Search

In cardiology clinic visits, the discussion of optimal dietary patterns for prevention and management of cardiovascular disease is usually very limited. Herein, we explore the benefits and risks of various dietary patterns, including intermittent fasting, low carbohydrate, Paleolithic, whole food plant-based diet, and Mediterranean dietary patterns within the context of cardiovascular disease to empower clinicians with the evidence and information they need to maximally benefit their patients.

An adequate intake of dietary protein is critical to human health and, as a result, protein is the only macronutrient with a recommended daily allowance. Yet, compared to fat and carbohydrate sources, the impact of dietary protein sources on human and cardiovascular health has received far less attention. However, consistent data from large prospective cohort studies from the United States, Europe, and Asia have consistently documented reduced risk of ischemic heart disease (IHD) in individuals with the highest consumption of plant proteins, and an increased risk of IHD in those with the highest intake of animal proteins. Which cardiovascular disease (CVD) risk factors are most impacted is less clear. Mechanistic studies are limited by the fact that, like other macronutrients, protein in the human diet comes from a myriad of sources with varying proportions of non-protein micro and macronutrients that have independent influences on the development of atherosclerotic cardiovascular disease (ASCVD). However, growing evidence suggests that both non-protein and protein components of plant vs. animal protein sources may confer ASCVD protection or risk, and that inflammation and the gut microbiome play crucial roles.

Despite numerous advances in all areas of cardiovascular care, cardiovascular disease (CVD) is the leading cause of death in the United States (US). There is compelling evidence that interventions to improve diet are effective in cardiovascular disease prevention. This clinical practice statement emphasizes the importance of evidence-based dietary patterns in the prevention of atherosclerotic cardiovascular disease (ASCVD), and ASCVD risk factors, including hyperlipidemia, hypertension, diabetes, and obesity. A diet consisting predominantly of fruits, vegetables, legumes, nuts, seeds, plant protein and fatty fish is optimal for the prevention of ASCVD. Consuming more of these foods, while reducing consumption of foods with saturated fat, dietary cholesterol, salt, refined grain, and ultra-processed food intake are the common components of a healthful dietary pattern. Dietary recommendations for special populations including pediatrics, older persons, and nutrition and social determinants of health for ASCVD prevention are discussed.

Highlights • Mechanistic studies illustrate the effects of PD on systemic inflammation, platelet and endothelial function, and lipoproteins. • Trials of PD treatment have not shown reductions in cardiovascular outcomes therefore a definite causal association is lacking. • The morbidity and impact on quality of life, mutual risk factors and systemic inflammation warrant preventive efforts. • Improved screening, better collaboration, and targeted health policies could greatly improve prevention of PD and its sequelae., Periodontal disease (PD) is common in the US and globally. Evidence suggests that poor oral health is associated with atherosclerotic cardiovascular disease (ASCVD); however, this relationship has not been a major focus in clinical cardiology. This manuscript will review the growing evidence linking PD to ASCVD, including pathophysiologic mechanisms and coexistent risk factors. Public health considerations with a focus on disparities, social determinants, preventive strategies, and a call to action to reduce the burden of coincident ASCVD and PD are also reviewed., Graphical abstract Image, graphical abstract

Poor dietary quality is a leading contributor to mortality in the United States, and to most cardiovascular risk factors. By providing education on lifestyle changes and, specifically, dietary changes, hospitals have the opportunity to use the patient experience as a "teachable moment." The food options provided to inpatients and outpatients can be a paradigm for patients to follow upon discharge from the hospital. There are hospitals in the United States that are showcasing novel ways to increase awareness of optimal dietary patterns and can serve as a model for hospitals nationwide.

Objectives In the management of cardiovascular disease, it is important to identify patients at risk early on, to provide interventions to prevent the disease and its complications. The goal of our study was to investigate the association between glucose levels and silent myocardial infarction (SMI) among patients, who consisted of veterans within the Veterans Affairs clinical system. Methods Among the group of patients with an initially normal electrocardiogram, a cohort of patients with a subsequent diagnosis of SMI was selected as the case cohort, whereas 4 patients for each study subject, without evidence of coronary artery disease and normal electrocardiogram within the previous 6 months, were identified and constituted the control cohort. We conducted an adjusted logistic regression model using the stepwise function to assess the association between glucose level and SMI. Results Of the 540 patients included in the study, 108 (20.0%) with an SMI diagnosis made up the case cohort. We observed that as compared with those who had normal levels of glucose, those who were prediabetic were 3.99 times as likely (95% confidence interval 1.48-12.85) to have SMI, whereas the diabetic patients were 3.80 times as likely (95% confidence interval 1.39-12.38) to experience SMI. Conclusions SMIs have been shown to be predictive of subsequent cardiovascular events, including another MI and death, and that indicates the importance of identifying a group at high risk for a SMI. As such, our findings could be extremely beneficial for targeted intervention toward prediabetics and to improve health outcomes in the entire population.

Each year, patients are bombarded with diverging and even contradictory reports concerning the impact of certain additives, foods, and nutrients on cardiovascular health and its risk factors. Accordingly, this third review of nutrition controversies examines the impact of artificial sweeteners, cacao, soy, plant-based meats, nitrates, and meats from grass compared to grain-fed animals on cardiovascular and other health outcomes with the goal of optimizing clinician-led diet counseling.

Existing cardiovascular disease (CVD) and its modifiable risk factors are associated with increased mortality from coronavirus 2019 (COVID-19). Clinical attention has focused on acute interventions for COVID-19, but reducing upstream risks associated with poor outcomes must occur in parallel. This is particularly urgent because risk factors for COVID-19 death are prevalent, and the pandemic has negatively impacted lifestyle and socioeconomic factors that augment these risks. Evidence-based lifestyle interventions have a generally short time-to-benefit, and lower risk of CVD and improve markers of immune function. Wider promotion of healthy lifestyle practices will improve the CVD health of the population and could favorably impact COVID-19 outcomes. Research examining how lifestyle modification affects COVID-19 susceptibility and severity is urgently needed.

Analysis of ionospheric variability is imperative for developing the day-to-day ionospheric modeling and prediction services of the global navigation satellite system (GNSS) applications. The highest-quality GNSS information is provided by the International GNSS Service (IGS) on open access to users. The GNSS information that accompanies them also covers services in support of positioning, navigation and timing information, which benefits science and society. In this paper, retrieve, processing and analysis of Global Positioning System (GPS) derived ionospheric total electron content (TEC) using the IGS Hyderabad GNSS station (17.41° N, 78.55° E; geographical). The presented work would be useful for download, process and analysis of the IGS GNSS data.

Vasculogenic erectile dysfunction has been aptly called the "canary in the coal mine" for cardiovascular disease because it almost always precedes other manifestations of atherosclerotic cardiovascular disease, including myocardial infarction and stroke. It is common, associated with the presence of modifiable cardiovascular risk factors, and impacted by diet and lifestyle choices. This concise review provides an update on the use of dietary and other lifestyle interventions to improve vasculogenic erectile dysfunction and atherosclerotic cardiovascular disease.

OBJECTIVES: The purpose of this study was to investigate real world safety and efficacy of hypertonic saline therapy in cases of refractory acute decompensated heart failure (ADHF) at a large U.S. academic medical center. BACKGROUND: Hypertonic saline therapy has been described as a potential management strategy for refractory ADHF, but experience in the United States is limited. METHODS: A retrospective analysis was performed in all patients receiving hypertonic saline for diuretic therapy-resistant ADHF at the authors’ institution since March 2013. The primary analytic approach was a comparison of the trajectory of clinical variables prior to and after administration of hypertonic saline, with secondary focus on predictors of treatment response. RESULTS: A total of 58 hypertonic saline administration episodes were identified across 40 patients with diuretic-therapy refractory ADHF. Prior to hypertonic saline administration, serum sodium, chloride, and creatinine concentrations were worsening but improved after hypertonic saline administration (p < 0.001, all). Both total urine output and weight loss significantly improved with hypertonic saline (p = 0.01 and

We read with great interest the paper by Griffin et al. ([1][1]) in which the authors report significant improvements in diuretic response and metabolic parameters among diuretic-resistant heart failure patients treated with hypertonic saline and diuretics. We applaud the authors for contributing

Machine learning can be defined as the ability of a computer to learn and solve a problem without being explicitly coded. The efficiency of the program increases with experience through the task specified. In traditional programming, the program and the input are specified to get the output, but in the case of machine learning, the targets and predictors are provided to the algorithm make the process trained. This chapter focuses on various machine learning techniques and their performance with commonly used datasets. A supervised learning algorithm consists of a target variable that is to be predicted from a given set of predictors. Using these established targets is a function that plots targets to a given set of predictors. The training process allows the system to train the unknown data and continues until the model achieves a desired level of accuracy on the training data. The supervised methods can be usually categorized as classification and regression. This chapter discourses some of the popular supervised machine learning algorithms and their performances using quotidian datasets. This chapter also discusses some of the non-linear regression techniques and some insights on deep learning with respect to object recognition.

We propose bilingual word embeddings based on word2vec and fastText models (CBOW and Skip-gram) to address the problem of Humor detection in Hindi-English code-mixed tweets in combination with deep learning architectures. We focus on deep learning approaches which are not widely used on code-mixed data and analyzed their performance by experimenting with three different neural network models. We propose convolution neural network (CNN) and bidirectional long-short term memory (biLSTM) (with and without Attention) models which take the generated bilingual embeddings as input. We make use of Twitter data to create bilingual word embeddings. All our proposed architectures outperform the state-of-the-art results, and Attention-based bidirectional LSTM model achieved an accuracy of 73.6% which is an increment of more than 4% compared to the current state-of-the-art results.

Social media sites like Facebook, Twitter, and other microblogging forums have emerged as a platform for people to express their opinions and views on different issues and events. It is often observed that people tend to take a stance; in favor, against or neutral towards a particular topic. The task of assessing the stance taken by the individual became significantly important with the emergence in the usage of online social platforms. Automatic stance detection system understands the user’s stance by analyzing the standalone texts against a target entity. Due to the limited contextual information a single sentence provides, it is challenging to solve this task effectively. In this paper, we introduce a Multi-Task Learning (MTL) based deep neural network architecture for automatically detecting stance present in the code-mixed corpus. We apply our approach on Hindi-English code-mixed corpus against the target entity - “Demonetisation.” Our best model achieved the result with a stance prediction accuracy of 63.2% which is a 4.5% overall accuracy improvement compared to the current supervised classification systems developed using the benchmark dataset for code-mixed data stance detection.

We present an enhanced service recommender system for web services, which takes existing recommendations from services or content providers and based on topics identified by the user's recent browsing history (including past clicks and searches) re-ranks the recommended URLs for each service, giving an aggregate re-ranked recommendation list. This information can be used by third party services for giving more relevant recommendations and notifications to the user, as well as to build a user interests profile. We have implemented our system using a modified version of LDA to cluster the browsing history, and validated it using browsing data gathered from a selection of users.

Data mining plays an important role in the business world and it also helps in the field of electricity to predict the usage patterns of the houses. At present there is a severe scarcity for electricity. So, electric data in real time is taken and analyzed using data mining techniques. By analyzing the real time electricity data we could understand the importance of supply chain. Real time electricity data was collected from houses using smart meters. Implementation of smart meters gives the detailed data about level of usage of appliances by consumer. To improve the supply of electricity, our primary challenge is to analyze the electricity consumption data. To analyze the data we have chosen mining techniques such as data pre-processing and understanding, clustering, frequent patterns. In this paper, we use these techniques and apply on REDD datasets which contain the usage statistics of six houses. After analyzing the data, we can understand the importance of the data by looking into results. This study can facilitate the electricity board to improve the efficiency in the supply of electricity. This study helps to find the usage patterns of individual houses and the devices with higher load. These mining techniques can further be implemented on larger datasets like regional wide or city wide or state wide.

Objectives A simple, economic, accurate Absorption ratio method was developed for the simultaneous estimation of atorvastatin calcium and nifedipine HCl in bulk and tablet dosage form. Method Methanol was used as a diluent. The absorptions were observed at 237 nm and 297 nm which were selected based on overlap spectra of atorvastatin calcium and nifedipine HCl. Results and conclusion The linearity range was found to be 6–10 μg/ml. The proposed method was validated. The reports was expressed that the proposed method was found to be simple, precise, accurate and rapid for the simultaneous estimation of atorvastatin calcium and nifedipine HCl in bulk and tablet dosage form using absorption ratio method.

The Third Universal Definition of Myocardial Infarction (MI) requires cardiac myocyte necrosis with an increase and/or a decrease in a patient's plasma of cardiac troponin (cTn) with at least one cTn measurement greater than the 99(th) percentile of the upper normal reference limit during: (1) symptoms of myocardial ischemia; (2) new significant electrocardiogram (ECG) ST-segment/T-wave changes or left bundle branch block; (3) the development of pathological ECG Q waves; (4) new loss of viable myocardium or regional wall motion abnormality identified by an imaging procedure; or (5) identification of intracoronary thrombus by angiography or autopsy. Myocardial infarction, when diagnosed, is now classified into five types. Detection of a rise and a fall of troponin are essential to the diagnosis of acute MI. However, high sensitivity troponin assays can increase the sensitivity but decrease the specificity of MI diagnosis. The ECG remains a cornerstone in the diagnosis of MI and should be frequently repeated, especially if the initial ECG is not diagnostic of MI. There have been significant advances in adjunctive pharmacotherapy, procedural techniques and stent technology in the treatment of patients with MIs. The routine use of antiplatelet agents such as clopidogrel, prasugrel or ticagrelor, in addition to aspirin, reduces patient morbidity and mortality. Percutaneous coronary intervention (PCI) in a timely manner is the primary treatment of patients with acute ST segment elevation MI. Drug eluting coronary stents are safe and beneficial with primary coronary intervention. Treatment with direct thrombin inhibitors during PCI is non-inferior to unfractionated heparin and glycoprotein IIb/IIIa receptor antagonists and is associated with a significant reduction in bleeding. The intra-coronary use of a glycoprotein IIb/IIIa antagonist can reduce infarct size. Pre- and post-conditioning techniques can provide additional cardioprotection. However, the incidence and mortality due to MI continues to be high despite all these recent advances. The initial ten year experience with autologous human bone marrow mononuclear cells (BMCs) in patients with MI showed modest but significant increases in left ventricular (LV) ejection fraction, decreases in LV end-systolic volume and reductions in MI size. These studies established that the intramyocardial or intracoronary administration of stem cells is safe. However, many of these studies consisted of small numbers of patients who were not randomized to BMCs or placebo. The recent LateTime, Time, and Swiss Multicenter Trials in patients with MI did not demonstrate significant improvement in patient LV ejection fraction with BMCs in comparison with placebo. Possible explanations include the early use of PCI in these patients, heterogeneous BMC populations which died prematurely from patients with chronic ischemic disease, red blood cell contamination which decreases BMC renewal, and heparin which decreases BMC migration. In contrast, cardiac stem cells from the right atrial appendage and ventricular septum and apex in the SCIPIO and CADUCEUS Trials appear to reduce patient MI size and increase viable myocardium. Additional clinical studies with cardiac stem cells are in progress.