Comprehensive analysis of gastrointestinal side effects in COVID-19 patients undergoing combined pharmacological treatment with azithromycin and hydroxychloroquine: a systematic review and network meta-analysis.

During the COVID-19 pandemic, several drugs were repositioned and combined to quickly find a way to mitigate the effects of the infection. However, the adverse effects of these combinations on the gastrointestinal tract are unknown. We aimed investigate whether Hydroxychloroquine (HD), Azithromycin (AZ), and Ivermectin (IV) used in combination for the treatment of COVID-19, can lead to the development of gastrointestinal disorders. This is a systematic review and network meta-analysis conducted using Stata and Revman software, respectively. The protocol was registered with PROSPERO (CRD42023372802). A search of clinical trials in Cochrane Library databases, Embase, Web of Science, Lilacs, PubMed, Scopus and Clinicaltrials.gov conducted on November 26, 2023. The eligibility of the studies was assessed based on PICO criteria, including trials that compared different treatments and control group. The analysis of the quality of the evidence was carried out according to the GRADE. Six trials involving 1,686 COVID-19 patients were included. No trials on the association of HD or AZ with IV met the inclusion criteria, only studies on the association between HD and AZ were included. Nausea, vomiting, diarrhea, abdominal pain and increased transaminases were related. The symptoms of vomiting and nausea were evaluated through a network meta-analysis, while the symptom of abdominal pain was evaluated through a meta-analysis. No significant associations with these symptoms were observed for HD, AZ, or their combination, compared to control. Low heterogeneity and absence of inconsistency in indirect and direct comparisons were noted. Limitations included small sample sizes, varied drug dosages, and potential publication bias during the pandemic peak. This review unveils that there are no associations between gastrointestinal adverse effects and the combined treatment of HD with AZ in the management of COVID-19, as compared to either the use of a control group or the administration of the drugs individually, on the other hand, highlighting the very low or low certainty of evidence for the evaluated outcomes. To accurately conclude the absence of side effects, further high-quality randomized studies are needed.

Artificial Intelligence on Diagnostic Aid of Leprosy: A Systematic Literature Review.

Leprosy is a neglected tropical disease that can cause physical injury and mental disability. Diagnosis is primarily clinical, but can be inconclusive due to the absence of initial symptoms and similarity to other dermatological diseases. Artificial intelligence (AI) techniques have been used in dermatology, assisting clinical procedures and diagnostics. In particular, AI-supported solutions have been proposed in the literature to aid in the diagnosis of leprosy, and this Systematic Literature Review (SLR) aims to characterize the state of the art. This SLR followed the preferred reporting items for systematic reviews and meta-analyses (PRISMA) framework and was conducted in the following databases: ACM Digital Library, IEEE Digital Library, ISI Web of Science, Scopus, and PubMed. Potentially relevant research articles were retrieved. The researchers applied criteria to select the studies, assess their quality, and perform the data extraction process. Moreover, 1659 studies were retrieved, of which 21 were included in the review after selection. Most of the studies used images of skin lesions, classical machine learning algorithms, and multi-class classification tasks to develop models to diagnose dermatological diseases. Most of the reviewed articles did not target leprosy as the study's primary objective but rather the classification of different skin diseases (among them, leprosy). Although AI-supported leprosy diagnosis is constantly evolving, research in this area is still in its early stage, then studies are required to make AI solutions mature enough to be transformed into clinical practice. Expanding research efforts on leprosy diagnosis, coupled with the advocacy of open science in leveraging AI for diagnostic support, can yield robust and influential outcomes.