Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet

Overweight and obesity are key risk factors of cardiovascular disease (CVD). Obesity is currently presented as a pro-inflammatory state with an expansion in the outflow of inflammatory cytokines, such as interleukin-6 (IL-6) and tumor necrosis factor-alpha (TNF-α), alongside the expanded emission of leptin. The present review aimed to evaluate the relationship between obesity and inflammation and their impacts on the development of cardiovascular disease. A literature search was conducted by employing three academic databases, namely PubMed (Medline), Scopus (EMBASE), and the Cumulative Index to Nursing and Allied Health Literature (CINAHL). The search presented 786 items, and by inclusion and exclusion filterers, 59 works were considered for final review. The Newcastle–Ottawa Scale (NOS) method was adopted to conduct quality assessment; 19 papers were further selected based on the quality score. Obesity-related inflammation leads to a low-grade inflammatory state in organisms by upregulating pro-inflammatory markers and downregulating anti-inflammatory cytokines, thereby contributing to cardiovascular disease pathogenesis. Because of inflammatory and infectious symptoms, adipocytes appear to instigate articulation and discharge a few intense stage reactants and carriers of inflammation. Obesity and inflammatory markers are strongly associated, and are important factors in the development of CVD. Hence, weight management can help prevent cardiovascular risks and poor outcomes by inhibiting inflammatory mechanisms.

During the recent COVID-19 pandemic, healthcare providers have been encouraged to increase their use of telemedicine and to adopt telemedicine platforms for the majority of their clients who have chronic illnesses. Due to the outbreak itself, almost all countries worldwide were placed under emergency lockdowns. In this paper, we reviewed the literature regarding the use of telemedicine during the COVID-19 pandemic. Consequentially, we identified the adoption of telemedicine in various countries worldwide and evaluated their future steps in order to increase the adoption of e-health technologies. As a result of COVID-19, the e-health agenda, especially telemedicine, has been accelerated in several countries. COVID-19 is affecting individuals’ daily lives and has created major difficulties in the management of healthcare facilities for both infected and non-infected patients. A large portion of the rapid increase in the use of telemedicine can be attributed to evidence from previous pandemics as well as progress made by the field in response to COVID-19, especially in industrialized countries. A lack of effective treatment, large numbers of unvaccinated individuals, as well as social distancing and lockdown measures suggest telemedicine is the safest and most appropriate way of working with patients and doctors. In spite of this willingness, a large number of barriers need to be overcome in order for the telemedicine system to function properly and effectively throughout countries. In order for telemedicine to be sustainable and beneficial beyond the pandemic, several technical, educational, infrastructure, legal, and economic issues must be addressed and solved.

Choline participates in several relevant neurochemical processes. It is the precursor and metabolite of acetylcholine (ACh), plays a role in single-carbon metabolism and is an essential component of different membrane phospholipids (PLs). PLs are structural components of cell membranes involved in intraneuronal signal transduction. This paper reviews the roles of choline and of choline-containing phospholipids (CCPLs) on brain metabolism in health and disease followed by an analysis of the effects of exogenously administered CCPLs on the brain, a topic extensively investigated by literature. Based on the observation of decreased cholinergic neurotransmission in brain disorders characterized by cognitive impairment, cholinergic precursor loading therapy with CCPLs was the first approach used to attempt for relieving the cognitive symptoms of Alzheimer's disease. This therapeutic strategy was discontinued due to the negative clinical results obtained with choline or lecithin. Negative results obtained with some compounds cannot be generalized for all CCPLs, as CDP-choline (citicoline) and to a greater extent choline alphoscerate (GPC) displayed interesting effects documented in preclinical studies and limited clinical trials. We provide evidence in favor of CDP-choline and GPC activity in cerebrovascular or neurodegenerative disorders characterized by cholinergic neurotransmission impairment. Based on the results of the controlled clinical trials available, we suggest that due to the lack of novel therapeutic strategies, safe compounds developed a long time ago such as effective CCPLs could have still a place in pharmacotherapy. Therefore selected compounds of this class should be further investigated by new appropriate clinical studies.

(1) Background: Diabetes is a common chronic disease and a leading cause of death. Early diagnosis gives patients with diabetes the opportunity to improve their dietary habits and lifestyle and manage the disease successfully. Several studies have explored the use of machine learning (ML) techniques to predict and diagnose this disease. In this study, we conducted experiments to predict diabetes in Pima Indian females with particular ML classifiers. (2) Method: A Pima Indian diabetes dataset (PIDD) with 768 female patients was considered for this study. Different data mining operations were performed to a conduct comparative analysis of four different ML classifiers: Naïve Bayes (NB), J48, Logistic Regression (LR), and Random Forest (RF). These models were analyzed by different cross-validation (K = 5, 10, 15, and 20) values, and the performance measurements of accuracy, precision, F-score, recall, and AUC were calculated for each model. (3) Results: LR was found to have the highest accuracy (0.77) for all ‘k’ values. When k = 5, the accuracy of J48, NB, and RF was found to be 0.71, 0.76, and 0.75. For k = 10, the accuracy of J48, NB, and RF was found to be 0.73, 0.76, 0.74, while for k = 15, 20, the accuracy of NB was found to be 0.76. The accuracy of J48 and RF was found to be 0.76 when k = 15, and 0.75 when k = 20. Other parameters, such as precision, f-score, recall, and AUC, were also considered in evaluations to rank the algorithms. (4) Conclusion: The present study on PIDD sought to identify an optimized ML model, using with cross-validation methods. The AUC of LR was 0.83, RF 0.82, and NB 0.81). These three were ranked as the best models for predicting whether a patient is diabetic or not.