(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.

High-resolution esophageal manometry is used for the study of esophageal motility disorders, with the help of catheters with up to 36 sensors. Color pressure topography plots are generated and analyzed and using the Chicago algorithm a final diagnosis is established. One of the main parameters in this algorithm is integrated relaxation pressure (IRP). The procedure is time consuming. Our aim was to firstly develop a machine learning based solution to detect probe positioning failure and to create a classifier to automatically determine whether the IRP is in the normal range or higher than the cut-off, based solely on the raw images. The first step was the preprocessing of the images, by finding the region of interest—the exact moment of swallowing. Afterwards, the images were resized and rescaled, so they could be used as input for deep learning models. We used the InceptionV3 deep learning model to classify the images as correct or failure in catheter positioning and to determine the exact class of the IRP. The accuracy of the trained convolutional neural networks was above 90% for both problems. This work is just the first step in fully automating the Chicago Classification, reducing human intervention.

(1) Background: Recently, adipokines, including visfatin, have been studied in nonalcoholic fatty liver disease (NAFLD). Several studies evaluated visfatin levels in NAFLD, the presence and severity of hepatic steatosis, liver fibrosis, lobar inflammation, nonalcoholic steatohepatitis (NASH), and gender differences. However, inconclusive results have been reported. Accordingly, we performed a systematic review and meta-analysis, aiming to address these gaps in evidence. (2) Methods: We performed a systematic electronic search on PubMed, EMBASE, and Cochrane Library using predefined keywords. Diagnosis of NAFLD by liver biopsy or imagistic investigations was accepted. Full articles satisfying our inclusion and exclusion criteria were included. NHLBI quality assessment tools were used to evaluate included studies. The principal summary outcome was the mean difference in visfatin levels. (3) Results: There were 21 studies involving 1,923 individuals included in our qualitative assessment, while 14 studies were included in the quantitative assessment. No statistical significance was found assessing visfatin levels in NAFLD [3.361 (95% CI −0.175–6.897)], simple steatosis [7.523 (95% CI −16.221–31.267)], hepatic steatosis severity [−0.279 (95% CI −1.843–1.285)], liver fibrosis [4.133 (95% CI −3.176–11.443)], lobar inflammation [0.358 (95% CI −1.470–2.185)], NASH [−2.038 (95% CI −6.839–2.763)], and gender [(95% CI −0.554–0.556)]. (4) Conclusions: In conclusion, visfatin levels are not associated with NAFLD, presence or severity of hepatic steatosis, liver fibrosis, lobar inflammation, NASH, and gender. However, due to the limited methodological quality of the included studies, results should be interpreted with caution.

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.

Background and aimIrritable bowel syndrome (IBS) has been associated with high prevalence of psychological and psychiatric disorders. However, the association between IBS and each of its subtypes (diarrhea IBS-D, constipation IBS-C, mixed IBS-M) with anxiety still remains unclear. The purpose of this study was to perform a comparative analysis of the association between anxiety and IBS on a period of ten years.MethodsPubMed was searched for studies analyzing IBS and anxiety, published at 10 years interval. The study presents a comparative analysis of the articles that were published between 2003–2005 and 2013–2015, investigating the correlation between anxiety and IBS.ResultsThe initial search identified 220 articles, from which 156 were published between 2013 and 2015, and 64 were published between 2003 and 2005. Of these articles, 15 articles were included in the review. Out of these 15 articles, 10 articles analyzed the correlation between anxiety-depression status in IBS patients using specific questionnaires, 2 articles analyzed genetic variables in IBS, 1 article analyzed serotonin and monoamine oxidase levels in IBS, 1 article analyzed serum levels of IL-1β and IL-10 in IBS, 1 article analyzed somatostatin and vasoactive intestinal peptide levels in IBS. The result was a review of 15 studies that analyzed the association between IBS and anxiety.ConclusionsIBS is a heterogeneous disorder caused by numerous psychological, immunological, infectious, endocrine and genetic factors. In recent years, the number of studies concentrating on genetic factors, cytokines and hormones has increased in comparison with the 2003–2005 period, when clinical investigation, using mainly questionnaires was the essential method. Also, the total number of papers investigating anxiety and IBS, considerably increased. The recent studies have confirmed the fact that IBS symptoms are often exacerbated during stressful events and the psychiatric treatment has a positive effect on gastro-intestinal symptomatology.