Idiopathic sudden sensorineural hearing loss after COVID-19 vaccination: a systematic review and meta-analysis.

OBJECTIVE: To provide pooled analyses on the association between COVID-19 vaccine and the incidence of idiopathic sudden sensorineural hearing loss (ISSNHL). DATA SOURCES: "Medline" via "PubMed", "EMBASE", and "Google scholar". REVIEW METHODS: Data sources were inspected from January 2020 to January 2024 using search terms relevant to vaccines for COVID-19. Included were papers with reported numbers of vaccinated populations and incidence if ISSNHL in those populations. Quality assessment was performed with the Newcastle-Ottawa Quality Assessment Scale Criteria. RESULTS: Three publications encompassing more than 191.8 million patients and at least 283 million vaccine doses were included in the quantitative data synthesis. The pooled reported incidence (95%confidence interval) of ISSNHL among COVID-19 vaccine recipients was 1.2588 per 100,000 (0.1385-3.4836). This incidence is significantly lower than the incidence of 5-27 and 60 per 100,000/year reported in the United States and in the European Union, respectively (P < 0.0001). CONCLUSION: There is no evidence to indicate that the COVID-19 vaccine is associated with the incidence of ISSNHL.

Jejunal Inflammation in Crohn's Disease: Comparison between Diffusion Weighted Magnetic Resonance Imaging and Video Capsule Endoscopy.

BACKGROUND: Jejunal disease is associated with worse prognosis in Crohn's disease. The added value of diffusion weighted imaging for evaluating jejunal inflammation related to Crohn's Disease is scarce. OBJECTIVES: To compare diffusion weighted imaging, video capsule endoscopy, and inflammatory biomarkers in the assessment of Crohn's disease involving the jejunum. METHODS: Crohn's disease patients in clinical remission were prospectively recruited and underwent magnetic resonance (MR)-enterography and video capsule endoscopy. C-reactive protein and fecal-calprotectin levels were obtained. MR-enterography images were evaluated for restricted diffusion, and apparent diffusion coefficient values were measured. The video capsule endoscopy-based Lewis score was calculated. Associations between diffusion weighted imaging, apparent diffusion coefficient, Lewis score, and inflammatory biomarkers were evaluated. RESULTS: The study included 51 patients, and 27/51 (52.9%) with video capsule endoscopies showed jejunal mucosal inflammation. Sensitivity and specificity of restricted diffusion for video capsule endoscopy mucosal inflammation were 59.3% and 37.5% for the first reader, and 66.7% and 37.5% for the second reader, respectively. Diffusion weighted imaging was not statistically associated with jejunal video capsule endoscopy inflammation (P = 0.813). CONCLUSIONS: Diffusion weighted imaging was not an effective test for evaluation of jejunal inflammation as seen by video capsule endoscopy in patients with quiescent Crohn's disease.

Advancing radiology practice and research: harnessing the potential of large language models amidst imperfections.

Large language models (LLMs) are transforming the field of natural language processing (NLP). These models offer opportunities for radiologists to make a meaningful impact in their field. NLP is a part of artificial intelligence (AI) that uses computer algorithms to study and understand text data. Recent advances in NLP include the Attention mechanism and the Transformer architecture. Transformer-based LLMs, such as GPT-4 and Gemini, are trained on massive amounts of data and generate human-like text. They are ideal for analysing large text data in academic research and clinical practice in radiology. Despite their promise, LLMs have limitations, including their dependency on the diversity and quality of their training data and the potential for false outputs. Albeit these limitations, the use of LLMs in radiology holds promise and is gaining momentum. By embracing the potential of LLMs, radiologists can gain valuable insights and improve the efficiency of their work. This can ultimately lead to improved patient care.

Research trends analysis of chronic hepatitis C versus nonalcoholic fatty liver disease: A literature review text-mining analysis of publications.

Background: Hepatits C virus (HCV) rates have lowered due to direct-acting antiviral treatment. Nonalcoholic steatohepatitis (NASH)/nonalcoholic fatty liver disease (NAFLD) is rising with no available therapy. We employed text-mining to analyze trends in HCV and NAFLD research from the past two decades. Materials and Methods: We queried PubMed for all HCV and NASH/NAFLD entries published between 2000 and 2020. We compared the total number of publications on both etiologies. We performed subanalyses for different terms of interest and for geographic origin. Results: Overall, 75,934 HCV-related entries and 24,987 NASH/NAFLD-related entries were published during the study period. Up to 2015, there was a linear upward slope in the number of annual HCV publications (154.9 publications/year, p < 0.001). In 2015, the yearly number of HCV publications started showing a downward slope (-242.2 publications/year, p < 0.001). The number of NASH/NAFLD publications showed a continuous upward slope during the study period. The NASH/NAFLD field lacks publications on screening and treatment methods. Conclusion: Trends in publications varied between both etiologies. They reflect the success of antiviral treatment for HCV. The growing rates of NAFLD/NASH and the lack of a targeted cure explain the rise in related publications.