Cardiovascular diseases (CVDs), such as atherosclerosis, myocardial remodeling, myocardial ischemia-reperfusion (I/R) injury, heart failure, and oxidative stress, are among the greatest threats to human health globally. The molecular mechanisms underlying CVDs have not yet been fully elucidated, but progress has been made in research on epigenetics in CVDs. Post-translational modifications (PTMs), which involve the covalent attachment of functional groups to modulate protein structure and function, represent a critical regulatory mechanism. These modifications enhance the functional diversity of the proteome without the need for de novo protein synthesis. Traditional types of PTMs, such as phosphorylation, acetylation, and ubiquitination, are closely associated with the pathogenesis of CVDs. With the application of high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS), an increasing number of novel acylation modifications have been discovered, including propionylation, butylation, crotonylation, succinylation, lactylation, and isonicotinylation. A deeper understanding of the role of PTMs in CVDs is essential for unraveling their molecular regulatory mechanisms and identifying new biomarkers and therapeutic targets. This review summarizes the mechanisms related to the occurrence and development of CVDs associated with three novel acylation modifications: crotonylation, lactylation, and succinylation.
Diseases of the circulatory (Cardiovascular) system
Md. Shaheenur Islam Sumon, Md. Sakib Bin Islam, Md. Sohanur Rahman
et al.
The early detection and prediction of cardiovascular diseases are crucial for reducing the severe morbidity and mortality associated with these conditions worldwide. A multi-headed self-attention mechanism, widely used in natural language processing (NLP), is operated by Transformers to understand feature interactions in feature spaces. However, the relationships between various features within biological systems remain ambiguous in these spaces, highlighting the necessity of early detection and prediction of cardiovascular diseases to reduce the severe morbidity and mortality with these conditions worldwide. We handle this issue with CardioTabNet, which exploits the strength of tab transformer to extract feature space which carries strong understanding of clinical cardiovascular data and its feature ranking. As a result, performance of downstream classical models significantly showed outstanding result. Our study utilizes the open-source dataset for heart disease prediction with 1190 instances and 11 features. In total, 11 features are divided into numerical (age, resting blood pressure, cholesterol, maximum heart rate, old peak, weight, and fasting blood sugar) and categorical (resting ECG, exercise angina, and ST slope). Tab transformer was used to extract important features and ranked them using random forest (RF) feature ranking algorithm. Ten machine-learning models were used to predict heart disease using selected features. After extracting high-quality features, the top downstream model (a hyper-tuned ExtraTree classifier) achieved an average accuracy rate of 94.1% and an average Area Under Curve (AUC) of 95.0%. Furthermore, a nomogram analysis was conducted to evaluate the model's effectiveness in cardiovascular risk assessment. A benchmarking study was conducted using state-of-the-art models to evaluate our transformer-driven framework.
Dmitrii Seletkov, Sophie Starck, Ayhan Can Erdur
et al.
Reliable preclinical disease risk assessment is essential to move public healthcare from reactive treatment to proactive identification and prevention. However, image-based risk prediction algorithms often consider one condition at a time and depend on hand-crafted features obtained through segmentation tools. We propose a whole-body self-supervised representation learning method for the preclinical disease risk assessment under a competing risk modeling. This approach outperforms whole-body radiomics in multiple diseases, including cardiovascular disease (CVD), type 2 diabetes (T2D), chronic obstructive pulmonary disease (COPD), and chronic kidney disease (CKD). Simulating a preclinical screening scenario and subsequently combining with cardiac MRI, it sharpens further the prediction for CVD subgroups: ischemic heart disease (IHD), hypertensive diseases (HD), and stroke. The results indicate the translational potential of whole-body representations as a standalone screening modality and as part of a multi-modal framework within clinical workflows for early personalized risk stratification. The code is available at https://github.com/yayapa/WBRLforCR/
Emanuele Palumbo, Sorawit Saengkyongam, Maria R. Cervera
et al.
Continuous cardiovascular monitoring can play a key role in precision health. However, some fundamental cardiac biomarkers of interest, including stroke volume and cardiac output, require invasive measurements, e.g., arterial pressure waveforms (APW). As a non-invasive alternative, photoplethysmography (PPG) measurements are routinely collected in hospital settings. Unfortunately, the prediction of key cardiac biomarkers from PPG instead of APW remains an open challenge, further complicated by the scarcity of annotated PPG measurements. As a solution, we propose a hybrid approach that uses hemodynamic simulations and unlabeled clinical data to estimate cardiovascular biomarkers directly from PPG signals. Our hybrid model combines a conditional variational autoencoder trained on paired PPG-APW data with a conditional density estimator of cardiac biomarkers trained on labeled simulated APW segments. As a key result, our experiments demonstrate that the proposed approach can detect fluctuations of cardiac output and stroke volume and outperform a supervised baseline in monitoring temporal changes in these biomarkers.
Serverless computing has redefined cloud application deployment by abstracting infrastructure and enabling on-demand, event-driven execution, thereby enhancing developer agility and scalability. However, maintaining consistent application performance in serverless environments remains a significant challenge. The dynamic and transient nature of serverless functions makes it difficult to distinguish between benign and anomalous behavior, which in turn undermines the effectiveness of traditional anomaly detection methods. These conventional approaches, designed for stateful and long-running services, struggle in serverless settings where executions are short-lived, functions are isolated, and observability is limited. In this first comprehensive vision paper on anomaly detection for serverless systems, we systematically explore the unique challenges posed by this paradigm, including the absence of persistent state, inconsistent monitoring granularity, and the difficulty of correlating behaviors across distributed functions. We further examine a range of threats that manifest as anomalies, from classical Denial-of-Service (DoS) attacks to serverless-specific threats such as Denial-of-Wallet (DoW) and cold start amplification. Building on these observations, we articulate a research agenda for next-generation detection frameworks that address the need for context-aware, multi-source data fusion, real-time, lightweight, privacy-preserving, and edge-cloud adaptive capabilities. Through the identification of key research directions and design principles, we aim to lay the foundation for the next generation of anomaly detection in cloud-native, serverless ecosystems.
Accurate diagnosis of Mendelian diseases is crucial for precision therapy and assistance in preimplantation genetic diagnosis. However, existing methods often fall short of clinical standards or depend on extensive datasets to build pretrained machine learning models. To address this, we introduce an innovative LLM-Driven multi-agent debate system (MD2GPS) with natural language explanations of the diagnostic results. It utilizes a language model to transform results from data-driven and knowledge-driven agents into natural language, then fostering a debate between these two specialized agents. This system has been tested on 1,185 samples across four independent datasets, enhancing the TOP1 accuracy from 42.9% to 66% on average. Additionally, in a challenging cohort of 72 cases, MD2GPS identified potential pathogenic genes in 12 patients, reducing the diagnostic time by 90%. The methods within each module of this multi-agent debate system are also replaceable, facilitating its adaptation for diagnosing and researching other complex diseases.
The challenges and rigors of the modern-day health care systems demand a critical reappraisal of our training paradigms in cardiology. Today, modern day DM and DNB Cardiology training needs to seamlessly amalgamate traditional teaching methodologies with the rapidly evolving technology based educational tools now available to us for personalized and adaptive learning. The contemporary cardiology curricula need to incorporate ALL components of clinical competency including cognitive, psychomotor and affective skills to enable the next generation of cardiologists to provide truly holistic care to their patients. In addition, a greater focus on impactful cardiology research with an intent to publish it while in training, is likely to encourage at least some of the young trainees to pursue careers in academia. Most importantly, the exit examination patterns need to be restructured. We need to decide whether we need cardiologists who simply follow textbooks and are trained in procedures —or we need those who have the ability to themselves write the next chapters in cardiology, have the precision of thought, the depth of empathy, and the courage to question.
Surgery, Diseases of the circulatory (Cardiovascular) system
This narrative review is written at the occasion of the “Verstraete Centennial Memorial Lecture” organized on September 15, 2025 at NEUROMED (Pozzilli, Italy) by Giovanni de Gaetano and Maria Benedetta Donati. It represents a historical account of contributions by the CMVB, founded by Marc Verstraete, to our understanding of the regulation of fibrinolysis and the development of thrombolytic therapy.
Diseases of the circulatory (Cardiovascular) system
The circulatory system plays a critical role in maintaining the homeostasis of the body and ensuring general health, as it delivers nutrients, oxygen, and hormones to all parts of the body. However, various diseases that disrupt this complex system significantly affect individual well-being and public health. Conditions such as heart disease, hypertension, and vascular diseases are among the leading causes of morbidity and mortality worldwide. Despite their importance, the classification of circulatory system diseases remains a complex issue in the medical field
Pratham Mehta, Rahul O Narayanan, Harsha Karanth
et al.
Collaborative planning for congenital heart diseases typically involves creating physical heart models through 3D printing, which are then examined by both surgeons and cardiologists. Recent developments in mobile augmented reality (AR) technologies have presented a viable alternative, known for their ease of use and portability. However, there is still a lack of research examining the utilization of multi-user mobile AR environments to support collaborative planning for cardiovascular surgeries. We created ARCollab, an iOS AR app designed for enabling multiple surgeons and cardiologists to interact with a patient's 3D heart model in a shared environment. ARCollab enables surgeons and cardiologists to import heart models, manipulate them through gestures and collaborate with other users, eliminating the need for fabricating physical heart models. Our evaluation of ARCollab's usability and usefulness in enhancing collaboration, conducted with three cardiothoracic surgeons and two cardiologists, marks the first human evaluation of a multi-user mobile AR tool for surgical planning. ARCollab is open-source, available at https://github.com/poloclub/arcollab.
Abstract Background Clinical characteristics and long-term data on the safety and efficacy of LAAC in preventing cerebrovascular accident and thromboembolism among Chinese patients with non-valvular AF (NVAF) remain limited. Methods Data of consecutive NVAF patients who underwent LAAC at Beijing Anzhen Hospital, Capital Medical University, from June 1, 2014, to December 31, 2021, were collected and analyzed retrospectively. The primary effectiveness endpoint was the composite endpoint of stroke/transient ischemic attack, systemic embolism, and death from cardiovascular causes. The primary safety endpoint is the severe bleeding defined by the LAAC Munich consensus. Results Of the 222 patients enrolled, the mean age was 66.90 ± 9.62 years, with a majority being male (77.03%). Many patients are non-paroxysmal AF (71.19%) with a median duration of AF of 4.00 years. The mean CHA2DS2-VASc score was 3.78 ± 1.49, and the mean HAS-BLED score was 1.68 ± 0.86. Thromboembolic events (76.58%) were the most common indication for LAAC. The device, technical, and procedural success rates were 98.65%, 98.65%, and 93.69%, respectively. The anticoagulation continuation rate was 56.36%, 31.25%, and 22.60% at 3-, 6- and 12 months post-procedure, respectively. Throughout a mean 2.81 years of follow-up, the incidence of the primary efficacy endpoint was 4.27 per 100 patient-years, predominantly attributable to stroke/TIA (3.12 per 100 PYs). Five patients experienced major bleeding during the follow-up period. Post-procedure imaging revealed minimal complications, with only one substantial peri-device leak. Device-related thrombus occurred in 2.33% of patients, resolving with anticoagulation. Conclusion The study demonstrates that LAAC is a safe and effective alternative option for Chinese patients with AF, with a high success rate, few complications as well as fewer long-term adverse outcome events.
Diseases of the circulatory (Cardiovascular) system
Naledi Lenah Adam, Grzegorz Kowalik, Andrew Tyler
et al.
BackgroundSimultaneous multi-slice (SMS) bSSFP imaging enables stress myocardial perfusion imaging with high spatial resolution and increased spatial coverage. Standard parallel imaging techniques (e.g., TGRAPPA) can be used for image reconstruction but result in high noise level. Alternatively, iterative reconstruction techniques based on temporal regularization (ITER) improve image quality but are associated with reduced temporal signal fidelity and long computation time limiting their online use. The aim is to develop an image reconstruction technique for SMS-bSSFP myocardial perfusion imaging combining parallel imaging and image-based denoising using a novel noise map estimation network (NoiseMapNet), which preserves both sharpness and temporal signal profiles and that has low computational cost.MethodsThe proposed reconstruction of SMS images consists of a standard temporal parallel imaging reconstruction (TGRAPPA) with motion correction (MOCO) followed by image denoising using NoiseMapNet. NoiseMapNet is a deep learning network based on a 2D Unet architecture and aims to predict a noise map from an input noisy image, which is then subtracted from the noisy image to generate the denoised image. This approach was evaluated in 17 patients who underwent stress perfusion imaging using a SMS-bSSFP sequence. Images were reconstructed with (a) TGRAPPA with MOCO (thereafter referred to as TGRAPPA), (b) iterative reconstruction with integrated motion compensation (ITER), and (c) proposed NoiseMapNet-based reconstruction. Normalized mean squared error (NMSE) with respect to TGRAPPA, myocardial sharpness, image quality, perceived SNR (pSNR), and number of diagnostic segments were evaluated.ResultsNMSE of NoiseMapNet was lower than using ITER for both myocardium (0.045 ± 0.021 vs. 0.172 ± 0.041, p < 0.001) and left ventricular blood pool (0.025 ± 0.014 vs. 0.069 ± 0.020, p < 0.001). There were no significant differences between all methods for myocardial sharpness (p = 0.77) and number of diagnostic segments (p = 0.36). ITER led to higher image quality than NoiseMapNet/TGRAPPA (2.7 ± 0.4 vs. 1.8 ± 0.4/1.3 ± 0.6, p < 0.001) and higher pSNR than NoiseMapNet/TGRAPPA (3.0 ± 0.0 vs. 2.0 ± 0.0/1.3 ± 0.6, p < 0.001). Importantly, NoiseMapNet yielded higher pSNR (p < 0.001) and image quality (p < 0.008) than TGRAPPA. Computation time of NoiseMapNet was only 20s for one entire dataset.ConclusionNoiseMapNet-based reconstruction enables fast SMS image reconstruction for stress myocardial perfusion imaging while preserving sharpness and temporal signal profiles.
Diseases of the circulatory (Cardiovascular) system
The renin-angiotensin system (RAS) is an essential hormonal system involved in water and sodium reabsorption, renal blood flow regulation, and arterial constriction. Systemic stimulation of the RAS with infusion of the main peptide angiotensin II (Ang II) in animals as well as pathological elevation of renin (i.e renovascular hypertension) to increase circulatory Ang II in humans ultimately lead to hypertension and end-organ damage. In addition to hypertension, accumulating evidence support that the Ang II type 1 receptor exerts a critical role in cardiovascular and kidney diseases independent of blood pressure elevation. In the last two decades, the identification of an increased number of peptides and receptors has facilitated the concept that the RAS has both detrimental and beneficial effects on the cardiovascular system depending on which RAS components are activated. For example, angiotensin 1-7 and Ang II type 2 receptors act as a counter-regulatory system against the classical RAS by mediating vasodilation. While the RAS as an endocrine system for regulation of blood pressure is well established, there remain many unanswered questions and controversial findings regarding blood pressure regulation and pathophysiological regulation of cardiovascular diseases at the tissue level. This review article will include the latest knowledge gleaned from cell type-selective gene deleted mice regarding cell type-specific roles of AngII receptors and discuss their significance in health and diseases. In particular, we focus on the roles of these receptors expressed in vascular, cardiac, and kidney epithelial cells.
Pasquale Claudio Africa, Ivan Fumagalli, Michele Bucelli
et al.
Computational fluid dynamics (CFD) is an important tool for the simulation of the cardiovascular function and dysfunction. Due to the complexity of the anatomy, the transitional regime of blood flow in the heart, and the strong mutual influence between the flow and the physical processes involved in the heart function, the development of accurate and efficient CFD solvers for cardiovascular flows is still a challenging task. In this paper we present lifex-cfd, an open-source CFD solver for cardiovascular simulations based on the lifex finite element library, written in modern C++ and exploiting distributed memory parallelism. We model blood flow in both physiological and pathological conditions via the incompressible Navier-Stokes equations, accounting for moving cardiac valves, moving domains, and transition-to-turbulence regimes. In this paper, we provide an overview of the underlying mathematical formulation, numerical discretization, implementation details and examples on how to use lifex-cfd. We verify the code through rigorous convergence analyses, and we show its almost ideal parallel speedup. We demonstrate the accuracy and reliability of the numerical methods implemented through a series of idealized and patient-specific vascular and cardiac simulations, in different physiological flow regimes. The lifex-cfd source code is available under the LGPLv3 license, to ensure its accessibility and transparency to the scientific community, and to facilitate collaboration and further developments.
Shahabedin Nabavi, Hossein Simchi, Mohsen Ebrahimi Moghaddam
et al.
Background and Objectives: Cardiovascular magnetic resonance (CMR) imaging is a powerful modality in functional and anatomical assessment for various cardiovascular diseases. Sufficient image quality is essential to achieve proper diagnosis and treatment. A large number of medical images, the variety of imaging artefacts, and the workload of imaging centres are among the things that reveal the necessity of automatic image quality assessment (IQA). However, automated IQA requires access to bulk annotated datasets for training deep learning (DL) models. Labelling medical images is a tedious, costly and time-consuming process, which creates a fundamental challenge in proposing DL-based methods for medical applications. This study aims to present a new method for CMR IQA when there is limited access to annotated datasets. Methods: The proposed generalised deep meta-learning model can evaluate the quality by learning tasks in the prior stage and then fine-tuning the resulting model on a small labelled dataset of the desired tasks. This model was evaluated on the data of over 6,000 subjects from the UK Biobank for five defined tasks, including detecting respiratory motion, cardiac motion, Aliasing and Gibbs ringing artefacts and images without artefacts. Results: The results of extensive experiments show the superiority of the proposed model. Besides, comparing the model's accuracy with the domain adaptation model indicates a significant difference by using only 64 annotated images related to the desired tasks. Conclusion: The proposed model can identify unknown artefacts in images with acceptable accuracy, which makes it suitable for medical applications and quality assessment of large cohorts.
Masoud Mokhtari, Neda Ahmadi, Teresa S. M. Tsang
et al.
Echocardiography (echo) is an ultrasound imaging modality that is widely used for various cardiovascular diagnosis tasks. Due to inter-observer variability in echo-based diagnosis, which arises from the variability in echo image acquisition and the interpretation of echo images based on clinical experience, vision-based machine learning (ML) methods have gained popularity to act as secondary layers of verification. For such safety-critical applications, it is essential for any proposed ML method to present a level of explainability along with good accuracy. In addition, such methods must be able to process several echo videos obtained from various heart views and the interactions among them to properly produce predictions for a variety of cardiovascular measurements or interpretation tasks. Prior work lacks explainability or is limited in scope by focusing on a single cardiovascular task. To remedy this, we propose a General, Echo-based, Multi-Level Transformer (GEMTrans) framework that provides explainability, while simultaneously enabling multi-video training where the inter-play among echo image patches in the same frame, all frames in the same video, and inter-video relationships are captured based on a downstream task. We show the flexibility of our framework by considering two critical tasks including ejection fraction (EF) and aortic stenosis (AS) severity detection. Our model achieves mean absolute errors of 4.15 and 4.84 for single and dual-video EF estimation and an accuracy of 96.5 % for AS detection, while providing informative task-specific attention maps and prototypical explainability.
L Sreenivasamurthy, Vinod Mittal, Pramod Joshi
et al.
Objective: The objective of this study was to assess current practices and usage patterns of antihypertensive medications in managing hypertension (HTN) in India.
Materials and Methods: A cross-sectional, observational digital study was conducted among health-care practitioners (HCPs) across India (November 2022–March 2023).
Results: A total of 792 HCPs (cardiologists, consulting, and general physicians) participated in this study. According to 63.38% of HCPs, 20–50 essential HTN patients seek consultation weekly. The majority of patients were in the age range of 40–60 years (84.09%). A total of 67.55% of HCPs mentioned that systolic blood pressure (BP) ranged between 140 and 160 mmHg. Type 2 diabetes mellitus was the most common comorbidity among hypertensive patients (84.09%). In total, 53.79% and 37.37% of HCPs preferred angiotensin receptor blockers (ARBs)/angiotensin-converting enzyme and calcium channel blockers (CCBs) as the first choice of antihypertensive agents. The majority of HCPs (>69%) preferred prescribing a triple-drug fixed-dose combination (FDC) of CCBs + ARB + diuretics in hypertensive patients with coronary artery disease (CAD) and resistant HTN. The majority (89.90%) of HCPs preferred prescribing FDC of CCBs over monotherapy. Amlodipine (>55%) followed by cilnidipine (>32%) were preferred CCBs for young and older hypertensive patients. In patients with essential HTN without any CVD, amlodipine (72.85%) was the preferred CCB. Vascular selectivity (59.09%) and longer half-life (54.55%) were important factors for prescribing CCBs. Resistant HTN, inadequate BP control with monotherapy, and the presence of CAD were all (62.63%) important considerations for FDC with CCBs. The underutilization of CCBs in managing HTN (63.88%) was highlighted.
Conclusion: Overall responses provide a comprehensive overview of the prevailing perception and the usage patterns of antihypertensive agents employed by HCPs in India.
Diseases of the circulatory (Cardiovascular) system