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Background: Hypertensive disorders of pregnancy (HDP) are a major cause of maternal morbidity and mortality, and their association with increased cardiovascular disease (CVD) risk represents a major public health concern. However, assessing CVD risk in women with a history of these conditions presents unique challenges, especially when studies are carried out using routinely collected data. Objectives: To summarise and describe key challenges related to the design and conduct of administrative studies assessing CVD risk in women with a history of HDP and provide concrete recommendations for addressing them in future research. Methods: This is a methodological guidance paper. Results: Several conceptual and methodological factors related to the data‐generating mechanism and study conceptualisation, design/data management and analysis, as well as the interpretation and reporting of study findings should be considered and addressed when designing and carrying out administrative studies on this topic. Researchers should develop an a priori conceptual framework within which the research question is articulated, important study variables are identified and their interrelationships are carefully considered. Conclusions: To advance our understanding of CVD risk in women with a history of HDP, future studies should carefully consider and address the conceptual and methodological considerations outlined in this guidance paper. In highlighting these challenges, and providing specific recommendations for how to address them, our goal is to improve the quality of research carried out on this topic. [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 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.

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.

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.

• 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]

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The Coronavirus Disease 2019 (COVID-19) has had a continuous and robust impact on world health. The resulting COVID-19 pandemic has had a devastating physical, mental and fiscal impact on the millions of people living with noncommunicable diseases (NCDs). In addition to older age, people living with CVD, stroke, obesity, diabetes, kidney disease, and hypertension are at a particularly greater risk for severe forms of COVID-19 and its consequences. Meta-analysis indicates that hypertension, diabetes, chronic kidney disease, and thrombotic complications have been observed as both the most prevalent and most dangerous co-morbidities in COVID-19 patients. And despite the nearly incalculable physical, mental, emotional, and economic toll of this pandemic, forthcoming public health figures continue to place cardiovascular disease as the number one cause of death across the globe in the year 2020. The world simply cannot wait for the next pandemic to invest in NCDs. Social determinants of health cannot be addressed only through the healthcare system, but a more holistic multisectoral approach with at its basis the Sustainable Development Goals (SDGs) is needed to truly address social and economic inequalities and build more resilient systems. Yet there is reason for hope: the 2019 UN Political Declaration on UHC provides a strong framework for building more resilient health systems, with explicit calls for investment in NCDs and references to fiscal policies that put such investment firmly within reach. By further cementing the importance of addressing circulatory health in a future Framework Convention on Emergency Preparedness, WHO Member States can take concrete steps towards a pandemic-free future. As the chief representatives of the global circulatory health community and patients, the Global Coalition for Circulatory Health calls for increased support for the healthcare workforce, global vaccine equity, embracing new models of care and digital health solutions, as well as fiscal policies on unhealthy commodities to support these investments.

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]

Inflammation is a marker of arterial disease stemming from cholesterol-dependent to -independent molecular mechanisms. In recent years, the role of inflammation in atherogenesis has been underpinned by pharmacological approaches targeting systemic inflammation that have led to a significant reduction in cardiovascular disease (CVD) risk. Although the use of nutraceuticals to prevent CVD has largely focused on lipid-lowering (e.g, red-yeast rice and omega-3 fatty acids), there is growing interest and need, especially now in the time of coronavirus pandemic, in the use of nutraceuticals to reduce inflammatory markers, and potentially the inflammatory CVD burden, however, there is still not enough evidence to confirm this. Indeed, diet is an important lifestyle determinant of health and can influence both systemic and vascular inflammation, to varying extents, according to the individual nutraceutical constituents. Thus, the aim of this Position Paper is to provide the first attempt at recommendations on the use of nutraceuticals with effective anti-inflammatory properties. [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]

[Display omitted] • The electrochemical immunosensor designed very stable, low-cost and sensitive method for determination of Creatine Kinase. • CK immunosensor exhibited high analytical performance with a linear range 0.150 pg/mL and low detection limit (0.045 pg/mL). • Disposable graphite paper electrodes were firstly used to detection CK antigen for a novel biosensor system. • The optimization was performed for all fabrication steps of the electrochemical biosensor system. The enzyme creatine kinase (CK) is one of the most well-established biomarkers in cardiovascular disease. For acute myocardial infarction (AMI) diagnosis, CK has a clinical comorbidity rate of 90%. This study has developed a novel electrochemical immunosensor using disposable graphite paper electrodes (GP). The GP electrodes were modified with gold nanoparticles (AuNP) to facilitate CK detection. Afterwards, GP electrodes were covalently immobilized with 6-mercapto-1-hexanol (6-MH), resulting in self-assembled monolayers (SAMs). Then, the electrodes were formed with a 3-Glycidyloxypropyltrimethoxysilane (3-GOPE) agent. Afterwards, the electrodes were immobilized with anti-CK (antibody creatine kinase) protein. In the final immobilization step, BSA (bovine serum albumin) protein was used to block non-covalent interactions. All parameters of the proposed immunosensor were optimized, including concentrations and incubation times. Analytical characteristics such as square wave voltammetry, linear determination range, repeatability, reproducibility, and regeneration of biosensors were determined. All characterization steps were monitored by electrochemical impedance spectroscopy (EIS), cyclic voltammetry (CV). Moreover, the single frequency impedance (SFI) technique interacted with anti-CK and CK antigens. Furthermore, the proposed immunosensor was characterized using scanning electron microscopy (SEM). The proposed immunosensor exhibited a wide detection range (0.1–50 pg mL−1), and low limit of detection (LOD), and a low limit of quantification (LOQ); 0.045 pg mL−1 and 0.171 pg mL−1, respectively. Finally, the developed biosensor was tried in an actual blood sample, which showed it could be utilized in clinical applications. [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]

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]

The aim of the present study is to analyze the data indicating an association between high salt intake and the gastrointestinal microbiota in the development of salt-sensitive hypertension in animals and men. It is also, to discuss the preventive effects of exercise on gut-induced hypertension by favorably modifying the composition of gut microbiota. Salt sensitivity is quite common, accounting for 30%–60% in hypertensive subjects. Recently, a novel cause for salt-sensitive hypertension has been discovered through the action of gut microbiota by the secretion of several hormones and the action of short chain fatty acids (SCFAs). In addition, recent studies indicate that exercise might favorably modify the adverse effects of gut microbiota regarding their effects on BP. To identify the role of gut microbiota on the incidence of hypertension and CVD and the beneficial effect of exercise, a Medline search of the English literature was conducted between 2018 and 2023 and 42 pertinent papers were selected. The analysis of data from the selected papers disclosed that the gut microbiota contribute significantly to the development of salt-sensitive hypertension and that exercise modifies their gut composition and ameliorates their adverse effects on BP. [ABSTRACT FROM AUTHOR]

Background: This paper describes a UK-based study, SPICES-Sussex, which aimed to co-produce and implement a community-based cardiovascular disease (CVD) risk assessment and reduction intervention to support underserved populations at moderate risk of CVD. The objectives were to enhance stakeholder engagement; to implement the intervention in four research sites and to evaluate the use of Voluntary and Community and Social Enterprises (VCSE) and Community Health Worker (CHW) partnerships in health interventions. Methods: A type three hybrid implementation study design was used with mixed methods data. This paper represents the process evaluation of the implementation of the SPICES-Sussex Project. The evaluation was conducted using the RE-AIM framework. Results: Reach: 381 individuals took part in the risk profiling questionnaire and forty-one women, and five men participated in the coaching intervention. Effectiveness: quantitative results from intervention participants showed significant improvements in CVD behavioural risk factors across several measures. Qualitative data indicated high acceptability, with the holistic, personalised, and person-centred approach being valued by participants. Adoption: 50% of VCSEs approached took part in the SPICES programme, The CHWs felt empowered to deliver high-quality and mutually beneficial coaching within a strong project infrastructure that made use of VCSE partnerships. Implementation: Co-design meetings resulted in local adaptations being made to the intervention. 29 (63%) of participants completed the intervention. Practical issues concerned how to embed CHWs in a health service context, how to keep engaging participants, and tensions between research integrity and the needs and expectations of those in the voluntary sector. Maintenance: Several VCSEs expressed an interest in continuing the intervention after the end of the SPICES programme. Conclusion: Community-engagement approaches have the potential to have positively impact the health and wellbeing of certain groups. Furthermore, VCSEs and CHWs represent a significant untapped resource in the UK. However, more work needs to be done to understand how links between the sectors can be bridged to deliver evidence-based effective alternative preventative healthcare. Reaching vulnerable populations remains a challenge despite partnerships with VCSEs which are embedded in the community. By showing what went well and what did not, this project can guide future work in community engagement for health. [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]

Lycopene intake from tomatoes and other food sources has multiple potential health benefits. This report aimed to evaluate the current research literature on lycopene concerning human and animal health. The electronic Web of Science Core Collection database was searched with (lycopene*) AND (health* OR illness* OR disease* OR medic* OR pharma* OR drug* OR therap*). The resulted 3972 papers were analyzed with the aid of bibliometric software. Besides the United States, the lycopene papers received global contributions, particularly from China, Italy, India, and Spain. Examples of frequently mentioned chemicals/chemical classes were carotenoid, beta carotene, alpha carotene, beta cryptoxanthin, and alpha tocopherol. Examples of frequently mentioned medical conditions were prostate cancer, cardiovascular disease, and obesity. Published scientific articles reveal the diverse potential of lycopene in prompting human and animal health, and the knowledge on the bioactivities of this phytochemical is expected to further grow in the future. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases impose an enormous burden on patients and society. New health technologies promise to lower this burden; however, novel treatments often come at a high cost. In the Netherlands, health technology assessment (HTA) is increasingly being used to inform policy bodies about the optimal distribution of scarce healthcare resources and to guide decision-making about financing and reimbursement. In particular, economic evaluations, as one pillar of HTA, are frequently used to compare the costs and effects of different interventions. This paper aims to define HTA and its relevance to healthcare policy as well as providing a comprehensive overview of the methodology of economic evaluations targeting health professionals and researchers with limited prior knowledge of this subject. Accordingly, different types of economic evaluations are introduced, together with their respective costs and outcomes. Further, the results of economic evaluations are explained, along with techniques for performing them and methods for coping with uncertainty. In addition to this paper-based learning format, each chapter is complemented by a video lecture with further information and practical examples, helping to better understand and analyse health economic studies. [ABSTRACT FROM AUTHOR]

We performed a systematic review and meta-analysis of the literature regarding cardiovascular outcomes of differentiated thyroid cancer (DTC) patients who are on long term thyroid stimulating hormone suppression. Searches were carried out using Prisma guidelines in Medline, Embase, CENTRAL, CINAHL and Scopus databases. Eligible papers were those which investigated discrete cardiovascular clinical outcomes in TSH suppressed patients and meta-analysis of selected studies was performed using Revman 5.4.1. We found a total of 195 879 DTC patients with median length to follow up of 8.6 years (range 5–18.8 years). Analysis showed DTC patients to be at higher risk of atrial fibrillation (HR 1.58, 95% CI 1.40, 1.77), stroke (HR 1.14, 95% CI 1.09, 1.20) and all-cause mortality (HR 2.04, 95% CI 1.02, 4.07). However, there was no difference in risk of heart failure, ischemic heart disease or cardiovascular mortality. These findings suggest that degree of TSH suppression must be titrated to accommodate risk of cancer recurrence and cardiovascular morbidity. [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]

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]

Various forms of pollution carry a substantial burden with respect to increasing the risk of causing and exacerbating noncommunicable diseases, especially cardiovascular disease. The first part of this 2-part series on pollution and cardiovascular disease provided an overview of the impact of global warming and air pollution. This second paper provides an overview of the impact of water, soil, noise, and light pollution on the cardiovascular system. This review discusses the biological mechanisms underlying these effects and potential environmental biometrics of exposure. What is clear from both these pollution papers is that significant efforts and redoubled urgency are needed to reduce the sources of pollution in our environment, to incorporate environmental risk factors into medical education, to provide resources for research, and, ultimately, to protect those who are particularly vulnerable and susceptible. [Display omitted] • Increasing evidence suggests that water, soil, noise, and light pollution increase cardiovascular morbidity and mortality. • The mechanisms linking these sources of pollution to cardiovascular disease are diverse, making mitigation efforts challenging, but evolving methods to track these sources of pollution may point to new strategies. • Collectively, these data underscore the urgent need to address pollution in all its forms at a global level. [ABSTRACT FROM AUTHOR]

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Cardiovascular disease (CVD) is a prominent contributor to global mortality rates. The principal aim of this research is to employ machine learning techniques to anticipate the early actions needed to prevent the disease from progressing. The timely identification of individuals with a heightened risk of developing CVD plays an important role in implementing early interventions to impede disease progression. Machine learning techniques have shown promise in predicting CVD risk. For this paper, we propose a comprehensive CVD prediction model using ML techniques. Our approach utilizes a substantial dataset of electronic health records (EHRs) for training and validating our model. Through the incorporation of feature engineering, feature selection, and model optimization techniques, we have reached a high level of accuracy and interpretability. To evaluate the prediction of cardiovascular disease (CVD) threats, we compare the performance of various popular ML algorithms, such as logistic regression, random forest, and Support Vector Machine. Our findings point towards that our proposed model improve on existing approaches in regard of both accuracy and efficiency. This model can efficiently recognize individuals with an elevated risk of developing CVD, enabling early interventions to prevent the onset and progression of the disease. Additionally, we perform a acatalectic analysis of the features that contribute most to the assessment of CVD risk, providing insights into the underlying mechanisms of the disease. We also evaluate the robustness of our model by testing its performance on a separate dataset. Furthermore, we discuss the clinical implications of our proposed model, highlighting the potential benefits of using ML techniques in identifying individuals at high risk of developing CVD. Our model can aid in personalized medicine and facilitate the delivery of targeted interventions to high-risk individuals, thereby improving patient outcomes and reducing healthcare costs. When it comes to treating severe stages of cardiovascular disease, preventive treatments are typically more economical. Our approach can assist in lessening the financial burden related to CVD by lowering hospital stays, ER visits, and long-term care expenses via early detection of high-risk individuals and implementation of focused therapies. In summary, our model presents a robust solution for utilizing machine learning techniques to envisage the risk of cardiovascular disease (CVD). Our study targets to provide the expanding field of research regarding the application of machine learning in healthcare. The insights extended from our findings hold significant potential for enhancing the anticipation and treatment of CVD. [ABSTRACT FROM AUTHOR]

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]