Gruver, Aaron M.

ABSTRACT Background Despite histologic remission (HR) being important in management of ulcerative colitis, the impact of endoscopic sampling on newer scoring systems remains uncertain. This study examines the outcome of applying PICaSSO Histologic Remission Index (PHRI) scoring on biopsies collected during routine clinical care and investigates the impact of endoscopic sampling variation on PHRI scoring and HR status. Methods A retrospective survey of UC endoscopic biopsies ( N = 128, 559 bites, 1–13 bites/biopsy) was evaluated using standard H&E stain and myeloperoxidase (MPO) immunohistochemistry to aid neutrophil identification. Convolutional neural networks (CNN), trained on colonic tissue architecture and cytomorphology features, were deployed alongside pathologist interpretations. In silico modeling of bite‐derived virtual biopsies ( N = 19,760) was investigated to determine the impact of sampling variation on PHRI scores and compared with ground truth clinical samples. Results PHRI MPO scoring displayed improved sensitivity and specificity for identifying neutrophils, compared with PHRI H&E . CNN‐derived PHRI scoring was most accurate for chronic active and chronic active with ulceration categories, with greatest variability observed in chronic inactive biopsies. Virtual patient biopsies comprised of < 5 endoscopic bites were impacted most by sampling permutations. Limited variability in PHRI scores was observed in samples consisting of ≥ 5 bites. Conclusions HR status (PHRI = 0) determination was slightly impacted by sampling. Biopsies consisting of < 5 endoscopic bites exhibited the most variability. Conservative sampling of 5–7 endoscopic bites may produce more accurate assessments of microscopic heterogeneity in clinical studies employing quantitative histologic measurements to assess disease activity and remission. Additionally, incorporating MPO increased the sensitivity and specificity of detecting active inflammation for PHRI scoring.

Abstract Post-translational modifications (PTM) of tau are implicated in Alzheimer disease (AD) progression and are established biomarkers in cerebrospinal fluid and plasma; however, the labor-intensive nature of conventional proteomics limits their investigation in histology-specific contexts. We describe the findings of an artificial intelligence-guided laser capture microdissection (LCM) pipeline for harvesting neurofibrillary tangles (NFTs) by maturation level into pretangles (pre-NFTs), mature tangles (iNFTs), and ghost tangles (eNFTs) using 38 cases obtained from 2 independent biobanks. We evaluated the performance characteristics of proprietary machine learning algorithms for the subclassification of these NFT categories in anti-pTau217-stained whole slide images of entorhinal cortex, hippocampus, frontal, and parietal cortex sections. Overall precision/recall/F1 scores were highest for Classifier A (0.6/0.46/0.5). The best performing algorithm was used to guide LCM capture and inform NFT enrichment. Targeted proteomics on tau signature peptides (pTau181, pTau217, and TauMTBR) was performed on approximately 1250 NFT collections. The results demonstrated that their abundance increased from pretangles to mature tangles (∼2-11-fold increase), and that this was followed by a sharp reduction in ghost tangles (∼3-116-fold decrease), with pTau217 showing the most drastic change. Pathologist-trained NFT classifiers represent an objective albeit imperfect means to enrich specific morphologic forms permitting coupled LCM-MS (mass spectrometry) to investigate AD-associated PTM.