Introduction: Overweight and obesity are major global health issues, increasing disease risk and straining healthcare systems. Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) are effective for weight loss but cause gastrointestinal side effects, affecting adherence. Research often focuses on diabetics, leaving a gap in understanding their effects on non-diabetic individuals with overweight or obesity. This systematic review and dose-response network meta-analysis addresses this gap, analyzing gastrointestinal adverse events from GLP-1 RAs in non-diabetic subjects with overweight or obesity. Methods: We evaluated available evidence by searching PubMed and EMBASE databases, according to specific inclusion and exclusion eligibility criteria to evaluate gastrointestinal adverse events associated with GLP-1 RAs in non-diabetic individuals with overweight or obesity. Quality assessment of included studies was conducted using Cochrane Collaboration’s tool. Results: Thirty-nine articles were included in the review showing a total number of 33,354 individuals. Nausea, vomiting, diarrhea, and constipation were the most common gastrointestinal adverse effects. All evaluated GLP-1 RAs led to a significant increase in nausea risk, with orforglipron showing the highest risk, followed by exenatide, tirzepatide, semaglutide, and liraglutide. Additionally, liraglutide, orforglipron, semaglutide, and tirzepatide were associated with increased vomiting risk, while cagrilinitide and exenatide showed no significant increase. Exenatide, cagrilinitide, orforglipron were not associated with diarrhea risk. Finally, semaglutide and liraglutide were associated to increased constipation risk, while cagrilinitide and exenatide showed no significant increase. Conclusions: GLP-1 RAs showed several adverse gastrointestinal effects in non-diabetic patients with overweight or obesity. Understanding the different risk profiles of GLP-1 RAs helps clinicians make informed treatment decisions by balancing therapeutic benefits with potential side effects.

Over recent decades, a new antibiotic crisis has been unfolding due to a decreased research in this domain, a low return of investment for the companies that developed the drug, a lengthy and difficult research process, a low success rate for candidate molecules, an increased use of antibiotics in farms and an overall inappropriate use of antibiotics. This has led to a series of pathogens developing antibiotic resistance, which poses severe threats to public health systems while also driving up the costs of hospitalization and treatment. Moreover, without proper action and collaboration between academic and health institutions, a catastrophic trend might develop, with the possibility of returning to a pre-antibiotic era. Nevertheless, new emerging AI-based technologies have started to enter the field of antibiotic and drug development, offering a new perspective to an ever-growing problem. Cheaper and faster research can be achieved through algorithms that identify hit compounds, thereby further accelerating the development of new antibiotics, which represents a vital step in solving the current antibiotic crisis. The aim of this review is to provide an extended overview of the current artificial intelligence-based technologies that are used for antibiotic discovery, together with their technological and economic impact on the industrial sector.

(1) Background: In order to avoid a liver biopsy in non-alcoholic fatty liver disease (NAFLD), several noninvasive biomarkers have been studied lately. Therefore, we aimed to evaluate the visceral adiposity index (VAI) in NAFLD and liver fibrosis, in addition to its accuracy in predicting NAFLD and NASH. (2) Methods: We searched PubMed, Embase, Scopus, and Cochrane Library, identifying observational studies assessing the VAI in NAFLD and liver fibrosis. QUADAS-2 was used to evaluate the quality of included studies. The principal summary outcomes were mean difference (MD) and area under the curve (AUC). (3) Results: A total of 24 studies were included in our review. VAI levels were significantly increased in NAFLD (biopsy-proven and ultrasound-diagnosed), simple steatosis vs. controls, and severe steatosis vs. simple steatosis. However, no significant MD was found according to sex, liver fibrosis severity, simple vs. moderate and moderate vs. severe steatosis, pediatric NAFLD, and NASH patients. The VAI predicted NAFLD (AUC 0.767) and NASH (AUC 0.732). (4) Conclusions: The VAI has a predictive value in diagnosing NAFLD and NASH, with significantly increased values in adult NAFLD patients, simple steatosis compared to controls, and severe steatosis compared to simple steatosis.

Biomarkers of metabolic syndrome and inflammation are pathophysiological predictors and factors of senescence and age-related diseases. Recent evidence showed that particular diet components, such as walnuts rich in antioxidant bioactive compounds and with a balanced lipid profile, could have positive outcomes on human health. A systematic search in PubMed, EMBASE, Cochrane Library, Scopus, and ClinicalTrials.gov databases was performed to retrieve randomized controlled trials published from the beginning of each database through November 2021, reporting on the outcomes of walnut consumption over 22 metabolic syndrome and inflammatory markers in middle-aged and older adults. The search strategy rendered 17 studies in the final selection, including 11 crossover and 6 parallel trials. The study revealed that walnut-enriched diets had statistically significant decreasing effects for triglyceride, total cholesterol, and LDL cholesterol concentrations on some inflammatory markers and presented no consequences on anthropometric and glycemic parameters. Although further studies and better-designed ones are needed to strengthen these findings, the results emphasize the benefits of including walnuts in the dietary plans of this age group.