Haverkamp, A.-K. | bioRxiv Science

bioRxiv2022

A NEW VARIANT OF HEPATITIS A VIRUS CAUSING TRANSIENT LIVER ENZYME ELEVATIONS IN MAURITIUS-ORIGIN LABORATORY-HOUSED CYNOMOLGUS MACAQUES

Hepatitis A virus (HAV) infects humans and non-human primates causing an acute self-limited illness. Three HAV genotypes have been described for humans and three genotypes have been described for non-human primates. We observed transiently elevated liver enzymes in Mauritius-origin laboratory-housed macaques in Germany and were not able to demonstrate HAV by serology and PCR. Using deep sequencing, we have identified a new HAV genotype with 86% nucleotide sequence homology to HAV genotype IV capsid proteins and approximately 80% nucleotide homology to other HAV genotypes. In situ hybridization indicates persistence in the biliary epithelium up to 3 months after liver enzymes were elevated. Vaccination using a commercial vaccine against human HAV prevented reoccurrence of liver enzyme elevations. Since available assays for HAV did not detect this new variant, knowledge of its existence may ameliorate potential significant epidemiological and research implications in laboratories globally. Article Summary LineA new genotype of hepatitis A virus, that was not identifiable by available diagnostic assays, caused liver enzyme elevations in laboratory-housed Mauritius-origin Cynomolgus macaques.

microbiology↗

bioRxiv2021

Computer-Assisted Mitotic Count Using a Deep Learning-based Algorithm Improves Inter-Observer Reproducibility and Accuracy in canine cutaneous mast cell tumors

The mitotic count (MC) is an important histological parameter for prognostication of malignant neoplasms. However, it has inter- and intra-observer discrepancies due to difficulties in selecting the region of interest (MC-ROI) and in identifying/classifying mitotic figures (MFs). Recent progress in the field of artificial intelligence has allowed the development of high-performance algorithms that may improve standardization of the MC. As algorithmic predictions are not flawless, the computer-assisted review by pathologists may ensure reliability. In the present study we have compared partial (MC-ROI preselection) and full (additional visualization of MF candidate proposal and display of algorithmic confidence values) computer-assisted MC analysis to the routine (unaided) MC analysis by 23 pathologists for whole slide images of 50 canine cutaneous mast cell tumors (ccMCTs). Algorithmic predictions aimed to assist pathologists in detecting mitotic hotspot locations, reducing omission of MF and improving classification against imposters. The inter-observer consistency for the MC significantly increased with computer assistance (interobserver correlation coefficient, ICC = 0.92) compared to the unaided approach (ICC = 0.70). Classification into prognostic stratifications had a higher accuracy with computer assistance. The algorithmically preselected MC-ROIs had a consistently higher MCs than the manually selected MC-ROIs. Compared to a ground truth (developed with immunohistochemistry for phosphohistone H3), pathologist performance in detecting individual MF was augmented when using computer assistance (F1-score of 0.68 increased to 0.79) with a reduction in false negatives by 38%. The results of this study prove that computer assistance may lead to a more reproducible and accurate MCs in ccMCTs.

pathology↗