Search Results (4,930)

Background/objectives: Individuals with type 1 diabetes (T1DM) face an elevated risk of cardiovascular disease (CVD), yet the factors driving atherosclerosis remain unclear. This study aimed to assess factors associated with preclinical atherosclerosis development or progression in T1DM. Methods: We conducted a prospective study in T1DM individuals without established CVD, aged ≥40 years, with diabetic kidney disease and/or ≥10 years of T1DM plus another cardiovascular risk factor (CVRF). Baseline evaluation followed a standardized CV risk assessment protocol, including carotid ultrasound and cardiovascular risk estimation using the Steno Type 1 Risk Engine (ST1RE). Ultrasound was repeated after 3–5 years; progression was defined as an increase in plaque number. CVRF control was considered optimal when LDL-cholesterol was within target based on atherosclerotic burden, blood pressure <130/80 mmHg, HbA1c <7%, and non-smoking status. Logistic regression models identified predictors of progression. Results: We included 151 participants (55.6% women; mean age 49.8 ± 8.9 years; T1DM duration 27.3 ± 9.1 years); 42.4% had plaques at baseline. Over a follow-up of 5.22 ± 1.29 years, despite improved CVRF control (p < 0.05), 40.4% experienced progression. Older age (OR 1.38 [1.1–1.8]) and active smoking (OR 3.29 [1.4–7.5]) were significant predictors of progression. Baseline cardiovascular risk measured by the ST1RE independently predicted progression (OR 1.09 [1.03–1.15]) after adjusting for other CVRFs. Persistent smoking (OR 2.52 [1.06–5.99]) and baseline ST1RE (OR 1.06 [1.02–1.11]) remained significant after accounting for baseline and follow-up CVRFs. Conclusions: Despite improved CVRF control, atherosclerosis progression is common in T1D. ST1RE may help identify individuals at highest risk for targeted preventive strategies. Full article

Enhanced Giant Magneto-Impedance (GMI) effects of composite materials play a crucial role in producing devices with a good soft magnetic property. To improve this soft magnetic property, graphene is introduced to increase the conductivity of composite materials. However, the quality of graphene layers restricts the enhancement of GMI effects. There are few reports on the direct growth of graphene on Fe_73.5Si_13.5B₉Cu₁Nb₃ (FINEMET). In this paper, the composite ribbons of FINEMET coated with as-grown graphene are prepared by chemical vapor deposition (CVD), which is much better than previous results obtained by methods such as the transfer method or electroless plating in quality. The Ni layer, with good magnetic conductivity, is induced to the FINEMET as an auxiliary layer by the magnetron sputtering method for high-quality graphene-layer growth due to its high carbon dissolution rate. The results show that the growth temperature of the as-grown graphene layer on the FINEMET with the best GMI ratio could reach as high as 560 °C. Moreover, it was found that an Ni layer thickness of 300 nm has a crucial impact on GMI, with the maximum ratio reaching 76.8%, which is 1.9 times that of an initial bare FINEMET ribbon (39.7%). As a result, the direct growth of graphene layers on FINEMET ribbons by the CVD method is a promising way to light GMI-based devices. Full article

Borophene, a two-dimensional (2D) allotrope of boron, has emerged as a highly promising material owing to its exceptional mechanical strength, electronic conductivity, and diverse structural phases. Unlike graphene and other 2D materials, borophene exhibits inherent anisotropy, flexibility, and metallicity, offering unique opportunities for advanced nanotechnological applications. This review presents a comprehensive summary of recent progress in borophene synthesis methods, highlighting both bottom–up strategies such as chemical vapor deposition (CVD) and molecular beam epitaxy (MBE), and top–down approaches, including liquid-phase exfoliation and sonochemical techniques. A key challenge discussed is the stabilization of borophene’s polymorphs, as bulk boron’s non-layered structure complicates exfoliation. The influence of substrates and doping strategies on structural stability and phase control is also explored. Moreover, the intrinsic physicochemical properties of borophene, including its high flexibility, oxidation resistance, and anisotropic charge transport, were examined in relation to their implications for electronic, catalytic, and sensing devices. Particular attention was given to borophene’s performance in hydrogen storage and hydrogen evolution reactions (HERs), where functionalization with alkali and transition metals significantly enhances H₂ adsorption energy and storage capacity. Studies demonstrate that certain borophene–metal composites, such as Ti- or Li-decorated borophene, can achieve hydrogen storage capacities exceeding 10 wt.%, surpassing the U.S. Department of Energy targets for hydrogen storage materials. Despite these promising characteristics, large-scale synthesis, long-term stability, and integration into practical systems remain open challenges. This review identifies current research gaps and proposes future directions to facilitate the development of borophene-based energy solutions. The findings support borophene’s strong potential as a next-generation material for clean energy applications, particularly in hydrogen production and storage systems. Full article

Cardiovascular disease (CVD) is a term used for disorders affecting the heart. Globally, it is the most common cause of death. The main purpose of this study was the rapid detection of CVD, which is essential for effective cure and inhibition. Early detection may lower the risk of myocardial infarction (MI) and reduce the death rate in CVD patients. Laser-induced breakdown spectroscopy (LIBS) is a non-invasive and less sample preparation technique for early detection of CVD. LIBS technique investigated the variation in intensities of different biochemical elements such as Calcium (Ca), Nitrogen (N), Sodium (Na), Carbon (C) and CN-band in the spectra of healthy and CVD patients. Machine learning algorithms applied to LIBS spectral data for the determination of validation accuracy and classification between CVD and healthy individuals. Several models achieved a perfect 100% highest accuracy, which showed the exceptional precision in the given configuration. The Narrow Neural Network achieved 100% accuracy on both the validation and test datasets in a short duration of 10.008 s. This preliminary research of LIBS combined with machine learning may provide a complementary method over existing analytical techniques for early detection of CVD. Full article

Cardiovascular disease remains the world’s leading cause of mortality, yet everyday care still relies on episodic, symptom-driven interventions that detect ischemia, arrhythmias, and remodeling only after tissue damage has begun, limiting the effectiveness of therapy. A narrative review synthesized 183 studies published between 2016 and 2025 that were located through PubMed, MDPI, Scopus, IEEE Xplore, and Web of Science. This review examines CVD diagnostics using innovative technologies such as digital cardiovascular twins, which involve the collection of data from wearable IoT devices (electrocardiography (ECG), photoplethysmography (PPG), and mechanocardiography), clinical records, laboratory biomarkers, and genetic markers, as well as their integration with artificial intelligence (AI), including machine learning and deep learning, graph and transformer networks for interpreting multi-dimensional data streams and creating prognostic models, as well as generative AI, medical large language models (LLMs), and autonomous agents for decision support, personalized alerts, and treatment scenario modeling, and with cloud and edge computing for data processing. This multi-layered architecture enables the detection of silent pathologies long before clinical manifestations, transforming continuous observations into actionable recommendations and shifting cardiology from reactive treatment to predictive and preventive care. Evidence converges on four layers: sensors streaming multimodal clinical and environmental data; hybrid analytics that integrate hemodynamic models with deep-, graph- and transformer learning while Bayesian and Kalman filters manage uncertainty; decision support delivered by domain-tuned medical LLMs and autonomous agents; and prospective simulations that trial pacing or pharmacotherapy before bedside use, closing the prediction-intervention loop. This stack flags silent pathology weeks in advance and steers proactive personalized prevention. It also lays the groundwork for software-as-a-medical-device ecosystems and new regulatory guidance for trustworthy AI-enabled cardiovascular care. Full article

Electrocardiogram (ECG) and phonocardiogram (PCG) signals are widely used in the early prevention and diagnosis of cardiovascular diseases (CVDs) due to their ability to accurately reflect cardiac conditions from different physiological perspectives and their ease of acquisition. Currently, some studies have explored the joint use of ECG and PCG signals for disease screening, but few studies have considered the trade-off between classification performance and energy consumption in model design. In this study, we propose a multimodal CVDs detection framework based on Spiking Neural Networks (SNNs), which integrates ECG and PCG signals. A differential fusion strategy at the signal level is employed to generate a fused EPCG signal, from which time–frequency features are extracted using the Adaptive Superlets Transform (ASLT). Two separate Spiking Convolutional Neural Network (SCNN) models are then trained on the ECG and EPCG signals, respectively. A confidence-based dynamic decision-level (CDD) fusion strategy is subsequently employed to perform the final classification. The proposed method is validated on the PhysioNet/CinC Challenge 2016 dataset, achieving an accuracy of 89.74%, an AUC of 89.08%, and an energy consumption of 209.6 $\mathsf{\mu }$J. This method not only achieves better balancing performance compared to unimodal signals but also realizes an effective balance between model energy consumption and classification effect, which provides an effective idea for the development of low-power, multimodal medical diagnostic systems. Full article

The oral microbiota, long recognized for their role in local pathologies, are increasingly implicated in systemic disorders, particularly cardiovascular disease (CVD). This review focuses on emerging evidence linking oral dysbiosis to neuroglial activation and autonomic dysfunction as key mediators of cardiovascular pathology. Pathogen-associated molecular patterns, as well as gingipains and leukotoxin A from Porphyromonas gingivalis, Fusobacterium nucleatum, Treponema denticola, Aggregatibacter actinomycetemcomitans, etc., disrupt the blood–brain barrier, activate glial cells in autonomic centers, and amplify pro-inflammatory signaling. This glia driven sympathetic overactivity fosters hypertension, endothelial injury, and atherosclerosis. Crucially, sex hormones modulate these neuroimmune interactions, with estrogen and testosterone shaping microbial composition, glial reactivity, and cardiovascular outcomes in distinct ways. Female-specific factors such as early menarche, pregnancy, adverse pregnancy outcomes, and menopause exert profound influences on oral microbial ecology, systemic inflammation, and long-term CVD risk. By mapping this oral–brain–heart axis, this review highlights the dual role of oral microbial virulence factors and glial dynamics as mechanistic bridges linking periodontal disease to neurogenic cardiovascular regulation. Integrating salivary microbiome profiling with glial biomarkers [e.g., GFAP (Glial Fibrillary Acidic Protein) and sTREM2 (soluble Triggering Receptor Expressed on Myeloid cells 2)] offers promising avenues for sex-specific precision medicine. This framework not only reframes oral dysbiosis as a modifiable cardiovascular risk factor, but also charts a translational path toward gender tailored diagnostics and therapeutics to reduce the global CVD burden. Full article

Background/Objectives: The relationship between dairy consumption and cardiovascular or bone health outcomes remains controversial, with inconsistent findings across existing meta-analyses. In this study, we aimed to systematically evaluate and synthesize the evidence from published meta-analyses on dairy consumption and cardiovascular and bone health outcomes in adults, and to conduct updated meta-analyses incorporating recently published prospective cohort studies. Methods: We performed an umbrella review following PRISMA guidelines, searching published and grey literature up to April 2024. Meta-analyses evaluating dairy intake and its impact on cardiovascular and bone health outcomes were included. Updated meta-analyses were conducted for cardiovascular outcomes, while bone health outcomes were synthesized qualitatively. Methodological quality was assessed using the Joanna Briggs Institute checklist. Random-effects models were applied, and heterogeneity, small-study effects, excess significance, and prediction intervals were evaluated. Results: We included 33 meta-analyses (26 on cardiovascular, 7 on bone health outcomes). Updated meta-analyses showed that total dairy (RR: 0.96), milk (RR: 0.97), and yogurt (RR: 0.92) were significantly associated with reduced CVD risk. Total dairy and low-fat dairy were inversely linked to hypertension (RRs: 0.89, 0.87), and milk and low-fat dairy were associated with reduced stroke risk. Small-study effects were absent for most associations. Credibility was rated as “weak” for most associations, with total dairy and stroke, and total dairy and hypertension showing "suggestive" evidence. For bone health, dairy—especially milk—was linked to higher bone mineral density (BMD). Evidence on osteoporosis risk was mixed, and while total dairy and milk showed inconsistent associations with fractures, cheese and yogurt showed more consistent protective effects. Limited evidence suggested milk may reduce bone resorption markers. Conclusions: This review suggests that dairy consumption, particularly milk and yogurt, is modestly associated with reduced cardiovascular risk, while dairy intake appears to benefit BMD and fracture prevention. However, further research is needed to confirm these associations. Full article

Graphene/h-BN/Si heterostructures show considerable potential for future use in infrared detection and photovoltaic technologies due to their adjustable electrical behavior and well-matched interfacial structure. The near-lattice match between graphene and hexagonal boron nitride (h-BN) enables the deposition of low-defect-density graphene on h-BN surfaces. This study presents a thorough exploration of the low-frequency electrical noise behavior of graphene/h-BN/Si heterojunctions under both forward and reverse bias conditions at room temperature. Graphene nanolayers were directly grown on h-BN films using microwave plasma-enhanced CVD. The h-BN layers were formed by reactive high-power impulse magnetron sputtering (HIPIMS). Four h-BN thicknesses were examined: 1 nm, 3 nm, 5 nm, and 15 nm. A reference graphene/Si junction (without h-BN) prepared under identical synthesis conditions was also studied for comparison. Low-frequency noise analysis enabled the identification of dominant charge transport mechanisms in the different device structures. Our results demonstrate that grain boundaries act as dominant defects contributing to increased noise intensity under high forward bias. Statistical analysis of voltage noise spectral density across multiple samples, supported by Raman spectroscopy, reveals that hydrogen-related defects significantly contribute to 1/f noise in the linear region of the junction’s current–voltage characteristics. This study provides the first in-depth insight into the impact of h-BN interlayers on low-frequency noise in graphene/Si heterojunctions. Full article

In response to the demand for advanced materials in extreme environments, researchers have developed a variety of bulk and thin-film materials. One of the best-known processes for altering the mechanical and tribological properties of materials is surface engineering techniques. These involve various approaches to synthesize thin-film coatings, along with post-deposition treatments. The need for self-lubricating materials in extreme situations such as high-temperature applications, cryogenic temperatures, and vacuum systems has attracted the attention of researchers. They have fabricated several types of thin films using CVD and PVD techniques to meet this demand. Among the various techniques used for fabricating self-lubricating coatings, sputtering stands out as a special one. It contributes to developing smooth, homogeneous, and crack-free dense microstructures, which further enhance the coatings’ properties. This review explains the need for self-lubricating materials and the different techniques used to synthesize them. It discusses and summarizes the concept of synthesizing various types of self-lubricating films. It shows the different types of self-lubricating material systems, like transition metal-based nitrides and carbides, diamond-like carbon-based materials, and so on. This work also reflects the governing factors like the deposition temperature, doping elements, thickness of the film, deposition pressure, gas flow rate, etc., that influence the deposition results and, consequently, the properties of the film, as well as their advanced applications in different areas. This work reflects the self-lubricating properties of different kinds of films exposed to various environments in terms of their coefficient of friction and wear rate, emphasizing how the friction coefficient affects the wear rate. Full article

Graphene, a two-dimensional material with remarkable electrical, thermal, and mechanical properties, has revolutionized the fields of electronics, energy storage, and nanotechnology. This review presents a comprehensive analysis of graphene synthesis techniques, which can be classified into two primary approaches: top-down and bottom-up. Top-down methods, such as mechanical exfoliation, oxidation-reduction, unzipping carbon nanotubes, and liquid-phase exfoliation, are highlighted for their scalability and cost-effectiveness, albeit with challenges in controlling defects and uniformity. In contrast, bottom-up methods, including chemical vapor deposition (CVD), arc discharge, and epitaxial growth on silicon carbide, offer superior structural control and quality but are often constrained by high costs and limited scalability. The interplay between synthesis parameters, material properties, and application requirements is critically examined to provide insights into optimizing graphene production. This review also emphasizes the growing demand for sustainable and environmentally friendly approaches, aligning with the global push for green nanotechnology. By synthesizing current advancements and identifying critical research gaps, this work offers a roadmap for selecting the most suitable synthesis techniques and fostering innovations in scalable and high-quality graphene production. The findings serve as a valuable resource for researchers and industries aiming to harness graphene’s full potential in diverse technological applications. Full article

Background: Evidence linking dietary fiber intake with cancer risk and mortality is equivocal. Objective: We investigated the relationship between dietary fiber intake and all-cause, cancer, and cardiovascular disease (CVD) mortalities in US adults ≥ 20 years. Methods: Data from the National Health and Nutrition Examination Surveys (NHANES) from 2003 to 2016 were used. Seven two-year cycles were concatenated into one analytic data file, NHANES 2003–2016 (n = 25,868; age ≥ 20 years). Dietary fiber intakes were collected from one 24-h dietary recall. Fiber intakes were categorized into quartiles. Mortality information was obtained from data linkage. To determine mortality, subjects were followed up for 6.4 years. Association between dietary fiber and mortality from all causes, cancer, and CVD was determined with multivariable-adjusted Cox proportional hazards models. Multivariate-adjusted Cox proportional hazard regression was used to generate mortality survival rates. Results: During the follow-up period, out of 2520 deaths, 561 and 511 deaths were from cancer and CVD, respectively. Dietary fiber intake was inversely associated with all-cause mortality [RR (95% CI), 0.67 (0.56–0.80); p ≤ 0.001]. No relationship was observed between fiber intake and cancer mortality [RR (95% CI), 0.8 (0.55–1.17); p = 0.51] and CVD mortality [RR (95% CI), 0.84 (0.53–1.33); p = 0.67]. Conclusions: In the US population, dietary fiber intake was associated with decreased all-cause mortality, but not with cancer and CVD mortality. Full article

Metabolic dysfunction-associated steatotic liver disease (MASLD) significantly increases the risk of steatohepatitis and cirrhosis and multiple extrahepatic complications, in particular, cardiometabolic disease, including type 2 diabetes, atherosclerotic cardiovascular disease (CVD), and heart failure, with a significant negative impact on health-related quality of life, becoming a substantial economic burden. Moreover, cardiovascular events represent the leading cause of death in MASLD patients. A timely diagnosis stratifies patient for their risk. It can facilitate early lifestyle changes or pharmacological management of dysmetabolic conditions, thereby slowing disease progression, lowering cardiovascular risk, and preventing CVD and cirrhosis. In this narrative review, we will discuss the current knowledge on MASLD and metabolic dysfunction-associated steatohepatitis (MASH) pathophysiology, emphasizing their systemic nature, the link to CVD, and available and emerging treatment strategies. Full article

To ensure the mechanical performance of gas turbine hollow blades under high-temperature conditions, the application of aluminide high-temperature protective coatings on the inner gas flow channel surfaces of hollow blades via chemical vapor deposition (CVD) has become a critical measure for enhancing blade safety. This study employs computational fluid dynamics (CFD) to investigate the flow field within CVD reactors and the influences of deposition processes on the chemical reaction rates at sample surfaces, thereby guiding the optimization of CVD reactor design and deposition parameters. Three distinct CVD reactor configurations are examined to analyze the flow characteristics of precursor gases and the internal flow field distributions. The results demonstrate that Model A, featuring a bottom-positioned outlet and an extended inlet, exhibits a larger stable deposition zone with more uniform flow velocities near the sample surface, thereby indicating the formation of higher-quality aluminide coatings. Based on Model A, CFD simulations are conducted to evaluate the effects of process parameters, including inflow velocity, pressure, and temperature, on aluminide coating deposition. The results show that the surface chemical reaction rate increases with inflow velocity (0.0065–6.5 m/s), but the relative change rate (ratio of reaction rate to flow rate) shows a declining trend. Temperature variations (653–1453 K) induce a trapezoidal-shaped trend in deposition rates: an initial increase (653–1053 K), followed by stabilization (1053–1303 K), and a subsequent decline (>1303 K). The underlying mechanisms for this trend are discussed. Pressure variations (0.5–2 atm) reveal that both excessively low and high pressures reduce surface reaction rates, with optimal performance observed near 1 atm. This study provides a methodology and insights for optimizing CVD reactor designs and process parameters to enhance aluminide coating quality on turbine blades. Full article