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.

Analysis of serum biomarkers for the assessment of atrophic gastritis (AG), considered as precursor of the intestinal type of gastric cancer, is of growing interest. The combination of pepsinogen (PG), gastrin‑17 (G17) and anti‑Helicobacter pylori (H. pylori) antibody serological assays (panel test) is a non‑invasive tool for the diagnosis of atrophic gastritis. However, the diagnostic reliability of this test remains uncertain. The aim of our study was to assess the diagnostic performance of the serum panel test (GastroPanel) for the diagnosis of atrophic gastritis. From dyspeptic patients, endoscopic biopsy samples (two from the gastric corpus and two from the antrum) and blood samples were collected. The determination of sPGI, sPGII, sG17 and IgG antibodies to H. pylori (H.p IgG) was performed using an enzyme‑linked immunosorbent assay (GastroPanel; Biohit Oyj). Histopathology results were compared with GastroPanel values. Sixty patients were included: 35 (58.3%) females and 25 (41.66%) males; mean age 67.63±9.36 years; 45% H. pylori‑positive. A total of 65% of patients had atrophic gastritis. There were no significant differences between the levels of biomarkers and localization of atrophy. The ratio PG1/PG2 was lower in patients with multifocal atrophy; the difference being close to the threshold of statistical significance. In cases of intestinal metaplasia the values of G17, PG1, PG2, H.p IgG were not statistically altered compared to those without intestinal metaplasia; only the ratio PG1/PG2 was lower in intestinal metaplasia; the difference being almost of statistical significance. Our results revealed that, GastroPanel values did not differ depending on the severity of the atrophy. Biomarkers used by GastroPanel do not have enough accuracy for use in the diagnosis of atrophy in the population studied. A low accuracy only for the ratio PG1/PG2 in patients with multifocal atrophy was found. However, our data revealed a correlation in detecting intestinal metaplasia.

The global prevalence of vitamin D deficiency is more than 20%, and the main causes include insufficient intake, reduced absorption, abnormal metabolism, or resistance to its effects. The levels of serum vitamin D appear to influence cardiovascular risk, and the mechanism involved is linked to the transient outward current and the ultrarapid delayed rectifier K+ current densities, activated through the nuclear vitamin D receptor and Akt pathway. A significant number of studies have correlated vitamin D deficiency with an increased risk of developing cardiac arrhythmias and sudden cardiac death. For this reason, the purpose of this review is to analyze the relation between vitamin D deficiency and the pathogenesis of cardiac arrhythmias. Atrial fibrillation, increased QT interval, and QT dispersion were the most common findings associated with vitamin D deficiency. Due to the heterogeneity among existing studies, further research is necessary to confirm the existing data and to analyze its relationship with other types of arrhythmias.

Functional dyspepsia represents one of the most common and prevalent disorders of the brain–gut interaction, with a large number of widespread risk factors being identified. With an intricate pathogenesis and symptomatology, it heavily impacts the quality of life and, due to the limited efficacy of traditional pharmacological agents, patients are likely to seek other medical and non-medical solutions to their problem. Over the last few years, significant research in this domain has emphasized the importance of various psychological therapies and nutritional recommendations. Nevertheless, a correlation has been established between functional dyspepsia and food intolerances, with more and more patients adopting different kinds of exclusion diets, leading to weight loss, restrictive eating behaviour and an imbalanced nutritional state, further negatively impacting their quality of life. Thus, in this systematic review, we aimed at analysing the impact and efficiency of certain exclusion diets undertook by patients, more precisely, the gluten-free diet and the low-FODMAP diet.

Background: Coping strategies play a crucial role in managing inflammatory bowel disease (IBD), influencing both health-related quality of life (HRQoL) and psychological well-being. This study systematically reviews the available literature to analyze coping mechanisms in IBD populations and their impact. Methods: Relevant English-language studies published until 2023 were identified through a comprehensive search of PubMed, EMBASE, EBSCOhost, and Cochrane Library. After applying inclusion and exclusion criteria, 57 articles underwent full analysis. Results: The findings highlight the diversity of coping strategies used by individuals with IBD and emphasize the need for a nuanced approach considering factors like disease severity, duration, and individual characteristics. This review underlines the influence of coping mechanisms on QoL and indicates their potential to aid IBD management and rehabilitation. Conclusions: This study underscores the value of investigating coping strategies to promote better outcomes for individuals with IBD. Future research should explore personalized interventions that address the heterogeneity of the IBD population.

Background and Aims: Focal liver lesions (FLLs) are defined as abnormal solid or liquid masses differentiated from normal liver, frequently being clinically asymptomatic. The aim of this systematic review is to provide a comprehensive overview of current artificial intelligence (AI) applications, deep learning systems and convolutional neural networks, capable of performing a completely automated diagnosis of FLLs. Methods: We searched PubMed, Cochrane Library, EMBASE, and WILEY databases using predefined keywords. Articles were screened for relevant publications about AI applications capable of automated diagnosis of FLLs. The search terms included: (focal liver lesions OR FLLs OR hepatic focal lesions OR liver focal lesions OR liver tumor OR hepatic tumor) AND (artificial intelligence OR machine learning OR neural networks OR deep learning OR automated diagnosis OR ultrasound OR US OR computer scan OR CT OR magnetic resonance imaging OR MRI OR computer-aided diagnosis OR automated computer tomography OR automated magnetic imaging). Results: Our search identified a total of 32 articles analyzing complete automated imagistic diagnosis of FLLs, out of which 14 studies analyzing liver ultrasound images, 8 studies analyzing computer tomography images and 10 studies analyzing images obtained from magnetic resonance imaging. Conclusions: We found significant evidence demonstrating that implementing a complete automated system for FLLs diagnosis using AI-based applications is currently feasible. Various automated AI-based applications have been analyzed. However, there is no clear evidence about the superiority of any of the systems.