Effects of Electronic Nicotine Delivery Systems Substitution on Body Weight Status: Protocol for a Systematic Review and Meta-Analysis.

BACKGROUND: Weight gain following smoking cessation is a well-documented concern, often attributed to the absence of nicotine's metabolic influence. The adoption of Electronic Nicotine Delivery Systems (ENDS) has been used to achieve smoking cessation, with claims of aiding weight control. However, existing reviews present conflicting conclusions on ENDS' impact on weight status, necessitating a rigorous evaluation. OBJECTIVE: We aim to conduct a systematic review with meta-analysis to assess the actual impact of ENDS on weight status in individuals who have ceased or reduced conventional smoking. The primary goal is to provide clinicians with evidence-based insights into the potential effects of ENDS use as a smoking substitute on weight control. METHODS: Adhering to PRISMA-P (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols) guidelines, our systematic review will analyze randomized and nonrandomized controlled trials, clinical trials (quasi-experimental), and prospective or retrospective cohort studies on the weight status effects of ENDS among individuals who have either quit or reduced smoking. Searches will include PubMed, Scopus, and Cochrane Library, covering the period from 2010 to January 2024. A gray literature search and supplementary searches will be performed. Data will be extracted independently by 2 reviewers and quality assessments will be conducted concurrently. Quality assessments will use Joanna Briggs Institute tools, 2020 version, along with bias assessments for internal validity and reporting bias based on the Catalogue of Bias. The included studies will be examined for any internal data reporting discrepancies by using Puljak's checklist. Meta-analysis and subgroup analyses (ie, general ENDS usage, ENDS use coupled with a reduction in smoking exceeding 50%, and exclusive ENDS use for achieving smoking cessation) are planned. Certainty of evidence will be evaluated using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) framework. RESULTS: The protocol has been registered in PROSPERO (CRD42023494974) and the entire systematic review is expected to be completed by April 2024. The main goal of this review is to retrieve all current human research studies investigating the influence of ENDS on weight management among individuals who have quit or reduced smoking. Furthermore, the review will assess the quality of these studies and examine potential biases to identify the most dependable evidence available. Dissemination strategies will include traditional journal publications, social media announcements, and a white paper. The latter, available for download and distributed at conferences, aims to reach a broad audience, including clinicians and ENDS users. CONCLUSIONS: The review will address the importance of informing health care professionals and patients about the current and robust evidence regarding the effects of transitioning to ENDS for smoking cessation on weight status. TRIAL REGISTRATION: PROSPERO CRD42023494974; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=494974. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/56324.

Respiratory health effects of e-cigarette substitution for tobacco cigarettes: a systematic review.

BACKGROUND: E-cigarettes (electronic nicotine delivery system, ENDS) have been presented as a harm reduction strategy for people who smoke tobacco cigarettes but who cannot achieve abstinence, or for those who wish to continue to enjoy nicotine and the habit of smoking. What are the health effects of the substitution of ENDS for tobacco cigarettes? This systematic review evaluates the evidence of human clinical tests on the respiratory effects of ENDS use in participants who smoke tobacco cigarettes. METHODS: A registered and published protocol was developed conforming to PRISMA 2020 and AMSTAR2 standards. The literature search was conducted in PubMed, Scopus, and the CENTRAL Cochrane Library and updated to May 2022. Three supplementary searches and a grey literature search were performed. Studies were evaluated with the JBI quality tools and the Oxford Catalogue of Bias. Due to the heterogeneity (diversity) of the studies, a narrative data synthesis was performed on the test findings plus three sub-group analyses. RESULTS: The review consists of sixteen studies and twenty publications. Spirometry tests comprised the majority of the data. In total, 66 respiratory test measurements were reported, out of which 43 (65%) were not significant. Statistically significant findings were mixed, with 9 tests showing improvements and 14 measuring declines, none of which was clinically relevant. Ten studies were rated at a high risk of bias, and six had some concerns primarily due to inadequate research designs and the conduct of the studies. Reporting bias was documented in thirteen studies. CONCLUSIONS: Most of the studies showed no difference in respiratory parameters. This indicates that ENDS substitution for smoking likely does not result in additional harm to respiratory health. Due to the low quality of the studies, confidence in the conclusions is rated as low. Robust studies with a longer duration and sufficient power are required to validate any potential benefits or possible harms of ENDS substitution. Registration PROSPERO #CRD42021239094, International Registered Report Identifier (IRRID): DERR1-10.2196/29084.

Self-assessment and deep learning-based coronavirus detection and medical diagnosis systems for healthcare.

Coronavirus is one of the serious threat and challenge for existing healthcare systems. Several prevention methods and precautions have been proposed by medical specialists to treat the virus and secure infected patients. Deep learning methods have been adopted for disease detection, especially for medical image classification. In this paper, we proposed a deep learning-based medical image classification for COVID-19 patients namely deep learning model for coronavirus (DLM-COVID-19). The proposed model improves the medical image classification and optimization for better disease diagnosis. This paper also proposes a mobile application for COVID-19 patient detection using a self-assessment test combined with medical expertise and diagnose and prevent the virus using the online system. The proposed deep learning model is evaluated with existing algorithms where it shows better performance in terms of sensitivity, specificity, and accuracy. Whereas the proposed application also helps to overcome the virus risk and spread.

Nature-inspired solution for coronavirus disease detection and its impact on existing healthcare systems.

Coronavirus is an infectious life-threatening disease and is mainly transmitted through infected person coughs, sneezes, or exhales. This disease is a global challenge that demands advanced solutions to address multiple dimensions of this pandemic for health and wellbeing.  Different types of medical and technological-based solutions have been proposed to control and treat COVID-19. Machine learning is one of the technologies used in Magnetic Resonance Imaging (MRI) classification whereas nature-inspired algorithms are also adopted for image optimization. In this paper, we combined the machine learning and nature-inspired algorithm for brain MRI images of COVID-19 patients namely Machine Learning and Nature Inspired Model for Coronavirus (MLNI-COVID-19). This model improves the MRI image classification and optimization for better diagnosis. This model will improve the overall performance especially the area of brain images that is neglected due to the unavailability of the dataset. COVID-19 has a serious impact on the patient brain. The proposed model will help to improve the diagnosis process for better medical decisions and performance. The proposed model is evaluated with existing algorithms and achieved better performance in terms of sensitivity, specificity, and accuracy.

Respiratory and Cardiovascular Health Effects of e-Cigarette Substitution: Protocol for Two Living Systematic Reviews.

BACKGROUND: Despite the clear risks of tobacco use, millions of people continue to smoke. Electronic nicotine delivery systems (ENDS), commonly called e-cigarettes, have been proposed as a substitute for those who are unwilling or unable to quit. Current systematic and narrative reviews on the health effects of ENDS use, particularly respiratory and cardiovascular effects, have come to differing conclusions. OBJECTIVE: We conducted two systematic reviews to critically assess and synthesize available human studies on the respiratory and cardiovascular health effects of ENDS substitution for people who smoke. The primary goal is to provide clinicians with evidence on the health effects of ENDS substitution to inform their treatment recommendations and plans. The twin goal of the reviews is to promote health literacy in ENDS users with facts on the health effects of ENDS. METHODS: These two reviews will be living systematic reviews. The systematic reviews will be initiated through a baseline review. Studies will be evaluated using the JBI quality assessment tools and a checklist of biases drawn from the Centre for Evidence Based Medicine Catalogue of Bias. A narrative synthesis is planned because of the heterogeneity of data. A search for recently published studies will be conducted every 3 months, and an updated review will be published every 6 months for the duration of the project or possibly longer. RESULTS: The baseline and updated reviews will be published in a peer-reviewed journal. The findings of the reviews will be reported in a white paper for clinicians and a fact sheet for people who use ENDS. CONCLUSIONS: The substitution of ENDS for cigarettes is one way to potentially reduce the risks of smoking. Clinicians and their patients need to understand the potential benefits and possible risks of substituting ENDS for cigarettes. Our living systematic reviews seek to highlight the best and most up-to-date evidence in this highly contentious and fast-moving field of research. TRIAL REGISTRATION: PROSPERO CRD42021239094; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=239094. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/29084.