UNASSIGNED: The global prevalence of diabetic heart complication has been on the increase, and some of the drugs that are currently used to treat diabetes mellitus (DM) have not been able to mitigate this complication.
UNASSIGNED: This study determines the effect of Brazil nut (Bertholletia excelsa) and metformin on diabetic cardiomyopathy (DCM) in fructose/streptozotocin (STZ)-induced type 2 diabetic rats and also characterizes using Gas Chromatography Mass Spectrophotometry and Fourier Transform Infrared the bioactive compounds in 50% aqueous ethanol extract of Brazil nut.
UNASSIGNED: After inducing type 2 DM, 30 male albino Wistar rats were separated into five groups that comprised of six rats per group, and they were treated as follows: groups 1 (Control) and 2 (Diabetic control) rats received rat pellets and distilled water; group 3 (Diabetic + Brazil nut) received rat pellets and Brazil nut extract (100 mg/kg, orally) dissolved in distilled water, group 4 (Diabetic + metformin) received metformin (100 mg/kg, orally) dissolved in distilled water, while group 5 (Diabetic + Brazil nut + metformin) received oral administrations of Brazil nut (100 mg/kg) and metformin (100 mg/kg) dissolved in distilled water. This study lasted for 6 weeks. The dose of Brazil nut used was selected from our pilot study on the minimum therapeutic dose of different concentrations of Brazil nut extract.
UNASSIGNED: STZ administration induced insulin resistance, hyperglycemia, loss of weight, dyslipidemia, oxidative stress, inflammation, apoptosis, alteration of mammalian target of rapamycin, mitogen-activated protein kinase, heart function markers (creatine kinase MB, lactate dehydrogenase, and aspartate amino transaminase), and heart histology of the diabetic control, which was ameliorated after treatment with Brazil nut and metformin, but their combined treatment was better than the single treatments.
UNASSIGNED: This study shows that Brazil nut contains several bioactive compounds that support its biological properties as well as its candidature as a complementary therapy to metformin in mitigating cardiac complications arising from DM in rats.

暂无摘要。

BACKGROUND: Mesenchymal stem cells (MSCs), as living biodrugs, have entered advanced phases of clinical assessment for cardiac function restoration in patients with myocardial infarction and heart failure. While MSCs are available from diverse tissue sources, bone-marrow-derived MSCs (BM-MSCs) remain the most well-studied cell type, besides umbilical-cord-derived MSCs (UC-MSCs). The latter offers advantages, including noninvasive availability without ethical considerations.
OBJECTIVE: To compare the safety and efficacy of BM-MSCs and UC-MSCs in terms of left ventricular ejection fraction (LVEF), 6-min walking distance (6MWD), and major adverse cardiac events (MACEs).
METHODS: Five databases were systematically searched to identify randomized controlled trials (RCTs). Thirteen RCTs (693 patients) were included using predefined eligibility criteria. Weighted mean differences and odds ratio (OR) for the changes in the estimated treatment effects.
RESULTS: UC-MSCs significantly improved LVEF vs controls by 5.08% [95% confidence interval (CI): 2.20%-7.95%] at 6 mo and 2.78% (95%CI: 0.86%-4.70%) at 12 mo. However, no significant effect was observed for BM-MSCs vs controls. No significant changes were observed in the 6MWD with either of the two cell types. Also, no differences were observed for MACEs, except rehospitalization rates, which were lower only with BM-MSCs (odds ratio 0.48, 95%CI: 0.24-0.97) vs controls.
CONCLUSIONS: UC-MSCs significantly improved LVEF compared with BM-MSCs. Their advantageous characteristics position them as a promising alternative to MSC-based therapy.

In therapeutic diagnostics, early diagnosis and monitoring of heart disease is dependent on fast time-series MRI data processing. Robust encryption techniques are necessary to guarantee patient confidentiality. While deep learning (DL) algorithm have improved medical imaging, privacy and performance are still hard to balance. In this study, a novel approach for analyzing homomorphivally-encrypted (HE) time-series MRI data is introduced: The Multi-Faceted Long Short-Term Memory (MF-LSTM). This method includes privacy protection. The MF-LSTM architecture protects patient\'s privacy while accurately categorizing and forecasting cardiac disease, with accuracy (97.5%), precision (96.5%), recall (98.3%), and F1-score (97.4%). While segmentation methods help to improve interpretability by identifying important region in encrypted MRI images, Generalized Histogram Equalization (GHE) improves image quality. Extensive testing on selected dataset if encrypted time-series MRI images proves the method\'s stability and efficacy, outperforming previous approaches. The finding shows that the suggested technique can decode medical image to expose visual representation as well as sequential movement while protecting privacy and providing accurate medical image evaluation.

UNASSIGNED: Physical activity is important for everyone to maintain and improve health, especially for people with chronic diseases. Mobile exergaming has the potential to increase physical activity and to specifically reach people with poor activity levels. However, commercial mobile exergames are not specially designed for older people with chronic illnesses such as heart failure. The primary aim of this viewpoint is to describe the underlying reasoning guiding the design choices made in developing a mobile exergame, Heart Farming, tailored specifically for sedentary older people diagnosed with heart failure. The goal of the exergame is to increase physical activity levels by increasing the daily walking duration of patients with heart failure by at least 10 minutes. The rationale guiding the design decisions of the mobile exergame is grounded in the thoughtful integration of gamification strategies tailored for application in cardiovascular care. This integration is achieved through applying gamification components, gamification elements, and gamification principles. The Heart Farming mobile exergame is about helping a farmer take care of and expand a virtual farm, with these activities taking place while the patient walks in the real world. The exergame can be adapted to individual preferences and physical condition regarding where, how, when, and how much to play and walk. The exergame is developed using augmented reality so it can be played both indoors and outdoors. Augmented reality technology is used to track the patients\' movement in the real world and to interpret that movement into events in the exergame rather than to augment the mobile user interface.

BACKGROUND: Cardiovascular diseases (CVDs) pose a significant threat to public health. Evidence indicates that the combination of central obesity and normal body mass index (BMI) is associated with an increased risk of cardiovascular disease and mortality. However, limited evidences exists in middle aged and elderly adults in China.
METHODS: This was a prospective cohort study that utilized a nationally representative sample of 6,494 adults aged 45 years and above. These individuals participated in the China Health and Retirement Longitudinal Study spanning from 2011 to 2018. Height, weight and waist circumference (WC) were measured, and BMI was calculated by height and weight. Other variables were obtained through self-reported questionnaires. Association analysis was conducted using Cox proportional hazard regression models.
RESULTS: A total of 10,186 participants were investigated, with 57,185 person-years of follow-up. During this period, 1,571 CVDs occurred, including 1,173 heart diseases and 527 strokes. After adjusting for various factors including age, gender, education, marital status, smoking status, alcohol intake, social activity, hypertension, dyslipidemia, diabetes, cancer, chronic lung diseases, liver disease, kidney disease, digestive disease, ENP(emotional, nervous, or psychiatric problems), memory related disease, arthritis or rheumatism, asthma, self-rated health and depression, the results revealed that compared to those with normal WC normal body mass index (BMI), individuals with central obesity normal BMI had a 27.9% higher risk of CVD incidence (95% confidence interval [CI]:1.074-1.524), and a 33.4% higher risk of heart disease incidence (95% CI:1.095-1.625), while no significant association was found with stroke. Additionally, those with normal WC high BMI showed a 24.6% higher risk of CVD incidence (95% CI:1.046-1.483), and a 29.1% higher risk of heart disease incidence (95% CI:1.045-1.594), again with no significant association with stroke. Finally, individuals with central obesity high BMI exhibited a 49.3% higher risk of CVD incidence (95% CI:1.273-1.751), a 61% higher risk of heart disease incidence (95% CI:1.342-1.931), and a 34.2% higher risk of stroke incidence (95% CI:1.008-1.786). Age- and sex- specific analyses further revealed varying trends in these associations.
CONCLUSIONS: We discovered that the combined association of body mass index(BMI) and central obesity with CVD incidence exhibited a significantly enhanced predictive value. Specifically, a high BMI with central obesity was notably linked to an increased risk of CVD incidence. Additionally, central obesity with a normal BMI or a normal WC coupled with a high BMI significantly augmented the risk of heart disease incidence, but not stroke. Notably, male and middle-aged adults demonstrated a greater propensity for heart disease incidence. Our study underscores the importance of maintaining an optimal BMI and preventing abdominal obesity in promoting cardiovascular health.

The lack of sex-specific cardiovascular disease criteria contributes to the underdiagnosis of women compared to that of men. For more than half a century, the Framingham Risk Score has been the gold standard to estimate an individual\'s risk of developing cardiovascular disease based on the age, sex, cholesterol levels, blood pressure, diabetes status, and the smoking status. Now, machine learning can offer a much more nuanced insight into predicting the risk of cardiovascular diseases. The UK Biobank is a large database that includes traditional risk factors and tests related to the cardiovascular system: magnetic resonance imaging, pulse wave analysis, electrocardiograms, and carotid ultrasounds. Here, we leverage 20,542 datasets from the UK Biobank to build more accurate cardiovascular risk models than the Framingham Risk Score and quantify the underdiagnosis of women compared to that of men. Strikingly, for a first-degree atrioventricular block and dilated cardiomyopathy, two conditions with non-sex-specific diagnostic criteria, our study shows that women are under-diagnosed 2× and 1.4× more than men. Similarly, our results demonstrate the need for sex-specific criteria in essential primary hypertension and hypertrophic cardiomyopathy. Our feature importance analysis reveals that out of the top 10 features across three sexes and four disease categories, traditional Framingham factors made up between 40% and 50%; electrocardiogram, 30%-33%; pulse wave analysis, 13%-23%; and magnetic resonance imaging and carotid ultrasound, 0%-10%. Improving the Framingham Risk Score by leveraging big data and machine learning allows us to incorporate a wider range of biomedical data and prediction features, enhance personalization and accuracy, and continuously integrate new data and knowledge, with the ultimate goal to improve accurate prediction, early detection, and early intervention in cardiovascular disease management. Our analysis pipeline and trained classifiers are freely available at https://github.com/LivingMatterLab/CardiovascularDiseaseClassification.

Cardiovascular disease (CVD) stands as one of the leading causes of morbidity and mortality worldwide, and the continued search for novel therapeutics is vital for addressing this global health challenge. Over the past decade, hydrogen sulfide (H₂S) has garnered significant attention in the field of medical research, as it has been proven to be a cardioprotective gaseous signaling molecule. It joins nitric oxide and carbon monoxide as endogenously produced gasotransmitters. As for its mechanism, H₂S functions through the posttranslational addition of a sulfur group to cysteine residues on target proteins in a process called sulfhydration. As a result, the observed physiological effects of H₂S can include vasodilation, anti-apoptosis, anti-inflammation, antioxidant effects, and regulation of ion channels. Various studies have observed the cardioprotective benefits of H₂S in diseases such as myocardial infarction, ischemia-reperfusion injury, cardiac remodeling, heart failure, arrhythmia, and atherosclerosis. In this review, we discuss the mechanisms and therapeutic potential of H₂S in various CVDs.

This study aims to assess the global prevalence of kinesiophobia and the potential influencing factors among patients with heart disease. A comprehensive search was conducted in PubMed, Embase, Web of Science, PsycINFO, and Scopus databases to identify studies reporting on the prevalence of kinesiophobia and its influencing factors in heart disease patients up to January 2024. A random-effects model was employed to aggregate prevalence rates. Heterogeneity sources were investigated through subgroup analysis, while differences in the prevalence of kinesiophobia across regions, types of heart disease, and gender were evaluated. Additionally, a qualitative analysis of the factors influencing kinesiophobia was performed. This research incorporated 15 studies from six countries, with 14 providing data on the prevalence of kinesiophobia and nine exploring its potential influencing factors. The findings indicated that the overall prevalence of kinesiophobia among heart disease patients was 61.0% (95% CI 49.4-72.6%). Subgroup analysis revealed that the prevalence in upper-middle-income countries was 71.8% (95% CI 66.2-77.4%), while it stands at 49.9% (95% CI 30.2-69.5%) in high-income countries. The prevalence rates among patients with coronary artery disease, heart failure, and atrial fibrillation were 63.2% (95% CI 45.2-81.3%), 69.2% (95% CI 57.6-80.8%), and 71.6% (95% CI 67.1-76.1%), respectively. Gender-wise, no significant difference was observed in the prevalence of kinesiophobia between men and women (52.2% vs. 51.8%). A total of 24 potential influencing factors of kinesiophobia were identified, with education level, monthly income, anxiety, and exercise self-efficacy being the most recognized. The prevalence of kinesiophobia in patients with heart disease is notably high and is influenced by a multitude of factors. Early implementation of targeted preventive measures is imperative to mitigate the incidence of kinesiophobia in this population.

BACKGROUND: Heart failure (HF) is a significant global clinical and public health challenge, impacting 64.3 million individuals worldwide. To address the scarcity of donor organs, left ventricular assist device (LVAD) implantation has become a crucial intervention for managing end-stage HF, serving as a bridge to heart transplantation or as a destination therapy. Web-based health forums, such as MyLVAD.com, play a vital role as trusted sources of information for individuals with HF symptoms and their caregivers.
OBJECTIVE: We aim to uncover the latent topics within the posts shared by users on the MyLVAD.com website.
METHODS: Using the latent Dirichlet allocation algorithm and a visualization tool, our objective was to uncover latent topics within the posts shared on the MyLVAD.com website. Through the application of topic modeling techniques, we analyzed 459 posts authored by recipients of LVAD and their family members from 2015 to 2023.
RESULTS: This study unveiled 5 prominent themes of concern among patients with LVAD and their family members. These themes included family support (39.5% weight value), encompassing subthemes such as family caregiving roles and emotional or practical support; clothing (23.9% weight value), with subthemes related to comfort, normalcy, and functionality; infection (18.2% weight value), covering driveline infections, prevention, and care; power (12% weight value), involving challenges associated with power dependency; and self-care maintenance, monitoring, and management (6.3% weight value), which included subthemes such as blood tests, monitoring, alarms, and device management.
CONCLUSIONS: These findings contribute to a better understanding of the experiences and needs of patients implanted with LVAD, providing valuable insights for health care professionals to offer tailored support and care. By using latent Dirichlet allocation to analyze posts from the MyLVAD.com forum, this study sheds light on key topics discussed by users, facilitating improved patient care and enhanced patient-provider communication.