Search Results (742)

Millimeter-wave radar enables non-contact monitoring of cardiac activity and therefore has the potential to reconstruct electrocardiogram signals without surface electrodes. However, existing radar-based electrocardiogram reconstruction methods still suffer from incomplete extraction of heartbeat-related information and insufficient modeling of electrocardiogram-related features, which limits reconstruction accuracy. To address these issues, this study proposes a millimeter-wave radar-based electrocardiogram reconstruction method that integrates a respiratory-harmonic-suppressed multi-channel signal-processing frontend with the proposed CA-WTBNet deep reconstruction network. First, based on maximal overlap discrete wavelet transform-based multi-resolution analysis, respiratory harmonics mixed into heartbeat-related components are suppressed by combining respiratory harmonic detection with a heart-rate frequency protection strategy, while cardiac-related information is preserved as much as possible. A multi-channel input representation is then constructed. Meanwhile, the proposed deep reconstruction network is developed to jointly model complementary channel-wise features, local waveform morphology, and temporal dependencies by integrating channel-attention mechanisms, convolutional residual modules, window-based Transformer blocks, and bidirectional long short-term memory. Experiments conducted on the public dataset show that our method achieves an average Pearson correlation coefficient of 0.9641, a mean normalized root mean square error of 0.0458, an average R-peak F1 score of 0.9956, and an average R-peak timing error of 3.13 ms on the test set. In comparison with related studies on the same public Resting dataset, the proposed method achieves the best overall performance among the compared methods, with a 0.53% improvement in Pearson correlation coefficient and a 10.20% reduction in normalized root mean square error over the best-performing compared method. Full article

Background/Objectives: Spontaneous intracranial hypotension (SIH) is caused by spinal cerebrospinal fluid (CSF) leakage and is typically diagnosed by clinical presentation and characteristic MRI signs; however, objective tools for monitoring physiological changes and treatment response remain limited. Cine phase-contrast MRI (PC-MRI) enables noninvasive quantification of aqueductal CSF dynamics, yet reliable analysis is challenging since the cerebral aqueduct is extremely small and susceptible to low contrast, partial volume effects, and ROI-dependent measurement variability—particularly in SIH where CSF pulsatility is often reduced. Methods: We propose an end-to-end automated framework that integrates (1) a cascade localization–segmentation strategy, consisting of Tiny YOLOv4 detection followed by MultiResUNet segmentation on a YOLOv4-derived cropped ROI; (2) physiology-informed pulsatility-based segmentation (PUBS) to refine anatomical masks into functional flow ROIs; and (3) one-dimensional convolutional neural networks (1D-CNNs) to extract exploratory waveform morphology features from 32-phase cardiac-cycle velocity waveforms. The study includes 39 participants, yielding 59 cine PC-MRI examinations: 11 controls, 28 Pre-treatment SIH scans and 20 Post-treatment Recovery scans. Results: The cascade model significantly improves segmentation robustness compared with a full-image baseline, achieving higher Dice scores and markedly lower boundary errors across cohorts (e.g., Pre-treatment SIH HD95: 1.66 ± 0.74 px vs. 15.37 ± 44.98 px). PUBS refinement reduces quantification deviation from expert manual references in SIH (mean relative error: 7.4% to 5.6%) and improves diagnostic performance for multiple hemodynamic parameters (e.g., downward mean flow AUC: 0.747 to 0.792). For waveform morphology analysis, the end-to-end 1D-CNN classifier was evaluated using repeated-seed participant-level grouped LOOCV. The repeated-seed ensemble prediction showed modest out-of-sample discrimination between Normal controls and Pre-treatment SIH scans, with an AUC of 0.646, a bootstrap 95% confidence interval of 0.455–0.826, and a permutation-test p-value of 0.072. Separately, exploratory analysis of the final baseline-trained 1D-CNN latent space showed marked, apparent Normal-versus-SIH separability and an intermediate recovery distribution in PCA space, suggesting that aqueductal waveform morphology may encode SIH-related physiological information. Conclusions: These findings suggest that SIH-related information may be reflected not only in flow magnitude but also in aqueductal CSF waveform morphology. However, the modest and statistically non-significant out-of-sample performance of the end-to-end 1D-CNN classifier indicates that morphology-based AI features should currently be regarded as exploratory biomarker candidates rather than validated stand-alone diagnostic tools. Larger independent cohorts are required to confirm their reproducibility, physiological meaning, and clinical utility. Full article

Background/Objectives: Left ventricular (LV) geometry reflects structural adaptation to aging and biological sex. While cardiac magnetic resonance (CMR) provides precise morphologic assessment, most prior studies have focused on volumetric and mass-based parameters rather than routinely reported linear indices. This study aimed to evaluate the influence of age and sex on LV geometry using wall thickness, LV end-diastolic diameter (LVEDD), and proportional indices derived from standard CMR reports. Methods: In this retrospective cross-sectional study, 95 adult patients who underwent clinically indicated CMR were included. LV wall thickness, LVEDD, relative wall thickness (RWT), and wall thickness-to- LVEDD ratio (WT/LVEDD) were recorded. Participants were stratified by sex and age groups (18–40, 41–60, >60 years). Group comparisons, correlation analysis, multivariable linear regression, logistic regression, and Age × Sex interaction testing were performed to evaluate independent associated parameters of LV morphology and concentric remodeling. Results: The mean age was 34.94 ± 16.00 years; 60.0% were male. Males had significantly larger LVED (43.12 ± 6.83 mm vs. 39.76 ± 6.11 mm, p = 0.014) and greater wall thickness measurements (p < 0.05 for septal and posterior wall thickness). Age showed a significant positive correlation with mean LV wall thickness (r = 0.275, p = 0.007) and WT/LVEDD ratio (r = 0.241, p = 0.019), but not with LVEDD (p = 0.414). In multivariable analysis, male sex was independently associated with larger LVED (B = 3.345, p = 0.017), whereas age was independently associated with WT/LVEDD ratio (B = 0.0018, p = 0.019). Logistic regression demonstrated that age independently increased the odds of concentric remodeling (OR = 1.041 per year, 95% CI: 1.011–1.072, p = 0.006). No significant Age × Sex interaction was observed. Conclusions: Advancing age was independently associated with proportional LV geometric remodeling, whereas male sex primarily influenced absolute ventricular dimensions. Routine CMR report-derived linear measurements were sufficient to detect these distinct structural patterns. These findings highlighted the feasibility of using standardized morphologic indices in daily clinical practice to identify early age-related concentric remodeling. Full article

Background and Objectives: Premature ventricular contractions are among the most common arrhythmias encountered in clinical practice. However, this disorder can be associated with arrhythmia-induced cardiomyopathy or be the first sign of primary myocardial diseases. Certain morphologies of premature ventricular contractions are associated with a higher risk for sudden arrhythmia and cardiac dysfunction in the adult population. There is data on the clinical value and significance of the contraction morphology in adults, but there is a lack of such data for children. Materials and Methods: This observational prospective study of pediatric outpatients with premature ventricular contractions was conducted at Vilnius University Hospital Santaros Clinics. Inclusion criteria comprised children aged 3–17 years with more than 5% premature ventricular contractions over 24 h. Exclusion criteria included previously diagnosed congenital heart defects and cardiomyopathies, channelopathies, or the presence of any acute condition. The electrocardiographic morphology and measurements were assessed, analyzed, and described in this study. Results: The electrocardiograms of 80 patients were analyzed according to the ECG-estimated morphology of the arrhythmia complex, arrhythmic QRS complex duration, ratio with the normal QRS complex, and maximum deflection index in V5–V6 derivations. Cardiac MRI abnormalities (8 of 30 MRI studies) was reliably associated with a PVC duration of >150 ms and the maximal amount of extrasystoles per 24 h, with a median amount of 29.6%. A long postcoupling interval (>0.9 s) was associated with PVC progression. Conclusions: In this exploratory pediatric cohort, wider PVC QRS duration and higher maximal PVC burden were associated with ventricular MRI abnormalities, while longer postcoupling interval was associated with PVC progression. Full article

This study evaluated the effects of dietary coenzyme Q10 (CoQ10) supplementation on growth performance, slaughter performance, meat quality, antioxidant capacity, serum profiles, and intestinal morphology in Minxinan black rabbits. A total of 250 rabbits were allocated to five dietary treatments containing 0, 30, 60, 120, or 240 mg/kg CoQ10 for 14 weeks after a 1-week adaptation period. Results indicated that supplementation with 60 mg/kg CoQ10 resulted in the highest final body weight (2.83 kg) and average daily gain (29.54 g/day), with a significantly reduced feed-to-gain ratio and mortality rate compared to the control group. Regarding slaughter performance, the 60 mg/kg group significantly reduced the abdominal fat rate. In terms of meat quality, the 60 and 120 mg/kg groups showed significantly reduced drip loss and shear force, while meat lightness (L*) increased in all supplemented groups. Cooking loss was significantly reduced in the 60 mg/kg group. Antioxidant capacity in cardiac muscle and longissimus thoracis et lumborum (LTL) muscle was enhanced, particularly at 60 mg/kg, with significantly elevated activities of superoxide dismutase (SOD), catalase (CAT), glutathione peroxidase (GSH-Px), and total antioxidant capacity (T-AOC), alongside reduced malondialdehyde (MDA) content. Furthermore, the 60 mg/kg group increased LTL muscle polyunsaturated fatty acid (PUFA) content, elevated serum levels of triiodothyronine (T3), growth hormone (GH), and insulin-like growth factor-1 (IGF-1), enhanced immunoglobulin concentrations, and improved intestinal morphology. In conclusion, dietary supplementation with 60 mg/kg CoQ10 improved growth performance, carcass leanness, PUFA content, and antioxidant status in broiler rabbits. Full article

Electrocardiogram (ECG) analysis is a cornerstone non-invasive diagnostic technique for detecting cardiac arrhythmias, which remain a leading cause of mortality worldwide. While recent advances in deep learning have significantly improved automated arrhythmia classification, the current literature lacks systematic, fair comparisons of fundamental neural architectures under unified experimental conditions, and very few studies provide model interpretability. This study addresses these gaps by first providing a rigorous comparative analysis of three representative architectures—Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and Residual Network (ResNet)—on the MIT-BIH Arrhythmia Database under identical preprocessing, training, and evaluation protocols. We then propose an efficient Fine-Tuned CNN (FT-CNN) optimized for ECG signal characteristics through adaptive kernel sizing for P-QRS-T morphological extraction, multi-faceted regularization including L2, dropout, and batch normalization, cosine annealing learning rate, and a custom loss function combining weighted categorical cross-entropy with focal loss with gamma equal to 2.0 to address severe class imbalance. The FT-CNN achieves an accuracy of 98.51%, outperforming fourteen benchmark models, including standard CNN with an accuracy of 97.20%, ResNet with 96.88%, LSTM with 96.50%, GRU with 96.30%, and traditional classifiers. Comprehensive ablation studies confirm an improvement of 6.17% over the baseline. Class-wise analysis reveals excellent performance for normal beats with an F1-score of 0.99, ventricular ectopic beats with 0.95, and unknown beats with 0.98, while supraventricular ectopic beats with an F1-score of 0.79 and fusion beats with 0.70 remain challenging. Unlike most prior works, we integrate Grad-CAM and Integrated Gradients for explainability, quantitatively evaluating attribution faithfulness, sanity checks, and noise robustness. Full article

Connexin-43 (CX43) lateralization in ventricular myocardium has been associated with abnormal impulse propagation and increased arrhythmia susceptibility. Its quantitative assessment in histological sections remains challenging because previous methods require segmentation of individual cardiomyocytes and rely on geometric rules applied to segmented cell profiles. Here, we present CLARISA, a segmentation-free, ROI-based deep learning framework that classifies CX43-positive regions as terminal or lateralized directly from fluorescence images. An expert-annotated dataset was generated from left-ventricular cryosections of Wistar rat hearts, in which CX43-positive regions were labeled according to their distribution pattern. A dual-stream EfficientNetV2-S classifier was trained to capture both local and contextual ROI morphology. We also developed a semi-automated whole-section inference module to generate spatial lateralization probability maps and global percent lateralization estimates. On the held-out test set, CLARISA achieved a ROC-AUC of 0.904 (95% bootstrap CI: 0.828–0.960) and a PR-AUC of 0.808 (95% bootstrap CI: 0.682–0.913), supporting the feasibility of automated ROI classification for CX43 lateralization assessment. When deployed on whole tissue sections, including an independently analyzed section not used during model development, CLARISA generated spatial maps that captured heterogeneous CX43 organization and produced a global percent lateralization estimate closely aligned with expert annotation, differing by only 1.30 percentage points over the same detected CX43-positive area. Comparison with a previously published segmentation-based method further indicated that ROI-based and cell-segmentation-based approaches provide related but non-equivalent readouts of CX43 lateralization. The ROI-based design additionally reduces annotation burden—requiring classification of discrete CX43-positive signal rather than complex cardiomyocyte delineation—and ensures that all detected CX43-positive signal contributes to the lateralization estimate regardless of cell boundaries. These results establish CLARISA as a proof-of-principle framework for scalable, segmentation-free CX43 lateralization assessment in cardiac tissue. Further validation across larger, independent, and more heterogeneous datasets will be required to assess robustness, portability across imaging conditions, and translational applicability. The complete codebase, pretrained model, image data, and expert annotation tool are publicly available. Full article

Background: The Ejection Fraction (EF) represents a fundamental pillar for the phenotypic classification and clinical management of cardiovascular diseases. Although trans-thoracic echocardiography (TTE) acts as the first-line examination and cardiac magnetic resonance (CMR) is the reference gold standard, cardiac computed tomography (CCT) has undergone a technological evolution. The advent of wide-detector scanners and artificial intelligence (AI) models has enabled CCT to transition from a purely morphological tool to a modality capable of comprehensive, three-dimensional morpho-functional assessments. Methods: This narrative review evaluates the literature across Scopus, MEDLINE, and Web of Science regarding the calculation of biventricular function and EF using CCT. It provides an updated summary of current clinical applications, technological advancements, and comparative diagnostic reliability against TTE and CMR. Results: The CCT “one-stop-shop” concept allows for the simultaneous acquisition of anatomical data and systolic function metrics (EDV, ESV, SV, EF), optimizing clinical workflows at no additional cost. Being intrinsically three-dimensional, CCT bypasses the geometric assumptions and apical foreshortening artifacts typical of 2D-TTE, demonstrating high volumetric concordance with CMR. Nevertheless, structural limitations persist, primarily regarding ionizing radiation exposure, contrast media toxicity, dependence on heart rhythm stability, and lower temporal resolution compared to CMR. Conclusions: EF determination via CCT has achieved technical maturity and clinical validation. While it does not intend to replace TTE or CMR, it offers synergistic data when integrated with primary anatomical indications. Furthermore, AI integration has been shown to potentially automate this workflow, transforming CCT into an opportunistic screening tool for subclinical cardiac dysfunction. Full article

Pregnancy-associated hemodynamic overload and hormonal changes induce hypertrophy and metabolic remodeling of the maternal heart. Mitochondrial motility, mediated by ras homolog family member T (RHOT) 1 and RHOT2, is essential for cardiac adaptation to increased workload, cardiomyocyte hypertrophy, and sarcomere maturation. To test the hypothesis that Rhot1/2 expression is required for pregnancy- and postpartum-associated adaptations of the maternal heart, female mice with tamoxifen-inducible, cardiomyocyte-selective deletion of Rhot1 and Rhot2 (iRhot1/2-KO) were mated. Following gene deletion in adult mice, cardiac tissue and function were analyzed after three to five successive pregnancies and postpartum nursing periods. Age-matched nulliparous iRhot1/2-KO mice and age-matched mice expressing Rhot1 and Rhot2 served as controls. Motility of mitochondria isolated from iRhot1/2-KO hearts was impaired, as determined by the number of mobile mitochondria in an in vitro motor protein-driven single mitochondrion motility assay performed on surface-immobilized microtubules. Despite loss of Rhot1/2 expression, contractile function assessed by transthoracic echocardiography, mRNA expression of peripartum-associated heart failure markers, cardiac structure, mitochondrial morphology, mitochondrial enzymatic activity, and mitochondrial DNA content were all comparable to controls expressing Rhot1/2 at the investigated time points. RNA sequencing-based gene profiling identified a transcriptional program through which RHOT proteins preserve cardiac energetic and contraction gene expression during pregnancy and postpartum. Together, cardiomyocyte-selective loss of Rhot1/2 expression in the adult heart does not cause peripartum-associated heart failure, despite reduced cardiac energetic and contraction gene expression. Full article

Background/Objectives: Chromium picolinate [Cr(pic)₃] supplementation and strength training (ST) have been proposed as strategies to improve metabolic health in obesity; however, their combined effects on cardiac cellular function remain unclear. This study evaluated the impact of Cr(pic)₃ supplementation associated with ST on body composition, metabolic parameters, cardiac morphology, and cardiomyocyte contractile function in diet-induced obese rats. Methods: Male Wistar rats were fed a high-fat diet and allocated into four groups for 8 weeks: obese sedentary (Ob), obese + ST (ObST), obese + Cr(pic)₃ (ObCr(pic)₃), and obese + ST + Cr(pic)₃ (ObSTCr(pic)₃). Chromium picolinate (80 μg/kg/day) was administered by gavage, and ST was performed using a ladder-climbing protocol three times per week. Nutritional, metabolic, cardiac morphological, and isolated cardiomyocyte contractile parameters were assessed. A significance level of 5% was set for all tests. Results: Neither ST nor Cr(pic)₃, alone or combined, modified adiposity index, glucose tolerance, insulin resistance, lipid profile (except HDL), or cardiac morphology. ST improved maximal load capacity in trained groups, confirming protocol efficacy. HDL levels were higher in the combined intervention group compared with obese sedentary rats. Cardiomyocyte fractional shortening and maximal contraction and relaxation velocities were unchanged among groups. However, the association of ST and Cr(pic)₃ resulted in prolonged time to 50% relaxation, indicating delayed relaxation kinetics without alterations in contractile performance. Conclusions: These findings suggest that Cr(pic)₃ supplementation does not enhance metabolic adaptations to ST and may adversely affect cardiomyocyte relaxation dynamics in obesity. Full article

Heart failure (HF) remains a major global health challenge, necessitating accurate yet accessible diagnostic tools. While the left ventricular ejection fraction (LVEF) is the primary metric for classifying HF into preserved (HFpEF), mid-range (HFmrEF), and reduced (HFrEF) phenotypes, conventional imaging modalities such as echocardiography are resource intensive. In contrast, the electrocardiogram (ECG) offers a low-cost, non-invasive alternative for continuous cardiac assessment. This paper proposes a multi-algorithm artificial intelligence (AI) framework for automated HF phenotype classification using high-resolution ECG signals from 303 patients with chronic heart failure from the MUSIC cohort. After preprocessing (normalization, bandpass filtering), we employed a hybrid approach combining the Pan–Tompkins algorithm for robust R-peak detection with the NeuroKit2 toolbox for the precise delineation of P, Q, S, and T waves. ECG recordings were then segmented using an adaptive beat-centric windowing strategy. From the segmented beats, we extracted a comprehensive set of temporal, morphological, and energy-based features, including RR, QRS, and QT intervals, along with P-wave, QRS-complex, and T-wave energies. These features were used to train and evaluate several ensemble machine learning models—Random Forest, XGBoost, CatBoost, LightGBM, and a stacking classifier—using a stratified 70–15–15 train–validation–test split with 5-fold cross-validation. The LightGBM model achieved the highest performance with a test accuracy of 98.45%, an AUC of 0.9989, and a macro F1-score of 0.9804, outperforming other ensembles and the stacking classifier. The results demonstrate that an AI-driven analysis of ECG-derived morpho-energy features can serve as a reliable, non-invasive screening tool for the accurate and early discrimination of HF phenotypes, potentially supporting clinical decision making and improving patient management in resource-limited settings. Full article

Introduction: Accurate detection and characterization of intracardiac masses remain a major challenge in cardiovascular imaging due to overlapping morphological features between tumors, thrombi, and vegetations, as well as the inherent limitations of echocardiography, including operator dependency and variable image quality. Although echocardiography is the first-line imaging modality for evaluating cardiac masses, diagnostic uncertainty frequently necessitates additional multimodality imaging. Artificial intelligence (AI), including machine learning and deep learning approaches, has emerged as a promising strategy to improve image interpretation, automate feature extraction, and enhance diagnostic consistency. Objective: This narrative review aims to examine current advances in AI-enhanced echocardiography for cardiac tumor detection, with a particular focus on detection, segmentation, classification, multimodal integration, and clinical translation. Methods: A narrative literature review was conducted using PubMed, Scopus, and Google Scholar databases. Relevant English-language studies published between 2016 and 2026 were identified using keywords including “artificial intelligence”, “machine learning”, “deep learning”, “echocardiography”, “cardiac tumors”, “intracardiac masses”, “multimodal imaging”, and “ultrasomics”. Original studies, reviews, and methodological papers related to AI-assisted cardiovascular imaging were evaluated. Discussion: Current evidence suggests that AI-driven techniques, including radiomics (ultrasomics), convolutional neural networks, and multimodal learning frameworks, can improve the detection, segmentation, and classification of intracardiac masses. Experimental studies have reported high diagnostic performance, with some deep learning models achieving diagnostic accuracies exceeding 95% under controlled conditions. AI-assisted systems may also reduce interobserver variability and improve workflow efficiency. Multimodal AI approaches integrating echocardiography with cardiac magnetic resonance imaging, computed tomography, electrocardiography, and clinical data appear particularly promising for improving diagnostic discrimination. However, current models remain limited by small and imbalanced datasets, insufficient external validation, data heterogeneity, and limited generalizability across institutions and imaging protocols. Additional barriers to clinical implementation include annotation variability, limited interpretability of deep learning models, and regulatory considerations. Conclusions: AI-enhanced echocardiography has substantial potential to improve the detection and characterization of intracardiac masses by augmenting diagnostic consistency and supporting clinical decision-making. Nevertheless, current evidence remains largely based on retrospective and experimental studies. Future progress will depend on large multicenter collaborations, standardized imaging datasets, explainable AI frameworks, and prospective clinical validation to enable safe and effective integration into routine cardiovascular practice. Full article

Background/Objectives: Differentiating clinically suspected cardiac amyloidosis from hypertrophic cardiomyopathy (HCM) remains a significant clinical challenge, especially when contrast-enhanced imaging is contraindicated. This study evaluated the potential diagnostic utility of non-contrast cardiac MRI parameters, specifically native T1 and T2 mapping, as supportive indicators in this differential diagnosis. Methods: This retrospective single-center study included 20 patients with clinically suspected amyloidosis (based on combined clinical and echocardiographic assessment), 20 patients with HCM, and 20 healthy controls. Cine imaging and native T1/T2 mapping were analyzed. Myocardial, blood-pool, and liver T1/T2 values, along with morphological parameters, were recorded. N-terminal pro–B-type natriuretic peptide (NT-proBNP) and troponin levels, when available, were documented retrospectively for descriptive purposes. Receiver operating characteristic (ROC) analyses were performed to assess the discriminatory performance of imaging parameters. Results: Patients in the suspected amyloidosis group demonstrated significantly higher myocardial, blood-pool, and liver T1 values, as well as higher myocardial T2 values, compared with both the HCM and control groups (p < 0.001). Myocardial T1 showed strong discriminatory performance for differentiating suspected amyloidosis from controls (cut-off 1061 ms, AUC = 0.975). In distinguishing suspected amyloidosis from HCM, blood-pool T1 (AUC = 0.900) and myocardial T1 (AUC = 0.938) provided the highest diagnostic performance. Additionally, elevated NT-proBNP (>1000 pg/mL in 93% of tested cases) and troponin levels were observed in the suspected amyloidosis group, consistent with increased myocardial stress. Conclusions: Native T1 and T2 mapping may offer valuable supportive information in differentiating clinically suspected amyloidosis from HCM on non-contrast MRI. Myocardial and blood-pool T1 values appear to provide complementary tissue characterization, which may be particularly useful when gadolinium administration or invasive procedures are not feasible. These findings suggest a role for non-contrast mapping in the diagnostic workup but require further validation in larger, biopsy-confirmed multicenter cohorts. Full article

Background and Clinical Significance: Bicuspid aortic valve (BAV) is the most common congenital heart defect and may coexist with other cardiovascular anomalies. Among these is a single coronary artery (SCA), a rare congenital condition in which the entire coronary circulation originates from a single coronary ostium. Cardiac computed tomography (CCT) enables simultaneous evaluation of coronary artery anatomy and aortic valve morphology with high spatial resolution, which may influence procedural strategy in patients undergoing valve interventions. Case Presentation: This report represents the first documented case of a 59-year-old male with mixed aortic valve disease in whom preoperative CCT revealed the coexistence of BAV, SCA (Lipton type L-I), and myocardial bridging (MB) involving the mid segment of the left anterior descending artery (LAD). Identification of these findings was crucial for preoperative assessment and contributed to the selection of an appropriate surgical strategy. Conclusions: CCT plays a key role in the preoperative evaluation of valvular heart disease, including in patients with coexisting BAV and SCA. It enables individualized procedural planning and minimizes the risk of perioperative complications. Full article

Background/Objectives: Mucopolysaccharidosis type III (MPS III, Sanfilippo syndrome) is an autosomal recessive lysosomal storage disorder caused by deficiencies in enzymes required for heparan sulfate degradation. While primarily recognized for its devastating neurodegenerative course, the systemic extent of glycosaminoglycan (GAG) accumulation remains under-characterized. This study aims to provide a detailed multisystemic pathological mapping of MPS III to challenge the traditional “brain-only” disease paradigm and highlight the clinical relevance of extracerebral involvement. Methods: We present a comprehensive clinicopathological analysis of a 15-year-old female patient with a history of profound neuropsychomotor delay, refractory epilepsy, and spastic tetraplegia. Following her death due to terminal bronchopneumonia during palliative care, a complete forensic and pathological autopsy was conducted. Tissue samples from all major organ systems were processed using routine Hematoxylin–Eosin (HE) staining, immunohistochemical staining for CD68, and specialized histochemical stains to identify intracellular storage products. Results: Macroscopic evaluation revealed significant diffuse cerebral atrophy, meningoencephalic edema, cardiac valvulopathy with compensatory myocardial remodeling, and hepatosplenomegaly. Furthermore, erosive gastrointestinal lesions and degenerative renal changes were identified. Histopathological examination confirmed widespread cytoplasmic vacuolization across diverse cell populations, including neurons, hepatocytes, renal tubular cells, and the reticuloendothelial system. These findings demonstrate that GAG deposition is a generalized process affecting nearly every parenchymal structure. Conclusions: Although neurological decline dominates the clinical phenotype, our findings underscore that MPS III is a true systemic storage disorder. Significant involvement of the cardiovascular and visceral systems contributes to the disease’s complexity and mortality. This case reinforces the critical diagnostic value of a comprehensive autopsy in delineating the full morphological spectrum of Sanfilippo syndrome, providing essential insights for multidisciplinary management. Full article