obstructive or no significant epicardial coronary stenoses. However, when obstructive CAD was present, three-vessel disease and severe occlusive lesions were frequent. These findings indicate that obstructive epicardial CAD was uncommon in this selected angiography-referred cohort while underscoring the clinical role of coronary angiography when an ischemic or mixed etiology is clinically suspected. Full article

Background/Objectives: Type 2 diabetes mellitus (T2DM) is a global epidemic strongly associated with an increased risk of heart failure, independent of coronary artery disease or hypertension. This condition, historically termed diabetic cardiomyopathy (DCM) and recently redefined as “diabetic myocardial disorder,” remains frequently underdiagnosed in its subclinical stages. The objective of this non-systematic review is to synthesize current evidence on the pathophysiological mechanisms, diagnostic advancements, and evolving therapeutic strategies for diabetic myocardial involvement. Methods: A comprehensive review of contemporary literature was conducted, focusing on recent consensus statements from the ESC and AHA, large-scale epidemiological data (IDF/WHO), and pivotal clinical trials (EMPA-REG, DAPA-HF, and LEADER). We analyzed the role of multimodal imaging—specifically speckle-tracking echocardiography (STE) and multiparametric cardiac magnetic resonance (CMR)—and circulating biomarkers in early phenotyping. Results: Pathophysiological drivers include lipotoxicity, oxidative stress, and AGE-mediated fibrosis. Advanced imaging techniques, such as global longitudinal strain (GLS) and CMR T1-mapping/ECV quantification, demonstrate superior sensitivity over LVEF in detecting early subendocardial dysfunction and diffuse fibrosis. Furthermore, NT-proBNP serves as a robust prognostic marker for the HFpEF-like trajectory typical of diabetes. Clinically, the therapeutic landscape has shifted with SGLT2 inhibitors and GLP-1 receptor agonists, which provide significant cardioprotection and reduction in heart failure hospitalizations through mechanisms beyond glycemic control. Conclusions: Diabetic myocardial disorder represents a complex continuum within the cardiometabolic spectrum. Early detection through multimodal imaging and biomarkers is essential for risk stratification. Integrating novel glucose-lowering therapies with proven cardiovascular benefits is now mandatory to alter the natural history of the disease and prevent progression to overt heart failure. Full article

1-[(4S)-4-benzyl-1,3-oxazolidin-2-one-3-yl]propyltriphenylphosphonium tetrafluoroborate was synthesized in a two-step procedure starting from (4S)-4-benzyl-3-propionyl-1,3-oxazolidin-2-one. In the first step, (4S)-4-benzyl-3-propionyl-1,3-oxazolidin-2-one was treated with DIBAL-H at −78 °C in DCM and then converted to (4S)-4-benzyl-3-(1-trimethylsilyloxy)propyl-1,3-oxazolidin-2-one with TMSOTf at −78 °C in DCM in the presence of pyridine. Next, the silylated intermediate was transformed to the expected 1-[(4S)-4-benzyl-1,3-oxazolidin-2-one-3-yl]propyltriphenylphosphonium tetrafluoroborate by reaction with Ph₃P·HBF₄ at −40 °C in DCM. The structures of the compounds obtained were confirmed by spectroscopic methods (¹H-, ¹³C{¹H}-, ³¹P{¹H}-NMR, IR) and HRMS analysis. Full article

Diabetic cardiomyopathy (DCM) is a cardiac condition characterized by various structural and functional abnormalities that are associated with diabetes mellitus. Its development involves multiple factors, among which endothelial dysfunction plays a significant role. To fully describe the relationship between DCM and endothelial dysfunction, we conducted a literature search across several databases, including PubMed, Web of Science, and EMBASE. Our review of DCM research focused on understanding its mechanisms, exploring the methods used to diagnose it, and potential treatment options. Although researchers have made significant progress in DCM diagnosis and treatment in recent years, considerable challenges persist as well. Artificial intelligence (AI)-based multimodal approaches may provide new opportunities for cardiovascular risk stratification and early DCM screening, but DCM-specific models still require external validation before clinical implementation. Regarding treatment options, emerging evidence suggests potential benefits of medications that enhance mitochondrial function and antioxidant properties, as well as anti-inflammatory therapies and lifestyle modifications. Future research should focus on combining different relevant data types, such as genetic and molecular information, to improve the AI tools utilized in medical environments. This study aims to identify novel markers for diagnosing DCM and to devise tailored treatment approaches. These advancements have the potential to improve the prognosis of DCM patients and enhance the identification and management of this condition. Full article

Background: Atrial stiffness can be estimated non-invasively using speckle-tracking echocardiography (STE) and has recently emerged as an outcome predictor. We aimed to assess left atrial (LA) and right atrial (RA) phasic function; the LA stiffness index (LASI), the RA stiffness index (RASI) and their sum; and the biatrial stiffness index (BASI) in dilated cardiomyopathy (DCM), and to test whether combining the two atria adds prognostic information over either index alone. Methods: A total of 121 patients with non-ischaemic DCM in sinus rhythm were followed prospectively for a composite endpoint of all-cause death, non-fatal cardiac arrest, or hospitalisation for heart failure decompensation. LASI was defined as the mitral E/e′ ratio divided by LA reservoir strain, RASI as the tricuspid Et/e′t ratio divided by RA reservoir strain, and BASI as the sum of the two. Cox models were adjusted for NYHA class, LV ejection fraction (LVEF), maximal LA volume (LAVmax) and pulmonary artery systolic pressure (PASP). Results: After 19 ± 11 months, 55 patients reached the endpoint. LA reservoir and contraction strain, all three components of RA strain and all three stiffness indices were significantly impaired in patients with events. All stiffness indices were independent outcome predictors in multivariable Cox regression (HR 2.79 [95% CI, 1.35–5.75], p = 0.006 for LASI, HR 1.84 [95% CI, 1.02–3.29], p = 0.04 for RASI and HR 2.74 [95% CI, 1.36–5.51], p = 0.005 for BASI). BASI showed the greatest increase in risk prediction (Δ likelihood ratio χ² test = 10.3, p = 0.001) over NYHA class, left ventricular ejection fraction, LA maximal volume and pulmonary artery systolic pressure. BASI showed the highest discrimination (AUC = 0.73); however, it was not significantly better than LASI or RASI alone. Conclusions: Left and right atrial stiffness are both associated with adverse outcome in DCM and add prognostic information to an LV-centred risk model. Their unweighted sum performs at least as well as either component and offers a single parsimonious measure, whose incremental clinical value remains to be established in larger, externally validated cohorts. Full article

Monocular depth estimation plays a crucial role in applications such as autonomous driving and mobile 3D reconstruction. However, existing lightweight methods are often constrained by limited computational resources and rely on shallow feature representations for direct depth regression. As a result, cross-scale residual information is insufficiently modeled, which limits their ability to preserve structural consistency and recover fine-grained details in complex scenes. To address these challenges, we propose LapCR-Net, a lightweight monocular depth estimation network based on Laplacian residual reconstruction. Specifically, we formulate a progressive Laplacian residual framework that decomposes depth prediction into a coarse-to-fine multi-scale refinement process. To enhance feature representation in the decoder, we introduce a Structure-aware Feature Recalibration (SFR) module and a Depth-guided Convolution Module (DCM), which strengthen spatial semantic correlations and improve residual prediction across scales. Furthermore, we design an uncertainty-driven collaborative refinement strategy to adaptively adjust residual correction strength. By estimating prediction uncertainty, the proposed strategy sharpens object boundaries while suppressing texture artifacts. Extensive experiments on the NYU-Depth V2 and KITTI benchmarks demonstrate that LapCR-Net achieves competitive performance with only 5.4 M parameters. In particular, it shows clear advantages in structural preservation and detail reconstruction, achieving a favorable trade-off between accuracy and computational efficiency. Full article

Multi-label remote sensing scene classification requires identifying multiple land-cover categories from a single high-resolution aerial image. Existing methods strengthen visual features or model label dependencies, yet they apply a fixed calibration strategy regardless of the underlying label-density regime, leading to over-prediction on dense scenes or under-correction on sparse scenes. We propose Density-Adaptive CDG Calibration (DARC), a lightweight framework that explicitly conditions calibration on dataset label density. DARC comprises three modules: (1) Label-density Driven Profile Selection (DDP) automatically routes the calibration path based on training-set density statistics; (2) Label-token Correlative-Discriminative Graph Mixing (CDM) injects both co-occurrence and exclusivity relations into label semantic tokens through positive and negative graph propagation; (3) Density-aware Gated Calibration (DCM) applies cardinality-controlled gating for dense labels and EMA-stabilized graph calibration for sparse labels. Experiments on AID-ML and UCM-ML demonstrate that DARC achieves 90.12% and 89.05% sample-F1, respectively, outperforming six competitive baselines including SFIN, ASL, C-Tran, ML-Decoder, SPIN, and Two-Way Loss by 1.65–3.87%, while introducing only 1.42% additional parameters. Cross-regime routing analysis confirms that no single fixed strategy matches DARC’s adaptive approach, and sensitivity analysis shows the routing is robust across a wide threshold range. Ablation studies validate the necessity of each component, and visualization analyses demonstrate that the learned label graphs capture interpretable semantic patterns. Full article

Dynamic risk prediction is an important statistical technique for detecting temporal changes in risk and provides quantitative support for early risk identification in clinical decision-making, industrial process monitoring, and financial anomaly detection. This study proposes a Temporal Convolutional Network Deep Cox Mixtures Model (TCN-DCM) for longitudinal survival data by integrating a temporal convolutional network, which learns temporal patterns from longitudinal covariates, with a Deep Cox Mixtures framework that relaxes the conventional proportional hazards assumption. Simulation studies were conducted to compare the proposed model with existing deep learning-based methods, including recurrent deep survival machines and Dynamic-DeepHit, as well as the traditional joint model. The results showed that, when the proportional hazards assumption held, TCN-DCM outperformed the existing deep learning-based models. When the proportional hazards assumption was violated, TCN-DCM achieved predictive performance comparable to that of recurrent deep survival machines and yielded superior results for some evaluation metrics. The proposed model was further applied to a primary biliary cholangitis dataset, where it achieved the best overall predictive performance and demonstrated individualized dynamic survival risk prediction. These findings indicate that TCN-DCM provides a flexible and broadly applicable approach for dynamic risk prediction in longitudinal survival analysis. Full article

Background/Objectives: Dilated cardiomyopathy (DCM) is a genetically heterogeneous myocardial disorder. Emerging evidence suggests that some genes implicated in DCM may also be associated with neurological disorders, supporting the concept of genetic pleiotropy. This study explored the intersection between cardiac and neurological genetics, with the aim of identifying candidate genes that may contribute to shared pathogenic pathways linking these clinically distinct conditions. Methods: We performed exome sequencing in 149 patients with echocardiographically confirmed DCM and subsequently applied an in silico filter to a predefined list of 211 genes associated with inherited cardiomyopathies. Variants were classified according to the American College of Medical Genetics and Genomics (ACMG) criteria. Genes with validated evidence for DCM according to the Clinical Genome Resource (ClinGen) were further investigated through the Human Gene Mutation Database (HGMD) and a focused literature review to identify reported associations with neurological disorders. Results: Genetic variants in DCM-associated genes were identified in 105 patients. Overall, 137 variants were detected, including pathogenic variants and variants of uncertain significance. The most frequently involved genes were TTN, FLNC, and MYH6. Several DCM-associated genes also showed reported associations with neurological disorders, including autism spectrum disorder, Alzheimer’s disease, Parkinson’s disease, epilepsy, and schizophrenia. Among them, TTN, FLNC, RYR2, and SCN5A displayed the broadest overlap between cardiac and neurological phenotypes. Conclusions: These descriptive findings show that variants were most frequently observed in TTN, FLNC, and MYH6 and that several genes included in the ClinGen DCM curation framework have also been independently reported in neurological disorders. Because most identified variants were VUS and no control group or systematic neurological phenotyping was available, the findings indicate gene-level co-annotation only and do not establish variant enrichment, shared pathogenic mechanisms, or clinical overlap. Full article

Reliable medical image segmentation remains challenging because models must preserve fine boundary details while maintaining global semantic consistency. CNNs capture local structures effectively but have limited long-range modeling ability, whereas Transformer-based methods improve global context at high computational cost. Mamba-based state space models offer efficient long-range modeling, but may weaken high-frequency textures and boundary cues. To address these limitations, we propose DIG-MambaNet, a Dual-path Interactive Guided Mamba Network for medical image segmentation. The network introduces a dual-path complementary modeling block (DCM Block), where a cross-feature spatial interaction module (CSIM) adaptively integrates CNN-based local features and Mamba-based global features. A source image-guided module (SIGM) injects high-frequency information from the original image to compensate for downsampling-induced detail loss, while an inter-layer detail refinement fusion module (IDRFM) improves encoder–decoder feature alignment during reconstruction. Experiments on 2018DSB, ISIC2018, JSUAH-Cerebellum, and CVC-ClinicDB, covering nuclei segmentation in microscopy images, skin lesion segmentation in dermoscopic images, fetal cerebellum segmentation in ultrasound images, and polyp segmentation in colonoscopy images, demonstrate that DIG-MambaNet achieves consistent and competitive performance across diverse target structures and imaging conditions, with improved boundary delineation and favorable overlap-based accuracy compared with representative CNN-, Transformer-, and Mamba-based methods. Full article

Background: Pediatric dilated cardiomyopathy (DCM) is a genetically heterogeneous disorder with limited population-specific genetic data in Middle Eastern cohorts. Methods: Twenty-three pediatric DCM patients from a predominantly Kurdish–Arab consanguineous population underwent comprehensive clinical evaluation and targeted next-generation sequencing (NGS) using a 152-gene cardiomyopathy panel. Variants were classified according to ACMG/AMP guidelines. Results: Targeted NGS identified 163 rare variants; 24 (14.7%) were pathogenic/likely pathogenic (P/LP), 76 (46.6%) were variants of uncertain significance (VUS), and 63 (38.7%) were benign/likely benign. The overall diagnostic yield was 78.3% (18/23). The SCN5A c.1921delC (p.Gln641ArgfsTer3) frameshift variant was the most frequently detected P/LP variant, present in 69.6% of patients. No significant genotype–phenotype correlations were observed for SCN5A c.1921delC regarding age of onset, left ventricular ejection fraction, or valvular/structural abnormalities. Parental consanguinity was high (66.7%), and disease onset occurred early (mean 2.0 ± 3.27 years). Conclusions: This is the first genetic evaluation of a pediatric cohort with DCM from a mixed Kurdish–Arab population. A high diagnostic yield of DCM was found, particularly in consanguineous families, including an SCN5A recurrent variant. The study highlights the utility of targeted NGS in a consanguineous population. Further related studies are required in this region. Full article

Background: Diabetic cardiomyopathy (DCM) is characterized by myocardial inflammation, apoptosis, and fibrosis that contribute to ventricular remodeling and dysfunction. We investigated the effects of 5-methoxytryptophan (5-MTP) on histopathological and molecular markers of myocardial remodeling in a rat model of DCM. Methods: Forty-eight Sprague–Dawley rats with DCM induced by a high-fat high-fructose diet and low-dose streptozotocin (25 mg/kg) were randomized to control or 5-MTP treatment (25, 50, or 100 mg/kg) and evaluated after 8, 16, and 32 days. Histopathological assessment using hematoxylin–eosin and Masson’s trichrome staining, along with immunohistochemical analysis of inflammatory, apoptotic, and fibrotic markers, was performed. Results: Myocardial inflammatory histopathological scores did not differ significantly among groups. Myocardial fibrosis assessed by Masson’s trichrome staining was significantly reduced at day 16 (p = 0.020), with all 5-MTP doses demonstrating lower fibrosis scores than DCM controls. Collagen I expression did not differ significantly. Caspase-3 expression was significantly reduced in all treatment groups at day 16 (p = 0.029), with persistent reduction at day 32 only in the 100 mg/kg group (p = 0.004). Early molecular modulation was observed through reduced TGF-β expression at day 8 in the 25 mg/kg and 50 mg/kg groups, followed by reduced SMAD3 expression at day 16 in the 25 mg/kg and 100 mg/kg groups. At day 16, AKT expression increased in the 25 mg/kg group, while cytoplasmic NF-κB expression decreased in the 25 mg/kg and 100 mg/kg groups. Conclusion: 5-MTP demonstrated time-dependent changes in molecular and histopathological markers associated with myocardial remodeling in experimental DCM. Full article

Background/Objectives: Degenerative cervical myelopathy (DCM) is the leading cause of non-traumatic spinal cord dysfunction in adults. Both anterior and posterior surgical approaches are widely used for its treatment, yet their comparative impact on clinical outcomes and complication profiles remains clinically relevant. This study aimed to describe early and one-year postoperative outcomes between anterior and posterior surgical strategies in a consecutive single-center cohort. Methods: We conducted a retrospective single-center study of 102 consecutive adult patients surgically treated for DCM between 2013 and 2024, with complete datasets and a minimum one-year follow-up. Surgical approaches were classified as anterior, posterior, or combined. Clinical outcomes were assessed using the modified Japanese Orthopaedic Association scale (mJOA), Neck Disability Index (NDI), and Numeric Rating Scale for arm pain (NRS-arm) preoperatively, at 4–6 weeks and at one year. Complications were systematically recorded and stratified by approach. Nonparametric tests were used for all comparisons (significance threshold p < 0.05). Results: Anterior procedures were performed in 82 patients (80.4%), posterior in 18 (17.6%), and combined in 2 (2.0%). No significant between-group differences were observed in neurological, functional, or pain outcomes at either time point (ΔmJOA p = 0.268; ΔNDI p = 0.632; ΔNRS p = 0.562 at one year). Complications occurred in 13 patients (12.7%), with approach-specific profiles: anterior surgery was associated with hematoma, dysphagia, and dysphonia, posterior surgery with CSF leak, wound infection, and kyphosis. No C5 palsy was recorded. Conclusions: Both anterior and posterior surgical approaches were followed by neurological and functional improvement at one year. Given the descriptive nature of the study and the baseline differences between groups, these findings should not be read as a formal comparison of effectiveness, but they reinforce the importance of individualized, pathology-driven surgical planning in DCM. Full article

Background/Objectives: Dilated cardiomyopathy is a heterogeneous disorder with a substantial genetic contribution from a variety of pathogenic variants. Hereditary isolated DCM is often caused by variants in genes encoding sarcomere proteins, as well as proteins involved in desmosomes or other cardiac cell functions. Identifying genetic causes improves our understanding of DCM pathophysiology, facilitates prognostic assessment, and enables more personalized disease management. Methods: We retrospectively analyzed genetic data from adult patients with a clinical diagnosis of isolated DCM evaluated at a Lithuanian tertiary university hospital between 2019 and 2024. All patients were tested with a next-generation sequencing cardiovascular gene panel. Results: We gathered 169 patients and initially reached a 16.0% (n = 27) genetic testing diagnostic yield. We performed all genetic variant reanalyses with the most current classification guidelines, and we found an additional eight positive cases. Our final diagnostic yield was 20.7% (n = 35). TTN was the most frequently affected gene (n = 30), whereas variants in BAG3 (n = 2), DSP (n = 1), LMNA (n = 1), and FLNC (n = 1) were rare. In total, 15 variants were novel—not described in the literature or databases. We did not observe significant clinical differences between patients with pathogenic variants and those without pathogenic variants. We expected a different clinical course with variants in genes like BAG3 or LMNA, but there were only a few cases. Conclusions: Genetic testing remains an important tool for confirming complex DCM cases and allows earlier disease management for relatives at risk. Full article

Background: Diabetic cardiomyopathy (DCM) is characterized by metabolic dysfunction, inflammation, extracellular matrix (ECM) remodeling, and myocardial fibrosis. Increasing evidence suggests that ferroptosis-associated oxidative injury may contribute to cardiac remodeling; however, the interaction between ferroptosis-related pathways and fibrosis-associated molecular networks remains incompletely understood. This study explored the ferroptosis–fibrosis axis using an integrative transcriptomic and systems pharmacology framework. Methods: Differentially expressed genes were identified from the GSE5406 myocardial transcriptomic dataset comparing nonfailing donor hearts with ischemic and idiopathic cardiomyopathy samples and analyzed using functional enrichment, protein–protein interaction, and disease-association approaches. Cross-dataset comparison and exploratory sample-level external evaluation were performed using the independent GSE263297 DCM-related dataset. Candidate genes were further evaluated by receiver operating characteristic (ROC) analysis and machine learning-based feature selection using least absolute shrinkage and selection operator (LASSO), random forest, and support vector machine-recursive feature elimination (SVM-RFE). Representative compounds associated with fibrosis-, oxidative stress-, inflammation-, and ferroptosis-related pathways were subsequently assessed by molecular docking against TGFBR1, STAT3, GPX4, AKT1, SMAD3, and ACSL4. Results: Transcriptomic analyses highlighted ECM organization, collagen-containing ECM, and fibrosis-related pathways as dominant biological themes. Cross-dataset comparison showed partial preservation of transcriptional patterns between independent myocardial cohorts, with 20 of 51 evaluated genes demonstrating concordant expression direction across datasets. ROC analysis identified LUM and ASPN as having the highest area under the curve (AUC) values among candidate genes, whereas COL1A1, COL1A2, and COL3A1 also showed elevated AUC values. Machine learning analyses identified FCN3, HOPX, CNN1, and GLUL as the core signature consistently prioritized across all three algorithms, whereas LUM was additionally identified by two of three algorithms. Internal validation yielded a cross-validated AUC of 0.934 (95% CI: 0.820–1.000), and exploratory sample-level external evaluation of the four-gene signature in GSE263297 yielded an AUC of 0.673 (95% CI: 0.380–0.967). Exploratory docking analyses suggested potential structural compatibility between several candidate compounds and fibrosis-, inflammation-, and ferroptosis-associated targets, with comparatively lower predicted binding-energy values observed for selected ligand–target combinations. Conclusions: The findings are consistent with a fibrosis-dominant remodeling signature and suggest potential network-level links between ferroptosis-associated processes and cardiac fibrosis. These observations should be regarded as exploratory and hypothesis-generating and require validation in independent cohorts and experimental studies. Full article