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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.

Organic products have grown ever more popular in recent years due to increased concerns regarding one’s health, the environment, and sustainability. The COVID-19 pandemic has only accelerated this growth, prompting producers to adapt to a rapidly growing market while maintaining the same standard of quality. We have conducted a questionnaire-based pilot study on 190 organic food consumers from Bihor county, Romania, in order to analyze the main factors influencing customers’ beliefs regarding organic food consumption, as well as assess the extent to which their consumption frequency changed after the pandemic. A principal component analysis was performed, dividing the factors into two categories referring to intrinsic and extrinsic characteristics of the products, respectively. It was noticed that the most important cues were ranked the same by our 190 respondents, regardless of their socio-demographical background. At the same time, people who consumed organic products more frequently before the pandemic have either maintained or increased their consumption, while more indifferent consumers maintained or decreased the proportion of organic foods in their diet.

Crisis periods such as the COVID-19 pandemic may reshape consumers’ behavior and challenge all food chain actors on how to assure and better respond to consumers’ needs and wants. This study aimed to reveal the main concerns of consumers related to food consumption during the COVID-19 pandemic and to identify factors that may influence their behavior. An online survey was performed among 859 Romanian consumers. The Principal Component Analysis revealed five factors: ecofriendly, socio-economic aspects, food waste, plant-based food, and easily accessible food, which affected consumers’ food behavior during the COVID-19 pandemic. It was noticed that females tended to be more preoccupied with the socio-economic aspects and food waste components, compared to males. At the same time, older people were more concerned about the ecofriendly, socio-economic aspects and health concerns, compared with the younger group, the differences being statistically significant. These insights provide information on crucial aspects that shape consumers’ behavior during crisis periods.

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.

Background and Objectives: The development of liver fibrosis as a consequence of continuous inflammation represents a turning point in the evolution of chronic liver diseases. The recent developments of artificial intelligence (AI) applications show a high potential for improving the accuracy of diagnosis, involving large sets of clinical data. For this reason, the aim of this systematic review is to provide a comprehensive overview of current AI applications and analyze the accuracy of these systems to perform an automated diagnosis of liver fibrosis. Materials and Methods: We searched PubMed, Cochrane Library, EMBASE, and WILEY databases using predefined keywords. Articles were screened for relevant publications about AI applications capable of diagnosing liver fibrosis. Exclusion criteria were animal studies, case reports, abstracts, letters to the editor, conference presentations, pediatric studies, studies written in languages other than English, and editorials. Results: Our search identified a total of 24 articles analyzing the automated imagistic diagnosis of liver fibrosis, out of which six studies analyze liver ultrasound images, seven studies analyze computer tomography images, five studies analyze magnetic resonance images, and six studies analyze liver biopsies. The studies included in our systematic review showed that AI-assisted non-invasive techniques performed as accurately as human experts in detecting and staging liver fibrosis. Nevertheless, the findings of these studies need to be confirmed through clinical trials to be implemented into clinical practice. Conclusions: The current systematic review provides a comprehensive analysis of the performance of AI systems in diagnosing liver fibrosis. Automatic diagnosis, staging, and risk stratification for liver fibrosis is currently possible considering the accuracy of the AI systems, which can overcome the limitations of non-invasive diagnosis methods.

Although the COVID-19 pandemic has held the spotlight over the past years, the antimicrobial resistance (AMR) phenomenon continues to develop in an alarming manner. The lack of strict antibiotic regulation added to the overuse of antimicrobials fueled the AMR pandemic. This paper aims to analyze and identify the impact of the COVID-19 pandemic on antibiotic resistance patterns of Enterococcus spp. The study was designed as a retrospective observational study. Enterococcus spp. infections data were collected from one academic hospital in Cluj-Napoca, Romania over 18 months. A statistical analysis was performed to compare antibiotic resistance phenotypes identified. We recorded an increase in the isolation rates of Enterococcus spp. strains, from 26 isolates (26.53%) during Period A (November 2020–April 2021) to 42 strains (42.85%) during Period C (November 2021–April 2022). The number of strains with resistance to vancomycin increased from 8 during Period A to 17 during Period C. Of the total 36 strains with resistance to vancomycin, 25 were identified as E. faecium. SARS-CoV-2 patients (n = 29) proved to be at risk to develop an E. faecium co-infection (n = 18). We observed that strains with resistance to ampicillin (n = 20) and vancomycin (n = 15) are more often isolated from these patients. All changes identified in our study are to be considered in the light of COVID-19 pandemic, highlighting the threatening AMR phenomenon in Romania. Further studies should be performed to quantify the worldwide effects of these pandemics.

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.

Recent research on the pathogenesis of spondyloarthritis and related immune-mediated diseases associated with human leukocyte antigen class I molecule B27 (HLA-B27) has led to significant progress in terms of management and prognosis, with multiple treatments being constantly evaluated and implemented. Correlations between the genetic background of spondyloarthritis and inflammatory bowel diseases and the inflammatory processes involving gut microbiota have been established. This knowledge has allowed progress in pharmacological therapy. The role of diet in the pathogenesis and treatment of diseases pertaining to the HLA-B27 spectrum is of great significance, considering possible future applications in individualized medicine. Diet impacts the composition of gut microbiota, representing a substrate for the synthesis of metabolites affecting the mucosal immune system. Certain pro-inflammatory mediators, such as emulsifiers and microparticles, induce a more profound cytokine response, promoting inflammation. Numerous diets, including the low-starch diet, the Mediterranean diet, diets with low contents of fermentable oligosaccharides, disaccharides, monosaccharides and polyols (low-FODMAP diets), gluten-free diets and fasting, have been analysed and correlated with patients’ symptomatology and dietary adherence. The aim of this review is to provide an extensive perspective on the diets available to patients with spondyloarthritis and related immune-mediated disorders.