Hyperemesis gravidarum (HG) refers to severe nausea and emesis noted during pregnancy. However, no consensus exists on the specific diagnostic criteria that can be used for this condition. The aim of the present systematic review was to summarize the available evidence regarding the severe complications observed during HG with a heightened risk of fatality. A systematic search was conducted on PubMed, Cochrane Library, EMBASE and WILEY databases for the relevant publications regarding the severe and life‑threatening complications of HG. The search terms were as follows: ‘(Hyperemesis gravidarum)’ AND (‘complications’ OR ‘severe’ OR ‘adverse pregnancy outcomes’ OR ‘stroke’ OR ‘seizures’ OR ‘Wernicke's encephalopathy’ OR ‘arrhythmias’ OR ‘pneumomediastinum’ OR ‘coagulopathy’ OR ‘electrolytic imbalance’). Abstracts, conference presentations, letters to the editor, studies written in languages other than English and editorials were all excluded. This search identified 43 studies analyzing life‑threatening complications of HG, of which 11, seven, eight and 17 articles analyzed neurological, cardiovascular, thoracic and systemic complications, respectively. Reports on life‑threatening complications were exceptionally rare in HG. The most frequent severe complications noted were Wernicke's encephalopathy, electrolyte imbalance and vitamin K deficiency. The low mortality rate for patients with HG over the last decade could be explained by the high efficiency of modern therapy, and the precise management of every complication according to current guidelines.

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.

Burnout among healthcare workers has been extensively studied since its initial recognition in 1960, with its defining characteristics established by Maslach in 1982. The syndrome, characterized by emotional exhaustion, depersonalization, and low personal accomplishment, is exacerbated by work-related stress and has profound implications for individual and societal well-being. Methods: A review of the literature, including PubMed searches and analyses of risk factors and protective measures, was conducted to assess the prevalence, impacts, and biomarkers associated with burnout among healthcare workers. Various instruments for evaluating burnout were examined, including the widely used Maslach Burnout Inventory, alongside specific tools tailored to different occupational populations. Results: Healthcare workers, particularly physicians, exhibit significantly higher rates of burnout compared to the general population. Factors such as night shifts, workload, and exposure to biohazards contribute to elevated burnout risk. Biomarkers like cortisol, melatonin, and thyroid hormones have been linked to burnout, highlighting physiological implications. Conclusions: Burnout poses significant challenges to healthcare systems globally, impacting patient care, worker retention, and overall well-being. Identifying and addressing risk factors while promoting protective factors such as resilience and social support are crucial in mitigating burnout. Further research into prevention strategies and biomarker monitoring is warranted to support the mental and physical health of healthcare workers.

Parkinson's disease (PD) is the second most frequent neurodegenerative disorder following Alzheimer's disease. Advanced stages of PD, 4 or 5 of the Hoehn and Yahr Scale, are characterized by severe motor complications, limited mobility without assistance, risk of falling, and non‑motor complications. The aim of this review was to provide a practical overview on specific artificial intelligence (AI) systems for the management of advanced stages of PD, as well as relevant technological limitations. The authors conducted a systematic search on PubMed and EMBASE with predefined keywords searching for studies published until December 2020. Full articles that satisfied the inclusion criteria were included in the systematic review. To minimize results bias, the reference list was manually searched for pertinent articles to identify any additional relevant missed publications. Exclusion criteria included the following: Other stages of PD than 4 and 5 of the Hoehn and Yahr Scale, case reports, reviews, practice guidelines, commentaries, opinions, letters, editorials, short surveys, articles in press, conference abstracts, conference papers, and abstracts published without a full article. The search identified 21 studies analyzing AI‑based applications and robotic systems used for the management of advanced stages of PD, out of which 6 articles analyzed AI‑based applications for autonomous management of pharmacologic therapy, 5 articles analyzed home‑based telemedicine systems and 10 articles analysed robot‑assisted gait training systems. The authors identified significant evidence demonstrating that current AI‑based technologies are feasible for automatic management of patients with advanced stages of PD. Improving the quality of care and reducing the cost for patients and healthcare systems are the most important advantages.

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.