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Background: Seafarers are at an increased risk of developing cardiovascular diseases (CVDs), potentially due to a stressful working environment and behavioral risk factors. To develop better prevention strategies, it is important to elucidate the extent of this risk. Therefore, we conducted a systematic literature review on CVD in seafarers. Method: We conducted systematic searches in five databases. All studies investigating CVDs among occupational seafarers, published in articles or conference papers, were eligible for inclusion. The identified records were screened and reviewed by two independent researchers, who also evaluated the methodological quality of the included studies. Results: Three thousand nine hundred and seventeen records qualified for screening, and 55 were eligible for inclusion. Most of the studies were observational, including cohort, frequency, incidence or prevalence studies, and review of case records. Around half were assessed at risk of biased findings. Participants in the studies were primarily from North America or the European continent and work onboard transportation vessels. Many studies investigated CVDs as a cause of death, focusing on conditions such as CVD, ischemic heart disease, and myocardial infarction. Frequency of CVD conditions varied but indicate that seafarers face a greater risk compared to the reference populations or control groups. Environmental factors were mainly investigated as risk factors. Conclusion: Our results indicate a higher risk of CVDs among seafarers compared to reference or control groups. However, due to the variable quality of the evidence, well‐designed studies are needed to establish the causes of cardiovascular mortality and morbidity in seafarers and to investigate behavioral aspects of cardiovascular risk. [ABSTRACT FROM AUTHOR]

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: 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]

Objective: To scope how the Australian Bureau of Statistics Socio-Economic Indexes for Areas (SEIFA) has been applied to measure socio-economic status (SES) in peer-reviewed cardiovascular disease (CVD) research. Methods: The Joanna Briggs Institute's scoping review methodology was used. Results: The search retrieved 2788 unique citations, and 49 studies were included. Studies were heterogeneous in their approach to analysis using SEIFA. Not all studies provided information as to what version was used and how SEIFA was applied in analysis. Spatial unit of analysis varied between studies, with participant postcode most frequently applied. Study quality varied. Conclusions: The use of SEIFA in Australian CVD peer-reviewed research is widespread, with variations in the application of SEIFA to measure SES as an exposure. There is a need to improve the reporting of how SEIFA is applied in the methods sections of research papers for greater transparency and to ensure accurate interpretation of CVD research. What is known about the topic? A socio-economic status (SES) gradient is well established for cardiovascular disease (CVD). Research has generally applied two approaches to classifying SES: at an individual level using income, education or occupation data, and at an area level using a range of existing socio-economic information, including the Australian Bureau of Statistics (ABS) Socio-Economic Indexes for Areas (SEIFA). What does this paper add? This review examined how SEIFA has been applied to measure SES in Australian peer-reviewed CVD research and to identify any variations in research practice. What are the implications for practitioners? It is recommended that researchers provide a clear explanation in the methods section of research papers as to which SEIFA version and index was applied, how it was applied, at what spatial unit, and whether the spatial unit was an ABS or non-ABS unit. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy, a microvascular condition associated with an increased risk of cardiovascular disease, poses a substantial global healthcare challenge. The demand for timely diagnosis has prompted the development of automated solutions due to the scarcity of specialists. In this paper, we introduce a ground-breaking approach to diabetic retinopathy detection – the Diabetic Retinopathy Residual Network (DR-ResNet +). The proposed model leverages the power of deep learning to automatically extract features, achieving optimal results in just seven training epochs. The DR-ResNet + architecture is meticulously designed by incorporating a series of convolutional, pooling, and fully connected layers. Hyperparameter optimization is done using both grid and random search techniques to ensure peak performance. To validate the proposed model's robustness, simulated results are compared with well-established deep learning models, such as GoogleNet, VGG16, and AlexNet, using a comprehensive Kaggle dataset comprising over 35,000 retinal images. Moreover, the proposed model is also tested on external datasets like MESSIDOR and IDRiD for its validation. Simulation results reveal that the proposed DR-ResNet + model not only reduces training time by an impressive 95% but also exhibits outstanding performance metrics, including an accuracy of 0.9898, specificity of 0.9916, precision of 0.9670, sensitivity of 0.9829, and an F1-score of 0.9748. These findings position the proposed model as exceptionally well-suited for real-time clinical applications, offering a potential game-changer in diabetic retinopathy diagnosis. This paper presents DR-ResNet + as a pioneering advancement in diabetic retinopathy diagnosis. With its rapid training, superior accuracy, and significant real-world implications, the model holds promise for transforming the landscape of healthcare by providing timely and precise diagnoses for this critical condition. [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]

The present paper investigates constrained global optimization techniques for computationally expensive black box functions that are globally defined but subject to some a priori unknown taboo regions. This challenge typically arises in healthcare applications, where the goal is to maximize the efficacy of a drug while staying within critical safety limits imposed by an external medical regulator. Motivated by the additional challenge where the constrained global optimum lies along the constraint boundary, we comparatively assess the performance of established optimization methods coupled with different acquisition functions in terms of accuracy and efficiency on the physiological application aforementioned and several benchmark problems representative of the complexity of the physiological application. We find the best method based on an average score computed across all applications. We also propose an ensemble method combining results from individual methods, which vastly outperforms the best average method. Furthermore, our study provides a thorough qualitative analysis of the optimization results, emphasizing the challenges a user may encounter when applying Bayesian optimization on constrained optimization problems. [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]

Purpose: The kissing stent (KS) method is low-risk compared with open surgery techniques. It is often used to treat aorto-iliac occlusive disease (AIOD). Deployment of the KS geometry has a high technical success rate. However, stent patency reduces in the first 5 years potentially due to deleterious flow behavior. Potentially harmful hemodynamics due to the KS were investigated in vitro. Methodology: A compliant phantom of the aorto-iliac bifurcation was manufactured. Two surrogate stent-grafts were deployed into the phantom in the KS configuration to investigate effects of the presence of the stents, including the compliance mismatch they cause, on the hemodynamics proximal and distal to the KS. The investigation used pulsatile flow through a flow circuit to simulate abdominal aortic flow. Particle image velocimetry (PIV) was used to quantify the hemodynamics. Results: PIV identified peak proximal and distal velocity in vitro was 0.71 and 1.40m·s−1, respectively, which were within physiological ranges. Throughout systole, flow appeared normal and undisturbed. A lumen wall collapse in the sagittal plane formed during late systole and continued to early diastole proximal to the aorto-iliac bifurcation, distal to the inlet stent position. The wall collapse led to disturbed flow proximal to the stented region in early diastole producing potential recirculation zones and abnormal flow patterns. Conclusion: The normal systolic flow behavior indicates the KS configuration is unlikely to cause an inflammatory response of the arterial walls. The collapse has not been previously identified and may potentially cause long-term patency reduction. It requires further investigation. Clinical Impact: The role of this article is to provide further insight into the haemodynamic behavior through a stented aorto-iliac artery. The results of this investigation will improve the understanding of the effects that using the kissing stent method may have on a patient and help to identify high risk regions that may require more detailed monitoring. This paper also develops the in vitro modelling techniques that will enable further research that cannot be carried out within patients. [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]

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]

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]

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]

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]

Copyright of Chinese Journal of Stroke is the property of Chinese Journal of Stroke Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

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]

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]

Copyright of Zywnosc is the property of Polish Society of Food Technologists - Scientific Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Cardiovascular disease (CVD) has a high incidence and low cure rate worldwide, and atherosclerosis (AS) is the main factor inducing cardiovascular disease, of which lipid deposition in the vessel wall is the main marker of AS. Currently, although statins can be used to lower lipids and low-density lipoprotein (LDL) in AS, the cure rate for AS remains low. Therefore, there is an urgent need to develop new therapeutic approaches, and stem cells are now widely studied, while stem cells are a class of cell types that always maintain the ability to differentiate and can differentiate to form other cells and tissues, and stem cell transplantation techniques have shown efficacy in the treatment of other diseases. With the establishment of cellular therapies and continued research in stem cell technology, stem cells are also being used to address the problem of AS. In this paper, we focus on recent research advances in stem cell therapy for AS and briefly summarize the relevant factors that induce the formation of AS. We mainly discuss the efficacy and application prospects of mesenchymal stem cells (MSCs) for the treatment of AS, in addition to the partial role and potential of exosomes in the treatment of AS. Further, provide new ideas for the clinical application of stem cells. [ABSTRACT FROM AUTHOR]

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: Type 2 diabetes mellitus, cardiovascular disease and chronic obstructive pulmonary disease contribute significantly to societal and individual impact globally. High‐quality management of these long‐term health conditions is important to prevent deterioration of health, although potentially more complex for patients with intellectual disabilities in residential care. Disease management in this context particularly benefits from complete and accurate recording of disease management. Without complete records, long‐term health conditions are more difficult to track due to the level of uncertainty regarding which clinical examinations have and have not been performed. This study therefore aims to examine the recording routines of quality indicators for disease monitoring for chronically ill patients with intellectual disabilities in Dutch residential care. Methods: This retrospective study utilised medical record data from a large Dutch long‐term care provider. We assessed the occurrence of cardiovascular disease (ICPC‐2 codes K74, K75, K76, K89 and K90), type 2 diabetes mellitus (T90, T90.02) and/or COPD (R91, R95). For adults with intellectual disabilities and long‐term condition, we analysed data entries in an 18‐month period (between July 2020 and December 2021). Observed consultation rates were calculated and presented in median with interquartile range and contrasted against the baseline number of consultations in primary care. Information on recorded quality indicators was presented in frequencies and percentages. Findings: Of the three long‐term conditions investigated, the most common was type 2 diabetes mellitus (8.6%; n = 287), followed by cardiovascular disease (5.8%; n = 195) and COPD (3.0%; n = 101). Of those who received management for their long‐term condition from their contracted GP, patients with type 2 diabetes mellitus, cardiovascular disease, or COPD had fewer consultations in 2021 than the Dutch baseline. Discussion of lifestyle was often not recorded. Disease monitoring quality indicators were recorded more often but at a lower frequency than expected. Conclusions: Because of the infrequent recording of quality indicators, recording of management of long‐term conditions for patients with intellectual disabilities in long‐term care appears suboptimal. Although this may not directly harm individual patients, it may jeopardise the quality of management of long‐term conditions, as suboptimal recording limits opportunities for evaluation and improvement. Within a broader trend towards data‐driven work methods in healthcare, recording of quality indicators requires attention from practice, research and policy. We should watch over long‐term diseases to help people stay healthy. We run tests, like checking blood pressure. The results are written in their medical records. These tests are like quality checks.We wanted to see if and how these quality checks were used, mostly for people with a learning disability living in care homes who also have heart disease, diabetes, or lung problems.We looked at their medical papers to see if these checks were there. We also looked at how often they saw the doctor.We found that chronically ill people with a learning disability went to the doctor quite often. But not as often as we expected. Also, the checks were not always written down. The talks about their lifestyle with the doctors were not noted very often.This can impact how well doctors care for the health issues of people with learning disability. [ABSTRACT FROM AUTHOR]

Aims: To perform a systematic review of studies that sought to identify diagnostic biomarkers for the diagnosis of cardiovascular diseases (CVDs) and diabetes mellitus (DM), which could be used in low‐ and middle‐income countries (LMICs) where there is a lack of diagnostic equipment, treatments and training. Materials and Methods: Papers were sourced from six databases: the British Nursing Index, Google Scholar, PubMed, Sage, Science Direct and Scopus. Articles published between January 2002 and January 2023 were systematically reviewed by three reviewers and appropriate search terms and inclusion/exclusion criteria were applied. Results: A total of 18 studies were yielded, as well as 234 diagnostic biomarkers (74 for CVD and 160 for DM). Primary biomarkers for the diagnosis of CVDs included growth differentiation factor 15 and neurogenic locus notch homologue protein 1 (Notch1). For the diagnosis of DM, alpha‐2‐macroglobulin, C‐peptides, isoleucine, glucose, tyrosine, linoleic acid and valine were frequently reported across the included studies. Advanced analytical techniques, such as liquid chromatography mass spectrometry, enzyme‐linked immunosorbent assays and vibrational spectroscopy, were also repeatedly reported in the included studies and were utilized in combination with traditional and alternative matrices such as fingernails, hair and saliva. Conclusions: While advanced analytical techniques are expensive, laboratories in LMICs should carry out a cost–benefit analysis of their use. Alternatively, laboratories may want to explore emerging techniques such as infrared, Fourier transform‐infrared and near‐infrared spectroscopy, which allow sensitive noninvasive analysis. [ABSTRACT FROM AUTHOR]

Cardiovascular disease (CVD) remains to be the leading cause of death globally today and therefore the need for the development of novel therapies has become increasingly important in the cardiovascular field. The mechanism(s) behind the pathophysiology of CVD have been laboriously investigated in both stem cell and bioengineering laboratories. Scientific breakthroughs have paved the way to better mimic cell types of interest in recent years, with the ability to generate any cell type from reprogrammed human pluripotent stem cells. Mimicking the native extracellular matrix using both organic and inorganic biomaterials has allowed full organs to be recapitulated in vitro. In this paper, we will review techniques from both stem cell biology and bioengineering which have been fruitfully combined and have fueled advances in the cardiovascular disease field. We will provide a brief introduction to CVD, reviewing some of the recent studies as related to the role of endothelial cells and endothelial cell dysfunction. Recent advances and the techniques widely used in both bioengineering and stem cell biology will be discussed, providing a broad overview of the collaboration between these two fields and their overall impact on tissue engineering in the cardiovascular devices and implications for treatment of cardiovascular disease. [ABSTRACT FROM AUTHOR]

Steatotic liver disease (SLD) is a worldwide public health problem, causing considerable morbidity and mortality. Patients with SLD are at increased risk for major adverse cardiovascular (CV) events, type 2 diabetes mellitus and chronic kidney disease. Conversely, patients with cardiometabolic conditions have a high prevalence of SLD. In addition to epidemiological evidence linking many of these conditions, there is evidence of shared pathophysiological processes. In December 2022, a unique multi‐stakeholder, multi‐specialty meeting, called MOSAIC (Metabolic multi Organ Science Accelerating Innovation in Clinical Trials) was convened to foster collaboration across metabolic, hepatology, nephrology and CV disorders. One of the goals of the meeting was to consider approaches to drug development that would speed regulatory approval of treatments for multiple disorders by combining liver and cardiorenal endpoints within a single study. Non‐invasive tests, including biomarkers and imaging, are needed in hepatic and cardiorenal trials. They can be used as trial endpoints, to enrich trial populations, to diagnose and risk stratify patients and to assess treatment efficacy and safety. Although they are used in proof of concept and phase 2 trials, they are often not acceptable for regulatory approval of therapies. The challenge is defining the optimal combination of biomarkers, imaging and morbidity/mortality outcomes and ensuring that they are included in future trials while minimizing the burden on patients, trialists and trial sponsors. This paper provides an overview of some of the wide array of CV, liver and kidney measurements that were discussed at the MOSAIC meeting. [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]

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]

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]

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]

Background: Brown adipose tissue (BAT) has emerged as a potential therapeutic target for metabolic disorders due to its thermogenic and anti-obesity properties. β3-adrenergic receptor (β3-AR) agonists have also gained attention as potential agents for BAT activation and metabolic regulation. Mirabegron, a selective β3-AR-agonist used clinically for overactive bladder syndrome, has been explored for its utility in metabolic disorders. However, the controversy surrounding the ability of mirabegron to activate BAT to accelerate metabolism requires further investigation. The aim of this systematic review is to characterize comprehensively the impact of mirabegron on human BAT and its metabolism. Methods: We searched PubMed Central, Web of Science, Embase, and Cochrane Library databases for relevant papers published from the date of database inception to March 2023 for systematic reviews and meta-analyses. We extracted data on primary outcome indicators such as BAT volume, BAT activity, body temperature, and resting energy expenditure (REE), as well as secondary outcome indicators such as heart rate (HR), diastolic blood pressure (DBP), systolic blood pressure (SBP), non-esterified fatty acids (NEFA), blood glucose, and blood insulin from relevant studies. For studies that did not provide suitable data for meta-analysis, we used narrative data synthesis. For studies that provided suitable data for meta-analysis, we conducted meta-analysis using RevMan 5.4 software. Results: We reviewed 10 papers and included 6 in our meta-analysis. Our findings revealed no significant changes in BAT volume (p = 0.72) or blood glucose (p = 0.52) with mirabegron when compared to the placebo or pre-dose population. However, patients showed significant increases in BAT activity (p < 0.01), blood NEFA (p < 0.01), body temperature (p < 0.01), REE (p < 0.01), HR (p < 0.01), DBP (p < 0.01), SBP (p = 0.25), and blood insulin (p < 0.01). Conclusion: Through our meta-analysis of 6 papers, we found that mirabegron has the potential to increase human BAT activity, REE, NEFA content, body temperature, HR, blood pressure, and blood insulin content. These effects may lead to reductions in blood glucose levels in obese/overweight and diabetic patients. Additionally, the activation of BAT by mirabegron could represent a novel approach for treating obesity, diabetes, and cardiovascular disease. Trial registration number and date: CRD42023413446, 04/11/2023. [ABSTRACT FROM AUTHOR]

The past several decades have borne witness to several breakthroughs and paradigm shifts within the field of cardiovascular medicine, but one component that has remained constant throughout this time is the need for accurate animal models for the refinement and elaboration of the hypotheses and therapies crucial to our capacity to combat human disease. Numerous sophisticated and high-throughput molecular strategies have emerged, including rational drug design and the multi-omics approaches that allow extensive characterization of the host response to disease states and their prospective resolutions, but these technologies all require grounding within a faithful representation of their clinical context. Over this period, our lab has exhaustively tested, progressively refined, and extensively contributed to cardiovascular discovery on the basis of one such faithful representation. It is the purpose of this paper to review our porcine model of chronic myocardial ischemia using ameroid constriction and the subsequent myriad of physiological and molecular–biological insights it has allowed our lab to attain and describe. We hope that, by depicting our methods and the insight they have yielded clearly and completely—drawing for this purpose on comprehensive videographic illustration—other research teams will be empowered to carry our work forward, drawing on our experience to refine their own investigations into the pathogenesis and eradication of cardiovascular disease. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases, prevalent as leading health concerns, demand early diagnosis for effective risk prevention. Despite numerous diagnostic models, challenges persist in network configuration and performance degradation, impacting model accuracy. In response, this paper introduces the Optimally Configured and Improved Long Short-Term Memory (OCI-LSTM) model as a robust solution. Leveraging the Salp Swarm Algorithm, irrelevant features are systematically eliminated, and the Genetic Algorithm is employed to optimize the LSTM's network configuration. Validation metrics, including the accuracy, sensitivity, specificity, and F1 score, affirm the model's efficacy. Comparative analysis with a Deep Neural Network and Deep Belief Network establishes the OCI-LSTM's superiority, showcasing a notable accuracy increase of 97.11%. These advancements position the OCI-LSTM as a promising model for accurate and efficient early diagnosis of cardiovascular diseases. Future research could explore real-world implementation and further refinement for seamless integration into clinical practice. [ABSTRACT FROM AUTHOR]

Moderate to severe hypertensive retinopathies are more likely to correlate with uncontrolled blood pressure in all ages. Mild microvascular changes are expected with natural aging and are therefore more concerning in patients younger than 40. Risk assessment is subsequently determined based on blood pressure measurements and patient symptoms. The goal of this paper is to discuss current opinions regarding the role of grading hypertensive retinopathy in the risk assessment of systemic cardiovascular disease in the context of a clinical case. Management and referral recommendations for clinicians will be summarized. Emergent referral for hospital-based care is indicated in any patient with severe hypertensive retinopathy; pregnant women with moderate hypertensive retinopathy; patients younger than 55 with blood pressure greater than Grade 2; any patient with blood pressure greater than grade Severe; any patient with symptoms of chest pain, headache, dyspnea, or dizziness; and any patient with a symptomatic retinal plaque. [ABSTRACT FROM AUTHOR]

Hypercholesterolemia plays a crucial role in the formation of lipid plaques, particularly with elevated low-density lipoprotein (LDL-C) levels, which are linked to increased risks of cardiovascular disease, cerebrovascular disease, and peripheral arterial disease. Controlling blood cholesterol values, specifically reducing LDL-C, is widely recognized as a key modifiable risk factor for decreasing the morbidity and mortality associated with cardiovascular diseases. Historically, statins, by inhibiting the enzyme β-hydroxy β-methylglutaryl-coenzyme A (HMG)-CoA reductase, have been among the most effective drugs. However, newer non-statin agents have since been introduced into hypercholesterolemia therapy, providing a viable alternative with a favorable cost–benefit ratio. This paper aims to delve into the latest therapies, shedding light on their mechanisms of action and therapeutic benefits. [ABSTRACT FROM AUTHOR]

Breast Arterial Calcification (BAC) is a mammographic decision dissimilar to cancer and commonly observed in elderly women. Thus identifying BAC could provide an expense, and be inaccurate. Recently Deep Learning (DL) methods have been introduced for automatic BACdetection and quantification with increased accuracy. Previously, classification with deep learning had reached higher efficiency, but designing the structure of DL proved to be an extremely challenging task due to overfitting models. It also is not able to capture the patterns and irregularities presented in the images. To solve the overfitting problem, an optimal feature set has been formed by Enhanced Wolf Pack Algorithm (EWPA), and their irregularities are identified by Dense-kUNet segmentation. In this paper, Dense-kUNet for segmentation and optimal feature has been introduced for classification (severe, mild, light) that integrates DenseUNet and kU-Net. Longer bound links exist among adjacent modules, allowing relatively rough data to be sent to the following component and assisting the system in finding higher qualities. The major contribution of the work is to design the best features selected by Enhanced Wolf Pack Algorithm (EWPA), and Modified Support Vector Machine (MSVM) based learning for classification. k-Dense-UNet is introduced which combines the procedure of Dense-UNet and kU-Net for image segmentation. Longer bound associations occur among nearby sections, allowing relatively granular data to be sent to the next subsystem and benefiting the system in recognizing smaller characteristics. The proposed techniques and the performance are tested using several types of analysis techniques 826 filled digitized mammography. The proposed method achieved the highest precision, recall, F-measure, and accuracy of 84.4333%, 84.5333%, 84.4833%, and 86.8667%when compared to othermethods on the Digital Database for Screening Mammography (DDSM). [ABSTRACT FROM AUTHOR]

Introduction The current evidence from 2022 published by European Atherosclerosis Society brings the importance of the level of lipoprotein(a) and its influence on the human health. Especially atherosclerotic cardio-vascular events such as myocardial infarction, heart failure, stroke, atherosclerosis are taken into account. They prove, that the associations between the level of Lp(a) is linear with the occurrence of cardio-vascular events. High level of Lp(a), may contribute to the aortic stenosis, cause has pro-inflammatory and pro-atherosclerotic properties. The newest finding demonstrate that Lp(a) has no link with venous thrombosis. Nonetheless, very low level of may contribute with type 2 diabetes. It is recommended to measure the level of lipoprotein(a) at least once in adults to search the patients with higher value than norm. Even at low level of LDL, high Lp(a) is a risk factor for ASCVD. It brings the clinical importance of early detection of potential cardio-vascular diseases and introduction of management. The newest statement form EAS also brings promising possibilities due to specific Lp(a)-lowering therapies is in phase II/III trials. Aim of the study The following paper aims to present a review of the current knowledge about the influence of the level of Lp(a) on cardio-vascular disease and seeks to enhance knowledge and promote effective management of this prevalent condition. The primary goal is to promote the current problem to health care workers with up-to-date information to facilitate early recognition of cardiovascular events. Materials and methods This article presents the current state of knowledge of lipoprotein(a) found in various scientific articles. The following English keywords and its Polish equivalents were used to search Google Scholar's medical databases: lipoprotein(a), cardio-vascular disease, cardiovascular risk, aortic stenosis. The articles most pertinent to the subject have been chosen. [ABSTRACT FROM AUTHOR]

Historically, non-cancer diseases have not been considered a health risk following low-dose exposure to ionizing radiation. However, it is now well known that high-dose ionizing radiation causes cardiovascular disease, and emerging epidemiological evidence suggests an excess risk of non-cancer diseases even following exposure to lower doses of ionizing radiation than previously thought. In fact, the evidence is strongest for cardiovascular disease (CVD). The aim of this review was to report the most representative studies and data on the risk of CVD from low-dose radiation in people with occupational exposure. We reported the results of 27 articles selected from a database search of 1151 studies. The results show a complex evidence landscape on the relationship between radiation exposure and cardiovascular disease. In general, published papers show a positive association between ionizing radiation exposure and dermal microcirculation damage, ischemic heart disease, and cerebrovascular disease. Overall, they highlight the need for comprehensive and detailed research to clarify this relationship. Due to limited statistical power, the dose–risk relationship below 0.5 Gy is inconclusive, but if this relationship is found to have no threshold, it could have a significant impact on current estimates of health risks at low doses. [ABSTRACT FROM AUTHOR]

A popular low-calorie sweetener is erythritol, a naturally occurring sugar alcohol that can be found in fruits and fermented foods. Due to its lower calorie content, it has become a popular replacement for sugar, particularly for those who have diabetes. Although erythritol is believed to be safe for the majority of individuals, current research indicates that it may be linked to cardiovascular diseases, including a higher risk of blood clots formation, heart attack, and stroke. This paper discusses the erythritol's natural occurrence, production, and metabolism while raising concerns about its potential effects on human health. [ABSTRACT FROM AUTHOR]

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Introduction: Myocardial infarction and cardiovascular disease are significant global health issues, particularly in Somalia. The Somali population faces challenges due to armed conflict, limited education, and underdeveloped healthcare infrastructure. Cardiovascular risk factors like diabetes, hypertension, and obesity are prevalent, and Somali people face increased susceptibility.Methods: This study examined Somalia residents' perceptions, attitudes, and behaviors towards myocardial infarction using a cross-sectional paper-based questionnaire. The sample consisted of 313 urban residents in Mogadishu, aged 20 and above. The questionnaire was written in Somali and was designed for the demographic. Knowledge, attitude, and practice scores were categorized into two groups, with associations between knowledge, attitude, and practice with sociodemographic and health characteristics assessed using Logistic Regression.Results: This study analyzed data from A random sample of participants who arrived at the hospital for various reasons regarding their perceptions, attitudes, and responses towards myocardial infarction. The mean age was 42 ± 19 years, with 54% being male and 46% female. Hypertension was the most common comorbidity, followed by dyslipidemia and diabetes. Over half of the respondents had no formal education, and only 3.8% had a smoking history. About 5.7% had a previous history of myocardial infarction. The mean knowledge score was 11.07 ± 0.697, with 53.35% of respondents not knowing enough about myocardial infarction. The majority of the patients showed a favorable attitude, but only a slight majority could recognize symptoms of myocardial infarction. The majority of the patients had inadequate practice and behavior, with 53.6% not practicing regularly or engaging in sports.Conclusion: The research highlights gaps in Mogadishu's Somali community's knowledge and practices regarding myocardial infarction. It emphasizes the need for health education, primary care, and community involvement to improve cardiovascular health awareness and reduce MI incidence. [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]

Purpose: Worldwide, cardiovascular disease is the leading cause of hospitalization and death. Recently, the use of magnetizable nanoparticles for medical drug delivery has received much attention for potential treatment of both cancer and cardiovascular disease. However, proper understanding of the interacting magnetic field forces and the hydrodynamics of blood flow is needed for effective implementation. This paper presents the computational results of simulated implant assisted medical drug targeting (IA-MDT) via induced magnetism intended for administering patient specific doses of therapeutic agents to specific sites in the cardiovascular system. The drug delivery scheme presented in this paper functions via placement of a faintly magnetizable stent at a diseased location in the carotid artery, followed by delivery of magnetically susceptible drug carriers guided by the local magnetic field. Using this method, the magnetic stent can apply high localized magnetic field gradients within the diseased artery, while only exposing the neighboring tissues, arteries, and organs to a modest magnetic field. The localized field gradients also produce the forces needed to attract and hold drug-containing magnetic nanoparticles at the implant site for delivering therapeutic agents to treat in-stent restenosis. Methods: The multi-physics computational model used in this work is from our previous work and has been slightly modified for the case scenario presented in this paper. The computational model is used to analyze pulsatile blood flow, particle motion, and particle capture efficiency in a magnetic stented region using the magnetic properties of magnetite (Fe3O4) and equations describing the magnetic forces acting on particles produced by an external cylindrical electromagnetic coil. The electromagnetic coil produces a uniform magnetic field in the computational arterial flow model domain, while both the particles and the implanted stent are paramagnetic. A Eulerian-Lagrangian technique is adopted to resolve the hemodynamic flow and the motion of particles under the influence of a range of magnetic field strengths (Br = 2T, 4T, 6T, and 8T). Particle diameter sizes of 10 nm–4 µm in diameter were evaluated. Two dimensionless numbers were evaluated in this work to characterize relative effects of Brownian motion (BM), magnetic force induced particle motion, and convective blood flow on particle motion. Results: The computational simulations demonstrate that the greatest particle capture efficiency results for particle diameters within the micron range of 0.7–4 µm, specifically in regions where flow separation and vortices are at a minimum. Similar to our previous work (which did not involve the use of a magnetic stent), it was also observed that the capture efficiency of particles decreases substantially with particle diameter, especially in the superparamagnetic regime. Contrary to our previous work, using a magnetic stent tripled the capture efficiency of superparamagnetic particles. The highest capture efficiency observed for superparamagnetic particles was 78% with an 8 T magnetic field strength and 65% with a 2 T magnetic field strength when analyzing 100 nm particles. For 10 nm particles and an 8 T magnetic field strength, the particle capture efficiency was 55% and for a 2 T magnetic field strength the particle capture efficiency was observed to be 43%. Furthermore, it was found that larger magnetic field strengths, large particle diameter sizes (1 µm and above), and slower blood flow velocity improves the particle capture efficiency. The distribution of captured particles on the vessel wall along the axial and azimuthal directions is also discussed. Results for captured particles on the vessel wall along the axial flow direction showed that the particle density decreased along the axial direction, especially after the stented region. For the entrance section of the stented region, the captured particle density distribution along the axial direction is large, corresponding to the center-symmetrical distribution of the magnetic force in that section. Conclusion: The simulation results presented in this work have shown to yield favorable capture efficiencies for micron range particles and superparamagnetic particles using magnetized implants such as the stent discussed in this work. The results presented in this work justify further investigation of MDT as a treatment technique for cardiovascular disease. [ABSTRACT FROM AUTHOR]

Purpose of Review: Dyslipidemia and type 2 diabetes mellitus are two common conditions that are associated with an increased risk of atherosclerotic cardiovascular disease (ASCVD). In this review, we aimed to provide an in-depth and contemporary review of non-invasive approaches to assess subclinical atherosclerotic burden, predict cardiovascular risk, and guide appropriate treatment strategies. We focused this paper on two main imaging modalities: coronary artery calcium (CAC) score and computed tomography coronary angiography. Recent Findings: Recent longitudinal studies have provided stronger evidence on the relationship between increased CAC, thoracic aorta calcification, and risk of cardiovascular events among those with primary hypercholesterolemia, highlighting the beneficial role of statin therapy. Interestingly, resilient profiles of individuals not exhibiting atherosclerosis despite dyslipidemia have been described. Non-conventional markers of dyslipidemia have also been associated with increased subclinical atherosclerosis presence and burden, highlighting the contribution of apolipoprotein B-100 (apoB)-rich lipoprotein particles, such as remnant cholesterol and lipoprotein(a), to the residual risk of individuals on-target for low-density lipoprotein cholesterol (LDL-C) goals. Regarding type 2 diabetes mellitus, variability in atherosclerotic burden has also been found, and CAC testing has shown significant predictive value in stratifying cardiovascular risk. Summary: Non-invasive assessment of subclinical atherosclerosis can help reveal the continuum of ASCVD risk in those with dyslipidemia and diabetes mellitus and can inform personalized strategies for cardiovascular disease prevention in the primary prevention setting. [ABSTRACT FROM AUTHOR]

Bile acids (BAs) and bilirubin, primarily known for their role in lipid metabolism and as heme catabolite, respectively, have been found to have diverse effects on various physiological processes, including oxidative stress and inflammation. Indeed, accumulating evidence showed that the interplay between BAs and bilirubin in these processes involves intricate regulatory mechanisms mediated by specific receptors and signaling pathways under certain conditions and in specific contexts. Oxidative stress plays a significant role in the development and progression of cardiovascular diseases (CVDs) due to its role in inflammation, endothelial dysfunction, hypertension, and other risk factors. In the cardiovascular (CV) system, recent studies have suggested that BAs and bilirubin have some opposite effects related to oxidative and inflammatory mechanisms, but this area of research is still under investigation. This review aims to introduce BAs and bilirubin from a biochemical and physiological point of view, emphasizing their potential protective or detrimental effects on CVDs. Moreover, clinical studies that have assessed the association between BAs/bilirubin and CVD were examined in depth to better interpret the possible link between them. [ABSTRACT FROM AUTHOR]

Purpose of Review: Fatty acid-binding protein 4 (FABP4) plays a role in lipid metabolism and cardiovascular health. In this paper, we cover FABP4 biology, its implications in atherosclerosis from observational studies, genetic factors affecting FABP4 serum levels, and ongoing drug development to target FABP4 and offer insights into future FABP4 research. Recent Findings: FABP4 impacts cells through JAK2/STAT2 and c-kit pathways, increasing inflammatory and adhesion-related proteins. In addition, FABP4 induces angiogenesis and vascular smooth muscle cell proliferation and migration. FABP4 is established as a reliable predictive biomarker for cardiovascular disease in specific at-risk groups. Genetic studies robustly link PPARG and FABP4 variants to FABP4 serum levels. Considering the potential effects on atherosclerotic lesion development, drug discovery programs have been initiated in search for potent inhibitors of FABP4. Summary: Elevated FABP4 levels indicate an increased cardiovascular risk and is causally related to acceleration of atherosclerotic disease, However, clinical trials for FABP4 inhibition are lacking, possibly due to concerns about available compounds' side effects. Further research on FABP4 genetics and its putative causal role in cardiovascular disease is needed, particularly in aging subgroups. [ABSTRACT FROM AUTHOR]

The article informs about historical context of working hours, particularly the fight for the eight-hour workday and its relevance to International Workers' Day. Topic include recent research on the association between long working hours and cardiovascular diseases, emphasizing findings from studies conducted by the World Health Organization and the International Labour Organization, as well as subsequent analyses addressing socioeconomic factors and specific types of cardiovascular outcomes.