According to the World Health Organization, cardiovascular disease (CVD) remains the leading cause of death worldwide, accounting for approximately 18 million deaths per year. Nevertheless, the worldwide prevalence of metabolic diseases, such as type 2 diabetes mellitus, obesity, and non-alcoholic fatty liver disease (NAFLD), also known to be common risk factors for CVD, have dramatically increased over the last decades. Chronic alcohol consumption is a major cause of chronic liver diseases (CLD) as well as being a major health care cost expenditure accounting for the spending of tremendous amounts of money annually. NAFLD has become one of the major diseases plaguing the world while standing as the most common cause of liver disease in the Western countries by representing about 75% of all CLD. Currently, the most common cause of death in NAFLD remains to be CVD. Several mechanisms have been suggested to be responsible for associating FLD with CVD through several mechanisms including low-grade systemic inflammation, oxidative stress, adipokines, endoplasmic reticulum stress, lipotoxicity and microbiota dysbiosis which may also be influenced by other factors such as genetic and epigenetic variations. Despite of all this evidence, the exact mechanisms of how FLD can causally contribute to CVD are not fully elucidated and much remains unknown. Moreover, the current literature supports the increasing evidence associating FLD with several cardiovascular (CV) adverse events including coronary artery disease, increased subclinical atherosclerosis risk, structural alterations mainly left ventricular hypertrophy, increased epicardial fat thickness, valvular calcifications including aortic valve sclerosis and mitral annular calcification and functional cardiac modifications mainly diastolic dysfunction in addition to cardiac arrhythmias such as atrial fibrillation and ventricular arrythmias and conduction defects including atrioventricular blocks and bundle branch blocks. Patients with FLD should be evaluated and managed accordingly in order to prevent further complications. Possible management methods include non-pharmacological strategies including life style modifications, pharmacological therapies as well as surgical management. This review aims to summarize the current state of knowledge regarding the pathophysiological mechanisms linking FLD with an increased CV risk, in addition to associated CV adverse events and current management modalities.

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.

(1) Background: Approximately a billion people worldwide are affected by NAFLD, which places a high clinical burden and financial cost on society. Liver biopsy is the gold standard for diagnosing NAFLD, but its invasivity limits the early diagnosis of NAFLD. Hence, it is important to look for alternate techniques in detecting and diagnosing NAFLD. NAFLD is associated with atherosclerosis. The purpose of this study was to assess the effectiveness of the atherogenic index of plasma (AIP) as a non-invasive modality for predicting NAFLD. (2) Methods: A search using electronic databases PubMed, EMBASE, and Scopus was carried out to find observational studies, looking at research that had been published up until the date of 11 May 2022. The included studies’ quality, risk of bias, and internal validity were evaluated using the QUADAS-2 quality assessment tool. The key summary outcomes were the mean difference (MD) and area under the curve (AUC). (3) Results: A total of eight studies (81,178 participants) were included in our review, while 17% of the included participants had NAFLD. A sex distribution of 57.8% men and 42.2% women was observed. The AIP between NAFLD and the controls was not significant (MD 0.212 [95% CI 0.231–0.655]). A significant MD in AIP between the males and females with NAFLD was observed (MD 0.246 [95% CI 0.098–0.395]). The AIP predicted NAFLD with an AUC of 0.764 as well as in males (AUC 0.761) and females (AUC 0.733). (4) Conclusions: There was a substantial MD in the AIP between both sexes, but there was no significant difference in the AIP values between patients with NAFLD and the controls. The AIP is a reliable biomarker for the diagnosis of NAFLD since its ability to predict the development of NAFLD was comparable to that of the other biomarkers.

Antibiotic resistance (AR) is a naturally occurring phenomenon with the capacity to render useless all known antibiotics in the fight against bacterial infections. Although bacterial resistance appeared before any human life form, this process has accelerated in the past years. Important causes of AR in modern times could be the over-prescription of antibiotics, the presence of faulty infection-prevention strategies, pollution in overcrowded areas, or the use of antibiotics in agriculture and farming, together with a decreased interest from the pharmaceutical industry in researching and testing new antibiotics. The last cause is primarily due to the high costs of developing antibiotics. The aim of the present review is to highlight the techniques that are being developed for the identification of new antibiotics to assist this lengthy process, using artificial intelligence (AI). AI can shorten the preclinical phase by rapidly generating many substances based on algorithms created by machine learning (ML) through techniques such as neural networks (NN) or deep learning (DL). Recently, a text mining system that incorporates DL algorithms was used to help and speed up the data curation process. Moreover, new and old methods are being used to identify new antibiotics, such as the combination of quantitative structure-activity relationship (QSAR) methods with ML or Raman spectroscopy and MALDI-TOF MS combined with NN, offering faster and easier interpretation of results. Thus, AI techniques are important additional tools for researchers and clinicians in the race for new methods of overcoming bacterial resistance.