Search Results (1,438)

Atherosclerosis is a progressive, multifactorial disease driven by the interplay of lipid dysregulation, chronic inflammation, oxidative stress, and maladaptive vascular remodeling. Despite advances in systemic lipid-lowering and anti-inflammatory therapies, residual cardiovascular risk persists, highlighting the need for more precise interventions. Targeted drug delivery represents a transformative strategy, offering the potential to modulate key pathogenic processes within atherosclerotic plaques while minimizing systemic exposure and off-target effects. Recent innovations span a diverse array of platforms, including nanoparticles, liposomes, exosomes, polymeric carriers, and metal–organic frameworks (MOFs), engineered to engage distinct pathological features such as inflamed endothelium, dysfunctional macrophages, oxidative microenvironments, and aberrant lipid metabolism. Ligand-based, biomimetic, and stimuli-responsive delivery systems further enhance spatial and temporal precision. In parallel, advances in in-silico modeling and imaging-guided approaches are accelerating the rational design of multifunctional nanotherapeutics with theranostic capabilities. Beyond targeting lipids and inflammation, emerging strategies seek to modulate immune checkpoints, restore endothelial homeostasis, and reprogram plaque-resident macrophages. This review provides an integrated overview of the mechanistic underpinnings of atherogenesis and highlights state-of-the-art targeted delivery systems under preclinical and clinical investigation. By synthesizing recent advances, we aim to elucidate how precision-guided drug delivery is reshaping the therapeutic landscape of atherosclerosis and to chart future directions toward clinical translation and personalized vascular medicine. Full article

Moyamoya disease (MMD) is primarily associated with genetic variants in RNF213. RNF213 p.R4810K (c.14429G>A, p.Arg4810Lys) is a founder variant predominantly found in East Asian populations and is strongly associated with MMD, a rare cerebrovascular condition characterized by progressive stenosis of intracranial arteries and the development of abnormal collateral networks. Recent evidence suggests that RNF213 variants are also enriched in non-moyamoya intracranial arteriopathies, such as large-artery atherosclerotic stroke and intracranial arterial stenosis/occlusion (ICASO), particularly in east Asian individuals with early-onset or cryptogenic stroke. This expanded phenotypic spectrum, termed RNF213-related vasculopathy (RRV), represents a distinct pathogenic entity that may involve unique pathogenic processes separate from traditional atherosclerosis. In this review, we synthesize current genetic, clinical, radiological, and experimental findings that delineate the unique features of RRV. Patients with RRV typically exhibit a lower burden of traditional vascular risk factors, negative vascular remodeling in the absence of atheromatous plaques, and an increased propensity for disease progression. RNF213 variants may compromise vascular resilience by impairing adaptive responses to hemodynamic stress. Furthermore, emerging cellular and animal model data indicate that RNF213 influences angiogenesis, lipid metabolism, and stress responses, offering mechanistic insights into its role in maintaining vascular integrity. Recognizing RRV as a distinct clinical entity has important implications for diagnosis, risk stratification, and the development of genome-informed therapeutic strategies. Full article

Retinal vessel segmentation in fundus images is critical for diagnosing microvascular and ophthalmologic diseases. However, the task remains challenging due to significant vessel width variation and low vessel-to-background contrast. To address these limitations, we propose WDM-UNet, a novel spatial-wavelet dual-domain fusion architecture that integrates spatial and wavelet-domain representations to simultaneously enhance the local detail and global context. The encoder combines a Deformable Convolution Encoder (DCE), which adaptively models complex vascular structures through dynamic receptive fields, and a Wavelet Convolution Encoder (WCE), which captures the semantic and structural contexts through low-frequency components and hierarchical wavelet convolution. These features are further refined by a Gated Fusion Transformer (GFT), which employs gated attention to enhance multi-scale feature integration. In the decoder, depthwise separable convolutions are used to reduce the computational overhead without compromising the representational capacity. To preserve fine structural details and facilitate contextual information flow across layers, the model incorporates skip connections with a hierarchical fusion strategy, enabling the effective integration of shallow and deep features. We evaluated WDM-UNet in three public datasets: DRIVE, STARE, and CHASE_DB1. The quantitative evaluations demonstrate that WDM-UNet consistently outperforms state-of-the-art methods, achieving 96.92% accuracy, 83.61% sensitivity, and an 82.87% F1-score in the DRIVE dataset, with superior performance across all the benchmark datasets in both segmentation accuracy and robustness, particularly in complex vascular scenarios. Full article

Multiple sclerosis (MS), a chronic disease of the central nervous system, is known to cause structural and vascular changes in the retina. Although optical coherence tomography (OCT) and fundus photography can detect retinal thinning and circulatory abnormalities, these findings are not specific to MS. This study explores the potential of Infrared Scanning-Laser-Ophthalmoscopy (IR-SLO) imaging to uncover vascular morphological features that may serve as MS-specific biomarkers. Using an age-matched, subject-wise stratified k-fold cross-validation approach, a deep learning model originally designed for color fundus images was adapted to segment optic disc, optic cup, and retinal vessels in IR-SLO images, achieving Dice coefficients of 91%, 94.5%, and 97%, respectively. This process included tailored pre- and post-processing steps to optimize segmentation accuracy. Subsequently, clinically relevant features were extracted. Statistical analyses followed by SHapley Additive exPlanations (SHAP) identified vessel fractal dimension, vessel density in zones B and C (circular regions extending 0.5–1 and 0.5–2 optic disc diameters from the optic disc margin, respectively), along with vessel intensity and width, as key differentiators between MS patients and healthy controls. These findings suggest that IR-SLO can non-invasively detect retinal vascular biomarkers that may serve as additional or alternative diagnostic markers for MS diagnosis, complementing current invasive procedures. Full article

Ischemic stroke is a primary cause of cerebrovascular diseases and continues to be one of the leading causes of death and disability among patients worldwide. Pathological processes caused by vascular damage due to stroke occur in a time-dependent manner and are classified into three categories: acute, subacute, and chronic. Current treatments for ischemic stroke are limited to effectiveness in the early stages. In this study, we investigated the therapeutic effect of an oriental medicine, Gosha-jinki-gan (TJ107), on improving chronic ischemic stroke using the mouse model with middle cerebral artery occlusion (MCAO). The changes in the intracerebral inflammatory response (macrophages (F4/80), TLR2•4, IL-23, IL-17, TNF-α, and IL-1β) were examined using real-time RT-PCR. The MCAO mice showed the increased expression of glial fibrillary acidic protein (GFAP) and of F4/80, TLR2, TLR4, IL-1β, TNF-α, and IL-17 in the brain tissue from the MCAO region. This suggests that they contribute to the expansion of the ischemic stroke infarct area and to the worsening of the neurological symptoms of the MCAO mice in the chronic phase. On the other hand, the administration of TJ107 was proven to reduce the infarct area, with decreased GFAP expression, suppressed macrophage infiltration in the brain, and reduced TNF-α, IL-1β, and IL-17 production compared with the MCAO mice. This study first demonstrated Gosha-jinki-gan’s therapeutic effects on the chronic ischemic stroke. Full article

Brain organoid technology has revolutionized in vitro modeling of human neurodevelopment and disease, providing unprecedented insights into cortical patterning, neural circuit assembly, and pathogenic mechanisms of neurological disorders. Critically, human brain organoids uniquely recapitulate human-specific developmental processes—such as the expansion of outer radial glia and neuromelanin—that are absent in rodent models, making them indispensable for studying human brain evolution and dysfunction. However, a major bottleneck persists: Extended culture periods (≥6 months) are empirically required to achieve late-stage maturation markers like synaptic refinement, functional network plasticity, and gliogenesis. Yet prolonged conventional 3D culture exacerbates metabolic stress, hypoxia-induced necrosis, and microenvironmental instability, leading to asynchronous tissue maturation—electrophysiologically active superficial layers juxtaposed with degenerating cores. This immaturity/heterogeneity severely limits their utility in modeling adult-onset disorders (e.g., Alzheimer’s disease) and high-fidelity drug screening, as organoids fail to recapitulate postnatal transcriptional signatures or neurovascular interactions without bioengineering interventions. We summarize emerging strategies to decouple maturation milestones from rigid temporal frameworks, emphasizing the synergistic integration of chronological optimization (e.g., vascularized co-cultures) and active bioengineering accelerators (e.g., electrical stimulation and microfluidics). By bridging biological timelines with scalable engineering, this review charts a roadmap to generate translationally relevant, functionally mature brain organoids. Full article

Background/Objectives: Cardiovascular disease (CVD) contributes to stroke and dementia. Individuals with CVD have high risk for adverse cognitive outcomes and stroke, possibly due to shared risk factors between CVD, stroke, and dementia, which may be attributed to cerebral small vessel disease (CSVD). We aim to determine the association between prevalent CVD and atrial fibrillation (AF) with CSVD. Methods: Composite of CVD [coronary heart disease, heart failure (HF)], its individual components, and AF were assessed. Multi-marker CSVD score was used to reflect increasing CSVD burden (cerebral microbleeds (CMBs), high-burden perivascular spaces, extensive white matter hyperintensity, cortical superficial siderosis, or covert brain infarcts were assigned 1 point each, with a range of 0–5). We related prevalent CVD, its individual components, and AF to multi-marker CSVD score and individual CSVD markers using logistic regression analyses adjusted for age, sex, FHS cohort, time between MRI and clinic exam (model-1), and vascular risk factors (model-2). Results: In 3413 participants (mean age: 59 ± 14 years, 53.4% women), 11% had prevalent CVD or AF, 8% had prevalent CVD, and 4% had prevalent AF. One CSVD marker was seen in 23% participants, and 9% had ≥ 2 markers. In multivariable-adjusted analyses, composite prevalent CVD and AF was associated with the presence of one CSVD marker (OR: 1.38, 95% confidence interval [CI]: 1.05–1.84). The association with ≥2 CSVD markers was not significant. Only CMBs were associated with components of CVD and AF, with the highest odds of association with HF. Conclusions: Prevalent CVD (including AF) is associated with the presence of CSVD, with all components associated with CMBs. Full article

Background/Objectives: Interstitial lung disease (ILD) is frequently complicated by pulmonary hypertension (PH), which is associated with reduced exercise capacity and poor prognosis. Early and accurate non-invasive detection of PH remains a clinical challenge. This study evaluated whether combining quantitative CT analysis of lung fibrosis with cardiac MRI-derived measures of right ventricular (RV) function improves the diagnostic accuracy of PH in patients with ILD. Methods: We retrospectively analyzed 72 ILD patients who underwent chest CT, cardiac MRI, and right heart catheterization (RHC). Lung fibrosis was quantified using a Gaussian Histogram Normalized Correlation (GHNC) software that computed the proportions of diseased lung, ground-glass opacity (GGO), honeycombing, reticulation, consolidation, and emphysema. MRI was used to assess RV end-systolic volume (RVESV), ejection fraction, and RV longitudinal strain. PH was defined as a mean pulmonary arterial pressure (mPAP) ≥ 20 mmHg and pulmonary vascular resistance ≥ 3 Wood units on RHC. Results: Compared to patients without PH, those with PH (n = 21) showed significantly reduced RV strain (−13.4 ± 5.1% vs. −16.4 ± 5.2%, p = 0.026) and elevated RVESV (74.2 ± 18.3 mL vs. 59.5 ± 14.2 mL, p = 0.003). CT-derived indices also differed significantly: diseased lung area (56.4 ± 17.2% vs. 38.4 ± 12.5%, p < 0.001), GGO (11.8 ± 3.6% vs. 8.65 ± 4.3%, p = 0.005), and honeycombing (17.7 ± 4.9% vs. 12.8 ± 6.4%, p = 0.0027) were all more prominent in the PH group. In receiver operating characteristic curve analysis, diseased lung area demonstrated an area under the curve of 0.778 for detecting PH. This increased to 0.847 with the addition of RVESV, and further to 0.854 when RV strain was included. Combined models showed significant improvement in risk reclassification: net reclassification improvement was 0.700 (p = 0.002) with RVESV and 0.684 (p = 0.004) with RV strain; corresponding IDI values were 0.0887 (p = 0.03) and 0.1222 (p = 0.01), respectively. Conclusions: Combining CT-based fibrosis quantification with cardiac MRI-derived RV functional assessment enhances the non-invasive diagnosis of PH in ILD patients. This integrated imaging approach significantly improves diagnostic precision and may facilitate earlier, more targeted interventions in the management of ILD-associated PH. Full article

Background: Hemiplegic migraine (HM) is a rare and severe subtype of migraine with a complex genetic basis. Although pathogenic variants in CACNA1A, ATP1A2, and SCN1A explain some familial cases, a significant proportion of patients remain genetically undiagnosed. Increasing evidence points to an overlap between migraine and cerebral small vessel disease (SVD), implicating vascular dysfunction in HM pathophysiology. Objective: This study aimed to identify rare or novel variants in genes associated with SVD in a cohort of patients clinically diagnosed with HM who tested negative for known familial hemiplegic migraine (FHM) pathogenic variants. Methods: We conducted a case-control association analysis of whole exome sequencing (WES) data from 184 unrelated HM patients. A targeted panel of 34 SVD-related genes was assessed. Variants were prioritised based on rarity (MAF ≤ 0.05), location (exonic/splice site), and predicted pathogenicity using in silico tools. Statistical comparisons to gnomAD’s Non-Finnish European population were made using chi-square tests. Results: Significant variants were identified in several SVD-related genes, including LRP1 (p.Thr4077Arg), COL4A1 (p.Pro54Leu), COL4A2 (p.Glu1123Gly), and TGFBR2 (p.Met148Leu and p.Ala51Pro). The LRP1 variant showed the strongest association (p < 0.001). All key variants demonstrated pathogenicity predictions in multiple computational models, implicating them in vascular dysfunction relevant to migraine mechanisms. Conclusions: This study provides new insights into the genetic architecture of hemiplegic migraine, identifying rare and potentially deleterious variants in SVD-related genes. These findings support the hypothesis that vascular and cellular maintenance pathways contribute to migraine susceptibility and may offer new targets for diagnosis and therapy. Full article

Chronic inflammatory response syndrome (CIRS) and Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) are debilitating multisystem illnesses that share overlapping symptoms and molecular patterns, including immune dysregulation, mitochondrial impairment, and vascular dysfunction. This review provides a chronological synthesis of biomarker development in CIRS, tracing its evolution from early functional tests such as visual contrast sensitivity (VCS) to advanced transcriptomic profiling. Drawing on peer-reviewed studies spanning two decades, we examine the layered integration of neuroendocrine, immunologic, metabolic, and genomic markers that collectively support a multisystem model of innate immune activation specific to environmentally acquired illness. Particular focus is given to the Gene Expression: Inflammation Explained (GENIE) platform’s use of transcriptomics to classify disease stages and distinguish CIRS from other fatiguing conditions. While ME/CFS research continues to explore overlapping pathophysiologic features, it has yet to establish a unified diagnostic model with validated biomarkers or exposure-linked mechanisms. As a result, many patients labeled with ME/CFS may, in fact, represent unrecognized CIRS cases. This review underscores the importance of structured biomarker timelines in improving differential diagnosis and guiding treatment in complex chronic illness and highlights the reproducibility of the CIRS framework in contrast to the diagnostic ambiguity surrounding ME/CFS. Full article

Background: Vascular risk factors (VRFs) are known to influence cerebral small vessel disease (cSVD) burden and progression. However, their specific impact on the presence and distribution of each cSVD imaging marker (white matter hyperintensity [WMH], perivascular spaces [PVSs], lacunes, and cerebral microbleeds [CMBs]) and their spatial distribution remains unclear. Methods: We conducted a retrospective analysis of 93 patients with lacunar stroke with a standardized investigational magnetic resonance imaging protocol using a 3T scanner. WMH and PVSs were segmented semi-automatically, and lacunes and CMBs were manually segmented. We assessed the univariable associations of four common VRFs (hypertension, hyperlipidemia, diabetes, and smoking) with the load of each cSVD marker. Then, we assessed the independent associations of these VRFs in multivariable regression models adjusted for age and sex. Spatial lesion patterns were explored with regional volumetric comparisons using Pearson’s coefficient analysis, which was adjusted for multiple comparisons, and by visually examining heatmap lesion distributions. Results: Hypertension was the VRF that exhibited stronger associations with the cSVD markers in the univariable analysis. In the multivariable analysis, only lacunes (p = 0.009) and PVSs in the basal ganglia (p = 0.014) and white matter (p = 0.016) were still associated with hypertension. In the regional analysis, hypertension showed a higher WMH load in deep structures and white matter, particularly in the posterior periventricular regions. In patients with hyperlipidemia, WMH was preferentially found in hippocampal regions. Conclusions: Hypertension was confirmed to be the VRF with the most impact on cSVD load, especially for lacunes and PVSs, while the lesion topography was variable for each VRF. These findings shed light on the complexity of cSVD expression in relation to factors detrimental to vascular health. Full article

Microfluidic methods are an important tool for studying the microrheology of blood and the mechanical properties of blood cells—erythrocytes, leukocytes, and platelets. In patients with diabetes, hypertension, obesity, sickle cell anemia, or cerebrovascular or peripheral vascular diseases, hemorheological alterations are commonly observed. These include increased blood viscosity and red blood cell (RBC) aggregation, along with reduced RBC deformability. Such disturbances significantly contribute to impaired microcirculation and microvascular perfusion. In blood vessels, abnormal hemorheological parameters can elevate resistance to blood flow, exert greater mechanical stress on the endothelial wall, and lead to microvascular complications. Among these parameters, erythrocyte deformability is a potential biomarker for diseases including diabetes, malaria, and cancer. This review highlights recent advances in microfluidic technologies for in vitro assays of RBC deformability and aggregation, as well as leukocyte aggregation and adhesion. It summarizes the core principles of microfluidic platforms and the experimental findings related to hemodynamic parameters. The advantages and limitations of each technique are discussed, and future directions for improving these devices are explored. Additionally, some aspects of the modeling of the microrheological properties of blood cells are considered. Overall, the described microfluidic systems represent promising tools for investigating erythrocyte mechanics and leukocyte behavior. Full article

Background/Objectives: Gut-derived tryptophan (Trp) metabolites play important roles in metabolic and cardiovascular regulation. Although animal studies suggest their protective effects against metabolic dysfunction, data in adolescents, particularly those with obesity, remain limited. The objective of this study was to evaluate associations between circulating gut-derived Trp metabolites and markers of cardiometabolic, vascular, and platelet health in adolescents with obesity. Methods: Data were analyzed from 28 adolescents (ages 13–18; mean BMI = 36 ± 6.4 kg/m²). Fasting blood was collected to assess lipid profiles using a clinical analyzer and insulin resistance using the homeostatic model assessment for insulin resistance (HOMA-IR). Gut-derived Trp metabolites were measured by UPLC–mass spectrometry, peak oxygen uptake (VO_{2 peak}) by gas exchange during an incremental cycle ergometer test, and body composition by dual-energy X-ray absorptiometry. Platelet spare respiratory capacity (SRC), endothelial function, and liver fat were measured using high-resolution respirometry, flow-mediated dilation (FMD) of the brachial artery, and magnetic resonance imaging respectively. Results: Indole-3-propionic acid was inversely associated with diastolic blood pressure (rho = −0.39, p = 0.047), total cholesterol (rho = −0.55, p = 0.002), and LDL-C (rho = −0.57, p = 0.0014), independent of sex and obesity severity. Indoxyl sulfate was positively correlated with fasting glucose (rho = 0.47, p = 0.012), and adolescents with impaired fasting glucose had 1.6-fold higher IS levels. Indole-3-acetaldehyde declined with age (rho = −0.50, p = 0.007), and Indole-3-acetic acid and indole were higher in Hispanics vs. non-Hispanics. No significant associations were observed between Trp metabolites and FMD, VO₂ peak, or SRC. Conclusions: Gut-derived Trp metabolites, particularly indole-3-propionic and indoxyl sulfate, are associated with markers of cardiometabolic risk in adolescents with obesity. These findings support their potential relevance in early-onset cardiovascular disease risk. Full article

Continuous blood pressure (BP) monitoring provides valuable insight into the body’s dynamic cardiovascular regulation across various physiological states such as physical activity, emotional stress, postural changes, and sleep. Continuous BP monitoring captures different variations in systolic and diastolic pressures, reflecting autonomic nervous system activity, vascular compliance, and circadian rhythms. This enables early identification of abnormal BP trends and allows for timely diagnosis and interventions to reduce the risk of cardiovascular diseases (CVDs) such as hypertension, stroke, heart failure, and chronic kidney disease as well as chronic stress or anxiety disorders. To facilitate continuous BP monitoring, we propose an AI-powered estimation framework. The proposed framework first uses an expert-driven feature engineering approach that systematically extracts physiological features from photoplethysmogram (PPG)-based arterial pulse waveforms (APWs). Extracted features include pulse rate, ascending/descending times, pulse width, slopes, intensity variations, and waveform areas. These features are fused with demographic data (age, gender, height, weight, BMI) to enhance model robustness and accuracy across diverse populations. The framework utilizes a Tab-Transformer to learn rich feature embeddings, which are then processed through an ensemble machine learning framework consisting of CatBoost, XGBoost, and LightGBM. Evaluated on a dataset of 1000 subjects, the model achieves Mean Absolute Errors (MAE) of 3.87 mmHg (SBP) and 2.50 mmHg (DBP), meeting British Hypertension Society (BHS) Grade A and Association for the Advancement of Medical Instrumentation (AAMI) standards. The proposed architecture advances non-invasive, AI-driven solutions for dynamic cardiovascular health monitoring. Full article

Background/Objectives: Peripheral artery disease (PAD) impacts more than 200 million individuals globally and leads to mortality and morbidity secondary to progressive limb dysfunction and amputation. However, clinical management of PAD remains suboptimal, in part because of the lack of standardized biomarkers to predict patient outcomes. Growth differentiation factor 15 (GDF15) is a stress-responsive cytokine that has been studied extensively in cardiovascular disease, but its investigation in PAD remains limited. This study aimed to use explainable statistical and machine learning methods to assess the prognostic value of GDF15 for limb outcomes in patients with PAD. Methods: This prognostic investigation was carried out using a prospectively enrolled cohort comprising 454 patients diagnosed with PAD. At baseline, plasma GDF15 levels were measured using a validated multiplex immunoassay. Participants were monitored over a two-year period to assess the occurrence of major adverse limb events (MALE), a composite outcome encompassing major lower extremity amputation, need for open/endovascular revascularization, or acute limb ischemia. An Extreme Gradient Boosting (XGBoost) model was trained to predict 2-year MALE using 10-fold cross-validation, incorporating GDF15 levels along with baseline variables. Model performance was primarily evaluated using the area under the receiver operating characteristic curve (AUROC). Secondary model evaluation metrics were accuracy, sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV). Prediction histogram plots were generated to assess the ability of the model to discriminate between patients who develop vs. do not develop 2-year MALE. For model interpretability, SHapley Additive exPlanations (SHAP) analysis was performed to evaluate the relative contribution of each predictor to model outputs. Results: The mean age of the cohort was 71 (SD 10) years, with 31% (n = 139) being female. Over the two-year follow-up period, 157 patients (34.6%) experienced MALE. The XGBoost model incorporating plasma GDF15 levels and demographic/clinical features achieved excellent performance for predicting 2-year MALE in PAD patients: AUROC 0.84, accuracy 83.5%, sensitivity 83.6%, specificity 83.7%, PPV 87.3%, and NPV 86.2%. The prediction probability histogram for the XGBoost model demonstrated clear separation for patients who developed vs. did not develop 2-year MALE, indicating strong discrimination ability. SHAP analysis showed that GDF15 was the strongest predictive feature for 2-year MALE, followed by age, smoking status, and other cardiovascular comorbidities, highlighting its clinical relevance. Conclusions: Using explainable statistical and machine learning methods, we demonstrated that plasma GDF15 levels have important prognostic value for 2-year MALE in patients with PAD. By integrating clinical variables with GDF15 levels, our machine learning model can support early identification of PAD patients at elevated risk for adverse limb events, facilitating timely referral to vascular specialists and aiding in decisions regarding the aggressiveness of medical/surgical treatment. This precision medicine approach based on a biomarker-guided prognostication algorithm offers a promising strategy for improving limb outcomes in individuals with PAD. Full article