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Background: According to World Health Organization (WHO) estimates, there are currently 1.1 billion tobacco smokers worldwide. This study follows the need for change in the increasing trend of lifestyle-related diseases and the lack of extensive studies on the pattern of smoking. Methods: In the current study, an electronic database search was conducted to identify studies that examined the impact of smoking on internal diseases from the beginning of February 2018 to the end of December 2021. After eliminating numerous articles based on their titles and abstracts, 273 articles pertinent to the study's objectives were selected. Due to the inadequacy of the target audience and the inclusion and exclusion criteria, 239 of the remaining articles were eliminated. The 44 remaining articles were examined more closely. Results: Each cigarette produces more than 7,000 chemicals. Many of these substances are toxic, and about 69 of them can cause cancer. For every 15 cigarettes you smoke, a mutation occurs in the body. Mutations are the cause of cancer. Studies have shown a clear relationship between dose and response, with a sharp increase in the risk of arterial disease in heavy smokers. In countries where approximately 30% of the population smokes, 50% of arterial disease can be attributed to smoking. Conclusion: Smoking increases the risk of cardiovascular disease (CVD) and lung disease, and leads to an increased risk of lung, throat, stomach, and bladder cancer, and many other cancers. One of the most important organs in the body that can be disrupted by smoking is the heart.

Growing evidence has demonstrated exercise as an effective way to promote cardiovascular health and protect against cardiovascular diseases However, the underlying mechanisms of the beneficial effects of exercise have yet to be elucidated. Animal exercise studies are widely used to investigate the key mechanisms of exercise-induced cardiovascular protection. However, standardized procedures and well-established evaluation indicators for animal exercise models are needed to guide researchers in carrying out effective, high-quality animal studies using exercise to prevent and treat cardiovascular diseases. In our review, we present the commonly used animal exercise models in cardiovascular research and propose a set of standard procedures for exercise training, emphasizing the appropriate measurements and analysis in these chronic exercise models. We also provide recommendations for optimal design of animal exercise studies in cardiovascular research, including the choice of exercise models, control of exercise protocols, exercise at different stages of disease, and other considerations, such as age, sex, and genetic background. We hope that this position paper will promote basic research on exercise-induced cardiovascular protection and pave the way for successful translation of exercise studies from bench to bedside in the prevention and treatment of cardiovascular diseases.

Abstract Background People who have been incarcerated have high rates of cardiovascular risk factors, such as hypertension and smoking, and cardiovascular disease (CVD) is a leading cause of hospitalizations and mortality in this population. Despite this, little is known regarding what pathways mediate the association between incarceration exposure and increased rates of CVD morbidity and especially what incarceration specific factors are associated with this risk. The objective of this study is to better understand CVD risk in people exposed to incarceration and the pathways by which accumulate cardiovascular risk over time. Methods and Analysis The Justice-Involved Individuals Cardiovascular Disease Epidemiology (JUSTICE) study is a prospective cohort study of individuals released from incarceration with known cardiovascular risk factors. We are recruiting 500 individuals within three months after release from jail/prison. At baseline we are assessing traditional risk factors for CVD, including diet, exercise, and smoking, and exposure to incarceration-related policies, psychosocial stress, and self-efficacy. Cardiovascular risk factors are measured at baseline through point of care testing. We are following these individuals for the 12 months following the index release from incarceration with re-evaluation of psychosocial factors and clinical risk factors every 6 months. Using these data, we will estimate the direct and indirect latent effects of incarceration on cardiovascular risk factors and the paths via which these effects are mediated. We will also model the anticipated 10-year burden of CVD incidence, health care use, and mortality associated with incarceration. Discussion Our study will identify factors associated with CVD risk factor control among people released from incarceration. Our measurement of incarceration-related exposures, psychosocial factors, and clinical measures of cardiovascular risk will allow for identification of unique targets for intervention to modify CVD risk in this vulnerable population.

• Standard procedures and appropriate assessment of exercise are proposed for the commonly used animal models related to chronic exercise (e.g., treadmill running, voluntary wheel running, swimming exercise, and resistance exercise) in cardiovascular research. • Optimal design of animal exercise studies in cardiovascular research should consider the choice of exercise models, control of exercise protocols, exercise at different stages of disease, and other factors, such as age, sex, and genetic background. • An optimal design for studying exercise-induced physiological cardiac growth and its related beneficial effects against cardiovascular diseases is presented. Growing evidence has demonstrated exercise as an effective way to promote cardiovascular health and protect against cardiovascular diseases However, the underlying mechanisms of the beneficial effects of exercise have yet to be elucidated. Animal exercise studies are widely used to investigate the key mechanisms of exercise-induced cardiovascular protection. However, standardized procedures and well-established evaluation indicators for animal exercise models are needed to guide researchers in carrying out effective, high-quality animal studies using exercise to prevent and treat cardiovascular diseases. In our review, we present the commonly used animal exercise models in cardiovascular research and propose a set of standard procedures for exercise training, emphasizing the appropriate measurements and analysis in these chronic exercise models. We also provide recommendations for optimal design of animal exercise studies in cardiovascular research, including the choice of exercise models, control of exercise protocols, exercise at different stages of disease, and other considerations, such as age, sex, and genetic background. We hope that this position paper will promote basic research on exercise-induced cardiovascular protection and pave the way for successful translation of exercise studies from bench to bedside in the prevention and treatment of cardiovascular diseases. [Display omitted] [ABSTRACT FROM AUTHOR]

Heart disease can be life-threatening if not detected and treated at an early stage. The electrocardiogram (ECG) plays a vital role in classifying cardiovascular diseases, and often physicians and medical researchers examine paper-based ECG images for cardiac diagnosis. An automated heart disease prediction system might help to classify heart diseases accurately at an early stage. This study aims to classify cardiac diseases into five classes with paper-based ECG images using a deep learning approach with the highest possible accuracy and the lowest possible time complexity. This research consists of two approaches. In the first approach, five deep learning models, InceptionV3, ResNet50, MobileNetV2, VGG19, and DenseNet201, are employed. In the second approach, an integrated deep learning model (InRes-106) is introduced, combining InceptionV3 and ResNet50. This model is developed as a deep convolutional neural network capable of extracting hidden and high-level features from images. An ablation study is conducted on the proposed model altering several components and hyperparameters, improving the performance even further. Before training the model, several image pre-processing techniques are employed to remove artifacts and enhance the image quality. Our proposed hybrid InRes-106 model performed best with a testing accuracy of 98.34%. The InceptionV3 model acquired a testing accuracy of 90.56%, the ResNet50 89.63%, the DenseNet201 88.94%, the VGG19 87.87%, and the MobileNetV2 achieved 80.56% testing accuracy. The model is trained with a k-fold cross-validation technique with different k values to evaluate the robustness further. Although the dataset contains a limited number of complex ECG images, our proposed approach, based on various image pre-processing techniques, model fine-tuning, and ablation studies, can effectively diagnose cardiac diseases.

Despite significant advances in the diagnosis and treatment of cardiovascular diseases, recent calls have emphasized the unmet need to improve precision-based approaches in cardiovascular disease. Although some studies provide preliminary evidence of the diagnostic and prognostic potential of circulating coding and non-coding RNAs, the complex RNA biology and lack of standardization have hampered the translation of these markers into clinical practice. In this position paper of the CardioRNA COST action CA17129, we provide recommendations to standardize the RNA development process in order to catalyse efforts to investigate novel RNAs for clinical use. We list the unmet clinical needs in cardiovascular disease, such as the identification of high-risk patients with ischaemic heart disease or heart failure who require more intensive therapies. The advantages and pitfalls of the different sample types, including RNAs from plasma, extracellular vesicles, and whole blood, are discussed in the sample matrix, together with their respective analytical methods. The effect of patient demographics and highly prevalent comorbidities, such as metabolic disorders, on the expression of the candidate RNA is presented and should be reported in biomarker studies. We discuss the statistical and regulatory aspects to translate a candidate RNA from a research use only assay to an in-vitro diagnostic test for clinical use. Optimal planning of this development track is required, with input from the researcher, statistician, industry, and regulatory partners. [ABSTRACT FROM AUTHOR]

Background: According to World Health Organization (WHO) estimates, there are currently 1.1 billion tobacco smokers worldwide. This study follows the need for change in the increasing trend of lifestyle-related diseases and the lack of extensive studies on the pattern of smoking. Methods: In the current study, an electronic database search was conducted to identify studies that examined the impact of smoking on internal diseases from the beginning of February 2018 to the end of December 2021. After eliminating numerous articles based on their titles and abstracts, 273 articles pertinent to the study's objectives were selected. Due to the inadequacy of the target audience and the inclusion and exclusion criteria, 239 of the remaining articles were eliminated. The 44 remaining articles were examined more closely. Results: Each cigarette produces more than 7,000 chemicals. Many of these substances are toxic, and about 69 of them can cause cancer. For every 15 cigarettes you smoke, a mutation occurs in the body. Mutations are the cause of cancer. Studies have shown a clear relationship between dose and response, with a sharp increase in the risk of arterial disease in heavy smokers. In countries where approximately 30% of the population smokes, 50% of arterial disease can be attributed to smoking. Conclusion: Smoking increases the risk of cardiovascular disease (CVD) and lung disease, and leads to an increased risk of lung, throat, stomach, and bladder cancer, and many other cancers. One of the most important organs in the body that can be disrupted by smoking is the heart. [ABSTRACT FROM AUTHOR]

The cardiovascular system is significantly affected in coronavirus disease-19 (COVID-19). Microvascular injury, endothelial dysfunction, and thrombosis resulting from viral infection or indirectly related to the intense systemic inflammatory and immune responses are characteristic features of severe COVID-19. Pre-existing cardiovascular disease and viral load are linked to myocardial injury and worse outcomes. The vascular response to cytokine production and the interaction between severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and angiotensin-converting enzyme 2 receptor may lead to a significant reduction in cardiac contractility and subsequent myocardial dysfunction. In addition, a considerable proportion of patients who have been infected with SARS-CoV-2 do not fully recover and continue to experience a large number of symptoms and post-acute complications in the absence of a detectable viral infection. This conditions often referred to as 'post-acute COVID-19' may have multiple causes. Viral reservoirs or lingering fragments of viral RNA or proteins contribute to the condition. Systemic inflammatory response to COVID-19 has the potential to increase myocardial fibrosis which in turn may impair cardiac remodelling. Here, we summarize the current knowledge of cardiovascular injury and post-acute sequelae of COVID-19. As the pandemic continues and new variants emerge, we can advance our knowledge of the underlying mechanisms only by integrating our understanding of the pathophysiology with the corresponding clinical findings. Identification of new biomarkers of cardiovascular complications, and development of effective treatments for COVID-19 infection are of crucial importance. [ABSTRACT FROM AUTHOR]

Heart disease can be life-threatening if not detected and treated at an early stage. The electrocardiogram (ECG) plays a vital role in classifying cardiovascular diseases, and often physicians and medical researchers examine paper-based ECG images for cardiac diagnosis. An automated heart disease prediction system might help to classify heart diseases accurately at an early stage. This study aims to classify cardiac diseases into five classes with paper-based ECG images using a deep learning approach with the highest possible accuracy and the lowest possible time complexity. This research consists of two approaches. In the first approach, five deep learning models, InceptionV3, ResNet50, MobileNetV2, VGG19, and DenseNet201, are employed. In the second approach, an integrated deep learning model (InRes-106) is introduced, combining InceptionV3 and ResNet50. This model is developed as a deep convolutional neural network capable of extracting hidden and high-level features from images. An ablation study is conducted on the proposed model altering several components and hyperparameters, improving the performance even further. Before training the model, several image pre-processing techniques are employed to remove artifacts and enhance the image quality. Our proposed hybrid InRes-106 model performed best with a testing accuracy of 98.34%. The InceptionV3 model acquired a testing accuracy of 90.56%, the ResNet50 89.63%, the DenseNet201 88.94%, the VGG19 87.87%, and the MobileNetV2 achieved 80.56% testing accuracy. The model is trained with a k-fold cross-validation technique with different k values to evaluate the robustness further. Although the dataset contains a limited number of complex ECG images, our proposed approach, based on various image pre-processing techniques, model fine-tuning, and ablation studies, can effectively diagnose cardiac diseases. [ABSTRACT FROM AUTHOR]

Introduction and aim. Antiplatelet therapy constitutes a crucial part of cardiovascular disease (CVD) prevention. Although CVDs were long associated with men, we now know they critically affect women. Nevertheless, there is insufficient and inconsistent data on the effect of monotherapy with aspirin or P2Y12 inhibitor and of dual antiplatelet therapy on females. This review was conducted to summarize the current knowledge and to assess whether sex-specific antiplatelet therapy is required. Material and methods. A literature search was performed using PubMed and Mendeley. Evaluation of abstracts from databases was followed by detailed analysis of 60 papers. It was discussed i) how sex affects platelet biology and response to antiplatelet agents, ii) how sex and gender differences translate into clinical challenges and iii) how women's cardiological care might be improved. Analysis of literature. Evidence from multiple trials suggest that women and men exhibit heterogenous baseline platelet reactivity and respond to antiplatelet therapy in a different way, which is reflected by laboratory results and clinical outcomes. Sex disparities in diagnostic process, medicine prescription, therapy course and clinical outcomes have been reported, suggesting that women suffering from CVD may not receive equitable care. Conclusion. Further investigations with appropriate representation of women are required to understand the complex nature of sex-specific platelet response. As far as guidelines recommend the same treatment strategies in both sexes, there is a need to advocate equality in management of females and males with CVD. [ABSTRACT FROM AUTHOR]

Background: The incidence and mortality rates of cardiovascular disease worldwide are a major concern in the healthcare industry. Precise prediction of cardiovascular disease is essential, and the use of machine learning and deep learning can aid in decision-making and enhance predictive abilities. Objective: The goal of this paper is to introduce a model for precise cardiovascular disease prediction by combining machine learning and deep learning. Method: Two public heart disease classification datasets with 70,000 and 1190 records besides a locally collected dataset with 600 records were used in our experiments. Then, a model which makes use of both machine learning and deep learning was proposed in this paper. The proposed model employed CNN and LSTM, as the representatives of deep learning models, besides KNN and XGB, as the representatives of machine learning models. As each classifier defined the output classes, majority voting was then used as an ensemble learner to predict the final output class. Result: The proposed model obtained the highest classification performance based on all evaluation metrics on all datasets, demonstrating its suitability and reliability in forecasting the probability of cardiovascular disease. [ABSTRACT FROM AUTHOR]

Introduction: Degenerative cervical myelopathy (DCM) is a form of chronic spinal cord injury, with a natural history of potential for progression over time. Whilst driven by mechanical stress on the spinal cord from degenerative and congenital pathology, the neurological phenotype of DCM is likely to be modified by multiple systemic factors. The role of metabolic factors is therefore of interest, particularly given that ischaemia is considered a key pathological mechanism of spinal cord injury. The objective was therefore to synthesise current evidence on the effect of metabolism on DCM susceptibility, severity, and surgical outcomes. Methods: A systematic review in MEDLINE and Embase was conducted following PRISMA guidelines. Full-text papers in English, with a focus on DCM and metabolism, including diabetes, cardiovascular disease, anaemia, and lipid profile, were eligible for inclusion. Risk of methodological bias was assessed using the Joanna Briggs Institute (JBI) critical assessment tools. Quality assessments were performed using the GRADE assessment tool. Patient demographics, metabolic factors and the relationships between metabolism and spinal cord disease, spinal column disease and post-operative outcomes were assessed. Results: In total, 8,523 papers were identified, of which 57 met criteria for inclusion in the final analysis. A total of 91% (52/57) of included papers assessed the effects of diabetes in relation to DCM, of which 85% (44/52) reported an association with poor surgical outcomes; 42% of papers (24/57) discussed the association between cardiovascular health and DCM, of which 88% (21/24) reported a significant association. Overall, DCM patients with diabetes or cardiovascular disease experienced greater perioperative morbidity and poorer neurological recovery. They were also more likely to have comorbidities such as obesity and hyperlipidaemia. Conclusion: Metabolic factors appear to be associated with surgical outcomes in DCM. However, evidence for a more specific role in DCM susceptibility and severity is uncertain. The pathophysiology and natural history of DCM are critical research priorities; the role of metabolism is therefore a key area for future research focus. [ABSTRACT FROM AUTHOR]

Cardiovascular disease is the leading cause of global mortality and responsible for millions of deaths annually. The mortality rate and overall consequences of cardiac disease can be reduced with early disease detection. However, conventional diagnostic methods encounter various challenges, including delayed treatment and misdiagnoses, which can impede the course of treatment and raise healthcare costs. The application of artificial intelligence (AI) techniques, especially machine learning (ML) algorithms, offers a promising pathway to address these challenges. This paper emphasizes the central role of machine learning in cardiac health and focuses on precise cardiovascular disease prediction. In particular, this paper is driven by the urgent need to fully utilize the potential of machine learning to enhance cardiovascular disease prediction. In light of the continued progress in machine learning and the growing public health implications of cardiovascular disease, this paper aims to offer a comprehensive analysis of the topic. This review paper encompasses a wide range of topics, including the types of cardiovascular disease, the significance of machine learning, feature selection, the evaluation of machine learning models, data collection & preprocessing, evaluation metrics for cardiovascular disease prediction, and the recent trends & suggestion for future works. In addition, this paper offers a holistic view of machine learning's role in cardiovascular disease prediction and public health. We believe that our comprehensive review will contribute significantly to the existing body of knowledge in this essential area. [ABSTRACT FROM AUTHOR]

Aims: Standard outcome sets enable the value-based evaluation of health care delivery. Whereas the attainment of expert opinion has been structured using methods such as the modified-Delphi process, standardized guidelines for extraction of candidate outcomes from literature are lacking. As such, we aimed to describe an approach to obtain a comprehensive list of candidate outcomes for potential inclusion in standard outcome sets. Methods: This study describes an iterative saturation approach, using randomly selected batches from a systematic literature search to develop a long list of candidate outcomes to evaluate healthcare. This approach can be preceded with an optional benchmark review of relevant registries and Clinical Practice Guidelines and data visualization techniques (e.g. as a WordCloud) to potentially decrease the number of iterations. The development of the International Consortium of Health Outcome Measures Heart valve disease set is used to illustrate the approach. Batch cutoff choices of the iterative saturation approach were validated using data of 1000 simulated cases. Results: Simulation showed that on average 98% (range 92–100%) saturation is reached using a 100-article batch initially, with 25 articles in the subsequent batches. On average 4.7 repeating rounds (range 1–9) of 25 new articles were necessary to achieve saturation if no outcomes are first identified from a benchmark review or a data visualization. Conclusion: In this paper a standardized approach is proposed to identify relevant candidate outcomes for a standard outcome set. This approach creates a balance between comprehensiveness and feasibility in conducting literature reviews for the identification of candidate outcomes. [ABSTRACT FROM AUTHOR]

Arterial stiffness has been proved to be an important parameter in the evaluation of cardiovascular diseases, and Pulse Wave Velocity (PWV) is a strong indicator of arterial stiffness. Compared to regional PWV (PWV among different arteries), local PWV (PWV within a single artery) outstands in providing higher precision in indicating arterial properties, as regional PWVs are highly affected by multiple parameters, e.g., variations in blood vessel lengths due to individual differences, and multiple reflection effects on the pulse waveform. However, local PWV is less-developed due to its high dependency on the temporal resolution in synchronized signals with usually low signal-to-noise ratios. This paper presents a method for the noninvasive simultaneous measurement of two local PWVs in both left and right radial arteries based on the Fiber Bragg Grating (FBG) technique via correlation analysis of the pulse pairs at the fossa cubitalis and at the wrist. Based on the measurements of five male volunteers at the ages of 19 to 21 years old, the average left radial PWV ranged from 9.44 m/s to 12.35 m/s and the average right radial PWV ranged from 11.50 m/s to 14.83 m/s. What is worth mentioning is that a stable difference between the left and right radial PWVs was observed for each volunteer, ranging from 2.27 m/s to 3.04 m/s. This method enables the dynamic analysis of local PWVs and analysis of their features among different arteries, which will benefit the diagnosis of early-stage arterial stiffening and may bring more insights into the diagnosis of cardiovascular diseases. [ABSTRACT FROM AUTHOR]

Background: In the context of a move to universal health coverage, three separate systematic reviews were conducted to summarise available evidence on the direct costs of interventions for type 2 diabetes mellitus, hypertension, and cardiovascular disease in South Africa. Methods: PubMed® and Web of Science was searched for literature published between 01 and 1995 and 27 October 2022. Additionally, reference and citations lists of retrieved articles and experts were consulted. We also tracked reference lists of previous, related systematic reviews. Eligible publications were cost analyses of clinical interventions targeted at adults age 15 + reporting primary estimates of in- and out-of-hospital costs from a provider perspective. Costs were extracted and converted to 2021 US dollars, and article methodological and reporting quality was appraised using the 2013 CHEERS checklist. Results: Of the 600, 1,172 and 1,466 identified publications for type 2 diabetes mellitus, hypertension, and cardiovascular disease, respectively, 10, 12, and 17 met full inclusion criteria. 60% of articles reported cardiovascular disease costs, 52% were of good reporting quality, and 10%, 50%, and 39% of type 2 diabetes mellitus, hypertension and cardiovascular disease papers reported private-sector costs only. Hypertension drug costs ranged from $2 to $85 per person-month, while type 2 diabetes mellitus drug costs ranged between $57 and $630 per person-year (ppy). Diabetes-related complication treatment costs ranged from $55 for retinopathy treatment to $25,193 ppy for haemodialysis, while cardiovascular disease treatment costs were between $160 and $37,491 ppy. Drugs and treatment of complications were major cost drivers for hypertension and type 2 diabetes mellitus, while hospitalisation drove cardiovascular disease costs. Conclusion: The intervention costs of type 2 diabetes mellitus, hypertension and cardiovascular disease care have received more attention recently, particularly diabetes-related complications and cardiovascular disease. However, 39% of identified cardiovascular disease treatment costs used a private sector perspective, leaving significant research gaps in the public sector and the cheaper to treat hypertension and type 2 diabetes mellitus. This review fills an information gap regarding the intervention costs of these diseases in South Africa. [ABSTRACT FROM AUTHOR]

Background: This study explores the intricate relationship between Chronic Kidney Disease (CKD), the Triglyceride-Glucose (TyG) index, and Cardiovascular Disease (CVD) in a U.S. adult population. It focuses on understanding how the TyG index, as a marker of insulin resistance, relates to cardiovascular risk in the presence of CKD. Methods: A cross-sectional analysis was conducted using data from the National Health and Nutrition Examination Survey (NHANES) spanning from 1999 to 2018. The study involved classifying stages of CKD, calculating the TyG index, and applying statistical analyses including logistic regression. Results: The investigation revealed that individuals with CKD, who comprised 49% males with an average age of 47 years, exhibited a higher incidence of CVD. The study demonstrated that before adjusting for the TyG index, the odds ratio (OR) for the association between CKD and CVD was 1.77. Importantly, the TyG index was found to mediate 10% of the association between CKD and CVD. Moreover, a significant synergistic interaction was observed between a high TyG index and CKD, with the combined presence of these conditions increasing the risk ratio for CVD to 3.01. Conclusion: The findings highlight the crucial role of insulin resistance in the link between CKD and CVD. The paper discusses the implications of chronic inflammation and endothelial dysfunction in CKD patients and the importance of the TyG index in assessing cardiovascular risk. [ABSTRACT FROM AUTHOR]

Cardio‐cerebrovascular disease (CCVD) is a serious threat to huma strategy to prevent the occurrence and development of disease by giving electroacupuncture intervention before the disease occurs. EAP has been shown in many preclinical studies to relieve ischemic symptoms and improve damage from ischemia–reperfusion, with no comprehensive review of its mechanisms in cardiovascular disease yet. In this paper, we first systematically discussed the meridian and acupoint selection law of EAP for CCVD and focused on the progress of the mechanism of action of EAP for the prevention and treatment of CCVD. As a result, in preclinical studies, AMI and MCAO models are commonly used to simulate ischemic injury in CCVD, while MIRI and CI/RI models are used to simulate reperfusion injury caused by blood flow recovery after focal tissue ischemia. According to the meridian matching rules of EAP for CCVD, PC6 in the pericardial meridian is the most commonly used acupoint in cardiovascular diseases, while GV20 in the Du meridian is the most commonly used acupoint in cerebrovascular diseases. In terms of intervention parameters, EAP intervention generally lasts for 30 min, with acupuncture depths mostly between 1.5 and 5 mm, stimulation intensities mostly at 1 mA, and commonly used frequencies being low frequencies. In terms of molecular mechanisms, the key pathways of EAP in preventing and treating cardiovascular and cerebrovascular diseases are partially similar. EAP can play a protective role in cardiovascular and cerebrovascular diseases by promoting autophagy, regulating Ca2+ overload, and promoting vascular regeneration through anti‐inflammatory reactions, antioxidant stress, and anti‐apoptosis. Of course, both pathways involved have their corresponding specificities. When using EAP to prevent and treat cardiovascular diseases, it involves the metabolic pathway of glutamate, while when using EAP to prevent and treat cerebrovascular diseases, it involves the homeostasis of the blood–brain barrier and the release of neurotransmitters and nutritional factors. I hope these data can provide experimental basis and reference for the clinical promotion and application of EAP in CCVD treatment. [ABSTRACT FROM AUTHOR]

Physical activity and regular exercise are well known to reduce the risks of cerebrovascular and cardiovascular diseases, leading the American College of Sports Medicine to endorse the concept that "exercise is medicine". However, a single bout of exercise temporarily raises arterial blood pressure (BP) to meet the metabolic demands of working muscle, and this BP response is particularly exaggerated in older adults and patients with cardiovascular conditions, such as hypertension, resulting in an exaggerated BP response during exercise. This presents a paradox: while regular exercise is crucial for preventing these diseases, excessively high BP responses during exercise could increase the risk of vascular damage. The mechanisms underlying this exaggerated BP response during exercise remain unclear, and effective exercise regimens for these populations have yet to be established. Currently, low-intensity exercise is recommended; however, its efficacy in disease prevention is uncertain. Notably, even among healthy individuals, there is significant variation in the BP response to exercise. Some healthy individuals, despite having normal resting BP, exhibit an exaggerated BP response during physical activity. Importantly, these individuals are often unaware that their BP becomes excessively elevated during physical activity. Repeated exposure to these heightened BP responses through regular physical activity may increase their long-term risk of cardiovascular disease. How can we prevent disease development in these individuals while still ensuring the effectiveness of exercise? Some studies have shown that individuals with a family history of hypertension may experience this phenomenon even in children and adolescents. Additionally, left ventricular hypertrophy contributes to an exaggerated BP response to exercise, suggesting a possible genetic influence. Conversely, other reports indicate that factors such as arterial stiffness, obesity, and low exercise capacity also contribute to this exaggerated response. Our recent preliminary data suggest that the cognitive benefits of exercise may be diminished in individuals who exhibit an exaggerated BP response during exercise. This implies that individuals with an exaggerated BP response, despite having normal resting BP, may not fully benefit from exercise. In this perspective paper, we review the physiological aspects of this phenomenon and explore strategies to address it. Additionally, we discuss BP responses in athletes within this content. Our goal is to prevent disease while maximizing the benefits of exercise for healthy individuals with an exaggerated BP response, as well as for elderly and cardiovascular patients. [ABSTRACT FROM AUTHOR]

Background and Objectives: Metabolic syndrome (MetS) is a condition marked by a complex array of physiological, biochemical, and metabolic abnormalities, including central obesity, insulin resistance, high blood pressure, and dyslipidemia (characterized by elevated triglycerides and reduced levels of high-density lipoproteins). The pathogenesis develops from the accumulation of lipid droplets in the hepatocyte (steatosis). This accumulation, in genetically predisposed subjects and with other external stimuli (intestinal dysbiosis, high caloric diet, physical inactivity, stress), activates the production of pro-inflammatory molecules, alter autophagy, and turn on the activity of hepatic stellate cells (HSCs), provoking the low grade chronic inflammation and the fibrosis. This syndrome is associated with a significantly increased risk of developing type 2 diabetes mellitus (T2D), cardiovascular diseases (CVD), vascular, renal, pneumologic, rheumatological, sexual, cutaneous syndromes and overall mortality, with the risk rising five- to seven-fold for T2DM, three-fold for CVD, and one and a half–fold for all-cause mortality. The purpose of this narrative review is to examine metabolic syndrome as a "systemic disease" and its interaction with major internal medicine conditions such as CVD, diabetes, renal failure, and respiratory failure. It is essential for internal medicine practitioners to approach this widespread condition in a "holistic" rather than a fragmented manner, particularly in Western countries. Additionally, it is important to be aware of the non-invasive tools available for assessing this condition. Materials and Methods: We conducted an exhaustive search on PubMed up to July 2024, focusing on terms related to metabolic syndrome and other pathologies (heart, Lung (COPD, asthma, pulmonary hypertension, OSAS) and kidney failure, vascular, rheumatological (osteoarthritis, rheumatoid arthritis), endocrinological, sexual pathologies and neoplastic risks. The review was managed in accordance with the PRISMA statement. Finally, we selected 300 studies (233 papers for the first search strategy and 67 for the second one). Our review included studies that provided insights into metabolic syndrome and non-invasive techniques for evaluating liver fibrosis and steatosis. Studies that were not conducted on humans, were published in languages other than English, or did not assess changes related to heart failure were excluded. Results: The findings revealed a clear correlation between metabolic syndrome and all the pathologies above described, indicating that non-invasive assessments of hepatic fibrosis and steatosis could potentially serve as markers for the severity and progression of the diseases. Conclusions: Metabolic syndrome is a multisystem disorder that impacts organs beyond the liver and disrupts the functioning of various organs. Notably, it is linked to a higher incidence of cardiovascular diseases, independent of traditional cardiovascular risk factors. Non-invasive assessments of hepatic fibrosis and fibrosis allow clinicians to evaluate cardiovascular risk. Additionally, the ability to assess liver steatosis may open new diagnostic, therapeutic, and prognostic avenues for managing metabolic syndrome and its complications, particularly cardiovascular disease, which is the leading cause of death in these patients. [ABSTRACT FROM AUTHOR]

Active commuting (AC) may have the potential to prevent the incidence of cardiovascular disease (CVD). However, the evidence for a correlation between AC and the risk of CVD remains uncertain. The current study thoroughly and qualitatively summarized research on the relationship between AC and the risk of CVD disease. From conception through December 2022, researchers explored four databases (PubMed, PEDro, Cochrane, and Bibliothèque Nationale of Luxembourg [BnL]) for observational studies. The initial findings of the search yielded 1042 references. This systematic review includes five papers with 491,352 participants between 16 and 85 years old, with 5 to 20 years of follow-up period. The exposure variable was the mode of transportation used to commute on a typical day (walking, cycling, mixed mode, driving, or taking public transportation). The primary outcome measures were incident CVD, fatal and non-fatal (e.g., ischemic heart disease (IHD), ischemic stroke (IS), hemorrhagic stroke (HS) events, and coronary heart disease (CHD). Despite methodological variability, the current evidence supports AC as a preventive measure for the development of CVD. Future research is needed to standardize methodologies and promote policies for public health and environmental sustainability. [ABSTRACT FROM AUTHOR]

Simple Summary: Salt, or sodium chloride, is a crucial component of a balanced diet. However, an excessive consumption of salt can lead to a range of adverse health effects that are often overlooked. Salt enters the body's digestive system through the ingestion of everyday foods and exerts an effect on a key component of the digestive system, namely the intestinal microbiota. Some studies have indicated that alterations in the composition of the intestinal microbiota may influence the functioning of the human body. This paper presents a review of the effects of a high-salt diet on the gut microbiome and, in turn, on the development of cardiovascular disease and inflammatory bowel disease. Furthermore, it synthesizes recent research on the effects of high-salt diets on the gut microbiome and the gut immune system. This further elucidates the mechanisms through which a high-salt diet affects the gut microbiome and associated diseases. Salt, or sodium chloride, is an essential component of the human diet. Recent studies have demonstrated that dietary patterns characterized by a high intake of salt can influence the abundance and diversity of the gut microbiota, and may play a pivotal role in the etiology and exacerbation of certain diseases, including inflammatory bowel disease and cardiovascular disease. The objective of this review is to synthesize the effects of elevated salt consumption on the gut microbiota, including its influence on gut microbial metabolites and the gut immune system. Additionally, this review will investigate the potential implications of these effects for the development of cardiovascular disease and inflammatory bowel disease. The findings of this study offer novel insights and avenues for the management of two common conditions with significant clinical implications. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases (CVD) are a prominent source of death across the globe, and these deaths are taking place in low-to middle-income nations. Due to this, CVD prevention is a pressing issue that has already been the subject of extensive research. Innovative methodologies in machine learning (ML) can have a greater impact on the diagnosis of CVD, yet the research on CVD is more challenging and attracting more research indeed. In this paper, we investigate the differences between four distinct machine learning models, support vector machine (SVM), logistic regression, decision trees (DT), and artificial neural networks (ANN) in their classification accuracy and possible practicality in CVD classification. techniques such as ensemble learning and other model-specific optimizations are not part of this study, but more basic implementations of the various models were used. To implement abovementioned ML models, a subset of 14 features from the original heart disease dataset is considered and deemed relevant for classification where no individual feature data are missing. From the results, it is observed that there is no clear winner in the comparison of models. There is no significant difference in the average accuracy of models. The highest average hit rate is observed in SVM and ANN, however it is slightly lower in ANN. Even though the DT had lower accuracy, the fully trained model can be easily visualized and interpreted by humans. Hence, the DT is possibly the most practical model to use as a complement to doctors in their current methods of diagnosis. [ABSTRACT FROM AUTHOR]

A growing body of literature has attempted to characterize how traffic-related air pollution (TRAP) affects molecular and subclinical biological processes in ways that could lead to cardiorespiratory disease. To provide a streamlined synthesis of what is known about the multiple mechanisms through which TRAP could lead to cardiorespiratory pathology, we conducted a systematic review of the epidemiological literature relating TRAP exposure to methylomic, proteomic, and metabolomic biomarkers in adult populations. Using the 139 papers that met our inclusion criteria, we identified the omic biomarkers significantly associated with short- or long-term TRAP and used these biomarkers to conduct pathway and network analyses. We considered the evidence for TRAP-related associations with biological pathways involving lipid metabolism, cellular energy production, amino acid metabolism, inflammation and immunity, coagulation, endothelial function, and oxidative stress. Our analysis suggests that an integrated multi-omics approach may provide critical new insights into the ways TRAP could lead to adverse clinical outcomes. We advocate for efforts to build a more unified approach for characterizing the dynamic and complex biological processes linking TRAP exposure and subclinical and clinical disease and highlight contemporary challenges and opportunities associated with such efforts. [ABSTRACT FROM AUTHOR]

Background/Objectives: Oral lichen planus (OLP) is a chronic inflammatory autoimmune disease of the oral mucosa that affects between 0.5% and 2% of the general population. In the last decade, several studies have associated cardiovascular diseases (CVDs) with some inflammatory skin diseases such as oral lichen planus, demonstrating the presence of dyslipidemia in these pathologies. The objective of this work is to review whether patients with OLP show higher dyslipidemia and CRP levels compared to a healthy control population without OLP. Methods: Searches were carried out in Medline, Scopus, and Cochrane. The studies had to perform a histopathological diagnosis for OLP and the patients could not take any medication to treat this disorder. Non-lichenoid reactions were included. Results: After an initial search that provided us with 254 papers, this number was reduced to 10 articles after a detailed evaluation. All of them were case–control studies that compared the presence of analytical cardiovascular risk factors in patients affected by OLP and in healthy subjects. Conclusions: There is no scientific evidence of the possible association between OLP and CVDs. The only association we can prove is the one between OPL and CVD risk factors, especially those related to the lipid profile. More studies are needed in order to evaluate this relationship in patients diagnosed with CVDs. [ABSTRACT FROM AUTHOR]

Cell death is one of the most important mechanisms of maintaining homeostasis in our body. Ferroptosis and pyroptosis are forms of necrosis-like cell death. These cell death modalities play key roles in the pathophysiology of cancer, cardiovascular, neurological diseases, and other pathologies. Transition metals are abundant group of elements in all living organisms. This paper presents a summary of ferroptosis and pyroptosis pathways and their connection to significant transition metals, namely zinc (Zn), copper (Cu), molybdenum (Mo), lead (Pb), cobalt (Co), iron (Fe), cadmium (Cd), nickel (Ni), mercury (Hg), uranium (U), platinum (Pt), and one crucial element, selenium (Se). Authors aim to summarize the up-to-date knowledge of this topic. In this review, there are categorized and highlighted the most common patterns in the alterations of ferroptosis and pyroptosis by transition metals. Special attention is given to zinc since collected data support its dual nature of action in both ferroptosis and pyroptosis. All findings are presented together with a brief description of major biochemical pathways involving mentioned metals and are visualized in attached comprehensive figures. This work concludes that the majority of disruptions in the studied metals' homeostasis impacts cell fate, influencing both death and survival of cells in the complex system of altered pathways. Therefore, this summary opens up the space for further research. [ABSTRACT FROM AUTHOR]

Anxiety is the most common form of mental health disorder, affecting millions of people worldwide. Psychosocial interventions such as mindfulness and cognitive behavioral therapy (CBT) have been suggested as an effective treatment in the management of general anxiety and anxiety disorders, with emerging evidence also suggesting the effectiveness of hypnosis. Moreover, anxiety has shown to be linked to the onset and development of several cardiovascular diseases (CVD), which are the leading cause of global death. In this paper, we review the current literature to examine the role that anxiety has on the onset and development of CVD and summarize the current knowledge on the role that hypnosis and hypnotherapy have in reducing anxiety, also explaining how this can impact the cardiovascular system and the prevention of CVD. Review of the evidence suggests that hypnosis and hypnotherapy are effective in treating anxiety and may positively affect the heart and the cardiovascular system, reducing sympathetic activation and increasing parasympathetic tone, potentially preventing the onset of CVD related to increased sympathetic activation. However, further studies are required to further understand how hypnosis and hypnotherapy affect the cardiovascular system through investigation of the neurophysiological components of the hypnotic state and of the mind-body relationship. Healthcare systems should embed mental health screening in patients at risk of developing CVD as part of the clinical pathway and consider the role that hypnosis and hypnotherapy may play in the management of CVD. [ABSTRACT FROM AUTHOR]

Cardiovascular disease (CVD) constitutes the most common cause of death worldwide. In Europe alone, approximately 4 million people die annually due to CVD. The leading component of CVD leading to mortality is myocardial infarction (MI). MI is classified into several types. Type 1 is associated with atherosclerosis, type 2 results from inadequate oxygen supply to cardiomyocytes, type 3 is defined as sudden cardiac death, while types 4 and 5 are associated with procedures such as percutaneous coronary intervention and coronary artery bypass grafting, respectively. Of particular note is type 1, which is also the most frequently occurring form of MI. Factors predisposing to its occurrence include, among others, high levels of low-density lipoprotein cholesterol (LDL-C) in the blood, cigarette smoking, chronic kidney disease (CKD), diabetes mellitus (DM), hypertension, and familial hypercholesterolaemia (FH). The primary objective of this review is to elucidate the issues with regard to type 1 MI. Our paper delves into, amidst other aspects, its pathogenesis, risk assessment, diagnosis, pharmacotherapy, and interventional treatment options in both acute and long-term conditions. [ABSTRACT FROM AUTHOR]

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Cardiovascular disease (CVD) is the primary cause of death and disability worldwide. Although age-standardized CVD mortality rates decreased globally by 14.5% between 2006 and 2016, the burden of CVD remains disproportionately higher in low- and middle-income countries compared to high-income countries. Even though proven, effective approaches based on multiple-drug intake aimed at the prevention and treatment of CVD are currently available, poor adherence, early discontinuation of treatment, and suboptimal daily execution of the prescribed therapeutic regimes give rise to shortfalls in drug exposure, leading to high variability in the responses to the prescribed medications. Wald and Law, in their landmark paper published in BMJ 2003, hypothesized that the use of a fixed-dose combination of statins, β-blockers, angiotensin receptor blockers, angiotensin-converting enzyme inhibitors, and aspirin (classic Polypill composition) may increase adherence and decrease CVD by up to 80% when prescribed as primary prevention or in substitution of traditional protocols. Since then, many clinical trials have tested this hypothesis, with comparable results. This review aims to describe the available clinical trials performed to assess the impact of fixed-dose combinations on adherence, cost-effectiveness, and the risk factors critical to the onset of CVD. [ABSTRACT FROM AUTHOR]

Cardiovascular disease (CVD) is a leading cause of death globally; therefore, early detection of CVD is crucial. Many intelligent technologies, including deep learning and machine learning (ML), are being integrated into healthcare systems for disease prediction. This paper uses a voting ensemble ML with chi-square feature selection to detect CVD early. Our approach involved applying multiple ML classifiers, including naïve Bayes, random forest, logistic regression (LR), and k-nearest neighbor. These classifiers were evaluated through metrics including accuracy, specificity, sensitivity, F1-score, confusion matrix, and area under the curve (AUC). We created an ensemble model by combining predictions from the different ML classifiers through a voting mechanism, whose performance was then measured against individual classifiers. Furthermore, we applied chi-square feature selection method to the 303 records across 13 clinical features in the Cleveland cardiac disease dataset to identify the 5 most important features. This approach improved the overall accuracy of our ensemble model and reduced the computational load considerably by more than 50%. Demonstrating superior effectiveness, our voting ensemble model achieved a remarkable accuracy of 92.11%, representing an average improvement of 2.95% over the single highest classifier (LR). These results indicate the ensemble method as a viable and practical approach to improve the accuracy of CVD prediction. [ABSTRACT FROM AUTHOR]

Atherosclerosis is a complex process involving endothelial dysfunction, vascular inflammation, vascular smooth muscle cell (VSMC) proliferation, angiogenesis, and calcification. One of the pathomechanisms of atherosclerosis is the upregulation of Wnt signaling. This study aimed to summarize the current knowledge regarding the role of Wnt signaling and sclerostin in atherosclerosis, vascular calcification, aneurysms, and mortality based on the PubMed database. We followed the Preferred Reporting Items for Systematic Reviews and Meta- Analyses (PRISMA) recommendation and identified 160 papers that were included in this systematic review. The published data highlight that the upregulation of Wnt components facilitates the initiation and progression of atherosclerosis, arterial remodeling, VSMCs proliferation and phenotypic transition to the osteoblastic lineage in the arterial wall. This results in protein secretion, cell migration, calcification, fibrosis and aneurysm formation. The transformation of VSMCs into osteoblast-like cells that is observed in atherosclerosis results in sclerostin expression inhibiting the Wnt pathway. Furthermore, it was shown that sclerostin, expressed in atherosclerotic plaques, inhibits aneurysm formation in a mouse model. However, in humans, while the antisclerostin antibody romosozumab inhibits bone resorption, biochemical parameters of endothelial activation and inflammation are not affected, and the incidence of aneurysms is not increased. It was suggested that detecting sclerostin in the calcified aortic atherosclerotic plaques reflects a defense mechanism against Wnt activation and inhibition of atherosclerosis, although this has only been shown in animal models. Moreover, an increased number of vascular cells converted to osteogenic phenotypes results in increased plasma sclerostin concentrations. Therefore, plasma sclerostin derived from bone limits its importance as a global marker of vascular calcification. [ABSTRACT FROM AUTHOR]

Background: Epidemiological studies often rely on self-reported health problems and validation greatly improves study quality. In a study of late effects after childhood cancer, we validated self-reported cardiovascular problems by contacting general practitioners (GPs). This paper describes: (a) the feasibility of this approach; and (b) the agreement between survivor-reports and reports from their GP. Methods: The Swiss Childhood Cancer Survivor Study (SCCSS) contacts all childhood cancer survivors registered in the Swiss Childhood Cancer Registry since 1976 who survived at least 5 years from cancer diagnosis. We validated answers of all survivors who reported a cardiovascular problem in the questionnaire. Reported cardiovascular problems were hypertension, arrhythmia, congestive heart failure, myocardial infarction, angina pectoris, stroke, thrombosis, and valvular problems. In the questionnaire, we further asked survivors to provide a valid address of their GP and a consent for contact. We sent case-report forms to survivors' GPs and requested information on cardiovascular diagnoses of their patients. To determine agreement between information reported by survivors and GPs, we calculated Cohen's kappa (κ) coefficients for each category of cardiovascular problems. Results: We used questionnaires from 2172 respondents of the SCCSS. Of 290 survivors (13% of 2172) who reported cardiovascular problems, 166 gave consent to contact their GP and provided a valid address. Of those, 135 GPs (81%) replied, and 128 returned the completed case-report form. Survivor-reports were confirmed by 54/128 GPs (42%). Of the 54 GPs, 36 (28% of 128) confirmed the problems as reported by the survivors; 11 (9% of 128) confirmed the reported problem(s) and gave additional information on more cardiovascular outcomes; and seven GPs (5% of 128) confirmed some, but not all cardiovascular problems. Agreement between GPs and survivors was good for stroke (κ = 0.79), moderate for hypertension (κ = 0.51), arrhythmias (κ = 0.41), valvular problems (κ = 0.41) and thrombosis (κ = 0.56), and poor for coronary heart disease (κ = 0.15) and heart failure (κ = 0.32). Conclusions: Despite excellent GP compliance, it was found unfeasible to validate self-reported cardiovascular problems via GPs because they do not serve as gatekeepers in the Swiss health care system. It is thus necessary to develop other validation methods to improve the quality of patient-reported outcomes. [ABSTRACT FROM AUTHOR]

According to the Pan American Health Organization, cardiovascular disease is the leading cause of death worldwide, claiming an estimated 17.9 million lives each year. This paper presents a systematic review to highlight the use of IoT, IoMT, and machine learning to detect, predict, or monitor cardiovascular disease. We had a final sample of 164 high-impact journal papers, focusing on two categories: cardiovascular disease detection using IoT/IoMT technologies and cardiovascular disease using machine learning techniques. For the first category, we found 82 proposals, while for the second, we found 85 proposals. The research highlights list of IoT/IoMT technologies, machine learning techniques, datasets, and the most discussed cardiovascular diseases. Neural networks have been popularly used, achieving an accuracy of over 90%, followed by random forest, XGBoost, k-NN, and SVM. Based on the results, we conclude that IoT/IoMT technologies can predict cardiovascular diseases in real time, ensemble techniques obtained one of the best performances in the accuracy metric, and hypertension and arrhythmia were the most discussed diseases. Finally, we identified the lack of public data as one of the main obstacles for machine learning approaches for cardiovascular disease prediction. [ABSTRACT FROM AUTHOR]

Major depressive disorder (MDD) constitutes a challenge in the field of mental disorders, given its high prevalence in the general population and its impact on the quality of life, while representing a major burden of health worldwide. Currently, much interest in the pathophysiology of MMD ìs also directed towards disentangling the possible biological mechanisms shared with that medical condition known as metabolic syndrome (MeS) that is frequent in the general population and often comorbid with MDD. Therefore, the aim of this paper was to summarize the evidence on the relationships between depression and MeS, and to comment on the common factors and mediators present in these two conditions. For this reason, some of the main databases of scientific literature were accessed, and all the papers fulfilling the goal of this review were selected. The results demonstrated the existence of common pathways between depression and metabolic syndrome involving several mediators, such as inflammation, the hypothalamus-pituitary-adrenal axis, oxidative stress, platelet functions, coronary heart disease and peripheral hormones, thus requiring strict attention from the scientific community. Indeed, such pathways may be targeted in the near future in order to pave the way to new treatment options for these disorders. [ABSTRACT FROM AUTHOR]

This article, titled "Editorial: Plant derived bioactive compounds in the management and treatment of metabolic syndrome," discusses the use of plant-derived bioactive compounds in the management and treatment of metabolic syndrome. Metabolic syndrome is a group of conditions associated with cardiovascular diseases, type 2 diabetes, cognitive decline, and abnormal renal function. The article emphasizes the importance of lifestyle changes, such as modifying food choices and increasing physical activity, as the first line of treatment for metabolic syndrome. It also highlights the potential benefits of incorporating bioactive substances from certain foods or food supplements in the diet to help prevent and manage metabolic syndrome. The article includes several research papers that explore the therapeutic significance and translational value of bioactive compounds in metabolic and related disorders. These papers investigate the effects of specific bioactive compounds on glucose uptake, glucose transport, obesity, gout, coronary heart disease, and diabetes mellitus. Overall, the article suggests that bioactive substances can contribute to weight loss, improved cardiovascular health, lower blood pressure, and enhanced glucose metabolism, thereby aiding in the treatment and management of metabolic syndrome. [Extracted from the article]