Sivasubramaniam, Priyadharshini

AimThe aim of this study was to compare the histopathologic features of MMF‐induced colitis and colonic GVHD and develop a digital tool using deep learning convolutional neural networks (CNNs) to semi‐automate the quantification of eosinophils.MethodsMMF and GVHD colitis cases were retrieved. A cohort of transplant patients who were at risk for developing GVHD and were on MMF at the time of biopsy was also identified. Cases were reviewed for histologic features and a deep learning CNN was trained to quantify lamina propria eosinophils. Clinical history was collected.ResultsNinety‐five patients were included in the study group: GVHD (n = 37), MMF (n = 25) and GVHD vs. MMF (n = 33). Within the GVHD vs. MMF group, one patient was thought to have definitive MMF colitis and one possible MMF colitis. The GVHD group had a significantly higher apoptotic count compared to the MMF group (median: 7 vs. 2, P = 0.03). Neuroendocrine aggregates were present in the GVHD (N = 12) and GVHD vs. MMF (N = 4) groups while absent in the MMF group. The MMF group had a higher semi‐quantitative eosinophil score and a higher automated eosinophil count as detected by the deep learning CNN (median eos/HPF: MMF 19.2, GVHD vs. MMF 3.6, GVHD 3.4, P < 0.001).ConclusionsLower eosinophil counts, higher apoptotic counts and neuroendocrine cell aggregates favour the diagnosis of GVHD. AI models for eosinophil quantification may serve as an ancillary tool for assessing this differential diagnosis. Given the rarity of MMF‐induced colitis in SCT patients, pathologists should have a high threshold for making this diagnosis in this population.

Context.— Social media is a powerful tool in pathology education and professional networking that connects pathologists and pathology trainees from around the world. Twitter (X) appears to be the most popular social media platform pathologists use to share pathology-related content and connect with other pathologists. Although there has been some published research on pathology-related activity on Twitter during short time frames, to date there has not been published research examining pathology-related Twitter activity in totality from its earliest days of activity to recently. Objective.— To comprehensively evaluate the use of pathology on Twitter (X) during the last 10 years. Design.— Pathology-related tweets were systematically scraped from Twitter from January 2012 to January 2023 using pathology hashtags as a surrogate measure for all pathology content on Twitter. COVID-related tweets were approximated by tweets containing the term “COVID.” Results.— There were 591 812 unique pathology-related tweets identified during the time period, with #pathology being the most common hashtag used and #PathTwitter becoming more popular since 2020. There has been positive annual growth of pathology Twitter, with peaks in use during major pathology conferences. During the initial phases of the COVID-19 pandemic, a sustained increase in pathology tweets was observed. Conclusions.— Pathology Twitter has grown during the last 10 years and has become increasingly popular for pathology education and networking. With the changing landscape of social media platforms, this study provides an understanding of how pathology medical education and professional networking uses of social media happen and evolve over time.

Background: Alcohol-associated hepatitis (AH) is among the deadliest liver diseases, but its incidence is poorly defined. The aim of our study was to define the incidence of AH meeting the National Institute on Alcohol Abuse and Alcoholism criteria and to identify risk factors for AH. Methods: We conducted a retrospective cohort study using the Rochester epidemiology project database on adult patients hospitalized with AH between January 1, 2000 and December 31, 2018. Patients were screened using ICD-9 codes and then included if they met the National Institute on Alcohol Abuse and Alcoholism criteria on manual chart review. Baseline demographics, comorbidities, access to care, liver-related complications, and outcomes were obtained. The HOUsing-based index of SocioEconomic status index was used to measure socioeconomic status. Incidence rates were calculated in cases per 100,000 person-years of follow-up. Results: Among 204 patients, the cumulative AH incidence was 6.8 per 100,000 person-years. Between 2000–2004 and 2015–2018, AH incidence among males increased from 8.4 to 14.7 per 100,000 py, whereas AH incidence among females increased by 7-fold from 0.8 to 5.9 per 100,000 py. Such increases among females were accompanied by increases in comorbid depression and anxiety. The proportion of patients with AH in the lower socioeconomic status quartiles increased from 62.9% between 2000 and 2004 to 73.3% between 2015 and 2019. Conclusions: The incidence of AH is increasing rapidly, especially among females and individuals of lower socioeconomic status. There are areas of unmet need in preventative measures and treatments for comorbid psychiatric disorders in patients at high risk of AH.