Objective:To compare the benefits and harms of drug treatments for adults with type 2 diabetes, adding non-steroidal mineralocorticoid receptor antagonists (including finerenone) and tirzepatide (a dual glucose dependent insulinotropic polypeptide (GIP)/glucagon-like peptide-1 (GLP-1) receptor agonist) to previously existing treatment options. Design:Systematic review and network meta-analysis. Data sources:Ovid Medline, Embase, and Cochrane Central up to 14 October 2022. Eligibility criteria for selecting studies:Eligible randomised controlled trials compared drugs of interest in adults with type 2 diabetes. Eligible trials had a follow-up of 24 weeks or longer. Trials systematically comparing combinations of more than one drug treatment class with no drug, subgroup analyses of randomised controlled trials, and non-English language studies were deemed ineligible. Certainty of evidence was assessed following the GRADE (grading of recommendations, assessment, development and evaluation) approach. Results:The analysis identified 816 trials with 471 038 patients, together evaluating 13 different drug classes; all subsequent estimates refer to the comparison with standard treatments. Sodium glucose cotransporter-2 (SGLT-2) inhibitors (odds ratio 0.88, 95% confidence interval 0.83 to 0.94; high certainty) and GLP-1 receptor agonists (0.88, 0.82 to 0.93; high certainty) reduce all cause death; non-steroidal mineralocorticoid receptor antagonists, so far tested only with finerenone in patients with chronic kidney disease, probably reduce mortality (0.89, 0.79 to 1.00; moderate certainty); other drugs may not. The study confirmed the benefits of SGLT-2 inhibitors and GLP-1 receptor agonists in reducing cardiovascular death, non-fatal myocardial infarction, admission to hospital for heart failure, and end stage kidney disease. Finerenone probably reduces admissions to hospital for heart failure and end stage kidney disease, and possibly cardiovascular death. Only GLP-1 receptor agonists reduce non-fatal stroke; SGLT-2 inhibitors are superior to other drugs in reducing end stage kidney disease. GLP-1 receptor agonists and probably SGLT-2 inhibitors and tirzepatide improve quality of life. Reported harms were largely specific to drug class (eg, genital infections with SGLT-2 inhibitors, severe gastrointestinal adverse events with tirzepatide and GLP-1 receptor agonists, hyperkalaemia leading to admission to hospital with finerenone). Tirzepatide probably results in the largest reduction in body weight (mean difference -8.57 kg; moderate certainty). Basal insulin (mean difference 2.15 kg; moderate certainty) and thiazolidinediones (mean difference 2.81 kg; moderate certainty) probably result in the largest increases in body weight. Absolute benefits of SGLT-2 inhibitors, GLP-1 receptor agonists, and finerenone vary in people with type 2 diabetes, depending on baseline risks for cardiovascular and kidney outcomes (https://matchit.magicevidence.org/230125dist-diabetes). Conclusions:This network meta-analysis extends knowledge beyond confirming the substantial benefits with the use of SGLT-2 inhibitors and GLP-1 receptor agonists in reducing adverse cardiovascular and kidney outcomes and death by adding information on finerenone and tirzepatide. These findings highlight the need for continuous assessment of scientific progress to introduce cutting edge updates in clinical practice guidelines for people with type 2 diabetes.

Artificial intelligence has aided in the advancement of healthcare research. The availability of open-source healthcare statistics has prompted researchers to create applications that aid cancer detection and prognosis. Deep learning and machine learning models provide a reliable, rapid, and effective solution to deal with such challenging diseases in these circumstances. PRISMA guidelines had been used to select the articles published on the web of science, EBSCO, and EMBASE between 2009 and 2021. In this study, we performed an efficient search and included the research articles that employed AI-based learning approaches for cancer prediction. A total of 185 papers are considered impactful for cancer prediction using conventional machine and deep learning-based classifications. In addition, the survey also deliberated the work done by the different researchers and highlighted the limitations of the existing literature, and performed the comparison using various parameters such as prediction rate, accuracy, sensitivity, specificity, dice score, detection rate, area undercover, precision, recall, and F1-score. Five investigations have been designed, and solutions to those were explored. Although multiple techniques recommended in the literature have achieved great prediction results, still cancer mortality has not been reduced. Thus, more extensive research to deal with the challenges in the area of cancer prediction is required.

BACKGROUND: Social media is increasingly used to deliver HIV interventions for key populations worldwide. However, little is known about the specific uses and effects of social media on human immunodeficiency virus (HIV) interventions. OBJECTIVE: This systematic review examines the effectiveness of social media interventions to promote HIV testing, linkage, adherence, and retention among key populations. METHODS: We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist and Cochrane guidelines for this review and registered it on the International Prospective Register of Systematic Reviews, PROSPERO. We systematically searched six databases and three conference websites using search terms related to HIV, social media, and key populations. We included studies where (1) the intervention was created or implemented on social media platforms, (2) study population included men who have sex with men (MSM), transgender individuals, people who inject drugs (PWID), and/or sex workers, and (3) outcomes included promoting HIV testing, linkage, adherence, and/or retention. Meta-analyses were conducted by Review Manager, version 5.3. Pooled relative risk (RR) and 95% confidence intervals were calculated by random-effects models. RESULTS: Among 981 manuscripts identified, 26 studies met the inclusion criteria. We found 18 studies from high-income countries, 8 in middle-income countries, and 0 in low-income countries. Eight were randomized controlled trials, and 18 were observational studies. All studies (n=26) included MSM; five studies also included transgender individuals. The focus of 21 studies was HIV testing, four on HIV testing and linkage to care, and one on antiretroviral therapy adherence. Social media interventions were used to do the following: build online interactive communities to encourage HIV testing/adherence (10 studies), provide HIV testing services (9 studies), disseminate HIV information (9 studies), and develop intervention materials (1 study). Of the studies providing HIV self-testing, 16% of participants requested HIV testing kits from social media platforms. Existing social media platforms such as Facebook (n=15) and the gay dating app Grindr (n=10) were used most frequently. Data from four studies show that HIV testing uptake increased after social media interventions (n=1283, RR 1.50, 95% CI 1.28-1.76). In the studies where social media interventions were participatory, HIV testing uptake was higher in the intervention arm than the comparison arm (n=1023, RR 1.64, 95% CI 1.19-2.26). CONCLUSIONS: Social media interventions are effective in promoting HIV testing among MSM in many settings. Social media interventions to improve HIV services beyond HIV testing in low- and middle-income countries and among other key populations need to be considered.