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Background/Objectives: To develop a deep learning model for esophageal motility disorder diagnosis using high-resolution manometry images with the aid of Gemini. Methods: Gemini assisted in developing this model by aiding in code writing, preprocessing, model optimization, and troubleshooting. Results: The model demonstrated an overall precision of 0.89 on the testing set, with an accuracy of 0.88, a recall of 0.88, and an F1-score of 0.885. It presented better results for multiple categories, particularly in the panesophageal pressurization category, with precision = 0.99 and recall = 0.99, yielding a balanced F1-score of 0.99. Conclusions: This study demonstrates the potential of artificial intelligence, particularly Gemini, in aiding the creation of robust deep learning models for medical image analysis, solving not just simple binary classification problems but more complex, multi-class image classification tasks.

Cereals, as the world’s most consumed food, face challenges related to nutrient quality due to climate change and increased production impacting soil health. In this study, we investigated the vitamin and mineral content, polyphenols, and antioxidant activity in cereals from Western Romania, analyzing whole and hulled wheat, rye, oat, and soybeans before and after heat treatment. Samples from 2022 crops were processed into dough and subjected to 220 °C for 30 min. The results reveal that, despite efforts to optimize nutrient content, cereals, particularly after heat processing, exhibited lower vitamin and mineral levels than the recommended daily intake. The decrease in polyphenols and antioxidant capacity was notable, with rye flour experiencing the largest decline (15%). Mineral analysis showed copper levels in decorticated wheat decreased by 82.5%, while iron in rye decreased by 5.63%. Soy flour consistently displayed the highest calcium, magnesium, and potassium levels, whereas oat flour had the highest zinc and copper levels before and after heat processing. The study highlights the concerningly low vitamins and minerals contents in cereals, as well as in the final products reaching consumers in the Western part of Romania, and contributes to the assessment of measures that are meant to improve the contents of these minerals.

The initial clinical manifestation of acute mesenteric ischemia poses a diagnostic challenge, often leading to delays in identification and subsequent surgical intervention, contributing to adverse outcomes. Serum biomarkers, offering insights into the underlying pathophysiology, hold promise as prognostic indicators for acute mesenteric ischemia. This systematic review comprehensively explores the role of blood biomarkers in predicting clinical outcomes during follow-up for patients with mesenteric ischemia. A thorough literature search across the PubMed, Cochrane Library, and EMBASE databases yielded 33 relevant publications investigating the efficacy of serum biomarkers in predicting outcomes for mesenteric ischemia. Numerous studies underscore the utility of blood biomarkers in swiftly and accurately differentiating between causes of mesenteric ischemia, facilitating a prompt diagnosis. Elevated levels of specific biomarkers, particularly D-dimers, consistently correlate with heightened mortality risk and poorer clinical outcomes. While certain serum indicators exhibit substantial potential in associating with mesenteric ischemia, further research through rigorous human trials is imperative to enhance their consistent predictive ability during the follow-up period. This study underscores the diagnostic and prognostic significance of specific biomarkers for mesenteric ischemia, emphasizing the necessity for standardized procedures in future investigations.

Background: Small bowel disorders present a diagnostic challenge due to the limited accessibility of the small intestine. Accurate diagnosis is made with the aid of specific procedures, like capsule endoscopy or double-ballon enteroscopy, but they are not usually solicited and not widely accessible. This study aims to assess and compare the diagnostic effectiveness of enteroscopy and video capsule endoscopy (VCE) when combined with artificial intelligence (AI) algorithms for the automatic detection of small bowel diseases. Materials and methods: We performed an extensive literature search for relevant studies about AI applications capable of identifying small bowel disorders using enteroscopy and VCE, published between 2012 and 2023, employing PubMed, Cochrane Library, Google Scholar, Embase, Scopus, and ClinicalTrials.gov databases. Results: Our investigation discovered a total of 27 publications, out of which 21 studies assessed the application of VCE, while the remaining 6 articles analyzed the enteroscopy procedure. The included studies portrayed that both investigations, enhanced by AI, exhibited a high level of diagnostic accuracy. Enteroscopy demonstrated superior diagnostic capability, providing precise identification of small bowel pathologies with the added advantage of enabling immediate therapeutic intervention. The choice between these modalities should be guided by clinical context, patient preference, and resource availability. Studies with larger sample sizes and prospective designs are warranted to validate these results and optimize the integration of AI in small bowel diagnostics. Conclusions: The current analysis demonstrates that both enteroscopy and VCE with AI augmentation exhibit comparable diagnostic performance for the automatic detection of small bowel disorders.

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.

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.

Wine, one of the world’s oldest and most popular beverages, has a distinct variety matching a diverse base of consumers worldwide. The study was conducted in two counties of Romania in order to identify consumers’ perception towards wine consumption, as well as the driving factors behind wine consumption and the decision process of choosing a certain type of wine. Thus, four factors were identified through principal component analysis: intrinsic cues and consumers’ experience, extrinsic cues and origin, notoriety and the label and package of the wine, correlating them with the socio-demographic characteristics of our respondents. It might be concluded that the intrinsic cues and consumer experience ranked highest among the priorities of the participants between 35 and 45 years old when choosing a certain type of wine. Additionally, notoriety was more valued by people with higher income, and people with experience in the domain inclined to pay more for a bottle of wine.