Prevalence of Acute on Chronic Liver Failure in Autoimmune Hepatitis, Treatment Response and Mortality Burden Assessment: A Region-Predominant Systematic Review and Meta-Analysis.

Acute-on-chronic liver failure (ACLF) is a global health problem. Little scientific evidence exists on its prevalence in autoimmune hepatitis. Treatment response and mortality outcomes have also been reported differently. The study was conducted to estimate the overall prevalence of ACLF among patients with autoimmune hepatitis (AIH) and determine the associated treatment response and mortality. We scrutinized wide literature in Scopus, PubMed, Embase, Web of Science, and Cochrane, and assessed published articles completely, studies performed and reported from around the globe, until December 07, 2023, according to the PROSPERO registered protocol (CRD42023412176). Studies (retrospective and prospective cohort study type) that stated the ACLF development among established AIH cases were considered. Features of the study, duration of follow-up, and numeric patient information were retrieved from the studies included. The research paper quality was checked for risk of bias. Random effect meta-analysis with metaregression and subsection scrutinies were performed with R. The main outcome was the collective prevalence of ACLF in the AIH patients, whereas treatment response and mortality in AIH-associated ACLF were secondary outcomes. Six studies were involved with confirmed diagnoses in 985 AIH patients for the data synthesis. The pooled prevalence of ACLF in the explored patients was 12% (95% CI: 8-17) ( P =0.01). Heterogeneity was found to be high in the present meta-analysis ( I2 =72%; P < 0.01). For the secondary endpoint analysis, the pooled prevalence of complete remission at 1-year follow-up was 71% (0.52; 0.85), and mortality from the ACLF-AIH patient population was 32% (95% CI: 18-50). Sensitivity analysis showed no influence on the overall estimations of the pooled prevalence of ACLF by omitting studies one by one. One in 10 AIH patients likely present with ACLF. The response to treatment is seen in two-thirds of patients, and mortality is high.

Are paid tools worth the cost? A prospective cross-over study to find the right tool for plagiarism detection.

Background: The increasing pressure to publish research has led to a rise in plagiarism incidents, creating a need for effective plagiarism detection software. The importance of this study lies in the high cost variation amongst the available options for plagiarism detection. By uncovering the advantages of these low-cost or free alternatives, researchers could access the appropriate tools for plagiarism detection. This is the first study to compare four plagiarism detection tools and assess factors impacting their effectiveness in identifying plagiarism in AI-generated articles. Methodology: A prospective cross-over study was conducted with the primary objective to compare Overall Similarity Index(OSI) of four plagiarism detection software(iThenticate, Grammarly, Small SEO Tools, and DupliChecker) on AI-generated articles. ChatGPT was used to generate 100 articles, ten from each of ten general domains affecting various aspects of life. These were run through four software, recording the OSI. Flesch Reading Ease Score(FRES), Gunning Fog Index(GFI), and Flesch-Kincaid Grade Level(FKGL) were used to assess how factors, such as article length and language complexity, impact plagiarism detection. Results: The study found significant variation in OSI(p < 0.001) among the four software, with Grammarly having the highest mean rank(3.56) and Small SEO Tools having the lowest(1.67). Pairwise analyses revealed significant differences(p < 0.001) between all pairs except for Small SEO Tools-DupliChecker. Number of words showed a significant correlation with OSI for iThenticate(p < 0.05) but not for the other three. FRES had a positive correlation, and GFI had a negative correlation with OSI by DupliChecker. FKGL negatively correlated with OSI by Small SEO Tools and DupliChecker. Conclusion: Grammarly is unexpectedly most effective in detecting plagiarism in AI-generated articles compared to the other tools. This could be due to different softwares using diverse data sources. This highlights the potential for lower-cost plagiarism detection tools to be utilized by researchers.