bioRxiv Science⌕ Search

Biology subjects

Gazinska, P.

Publications and source records attributed to Gazinska, P..

2 recordsLinked to original sources

Bacteria-induced colitis in naked mole rats is alleviated by probiotic treatment: a new mammalian model for acute inflammatory disease

Enteropathogenic bacteria are a major cause of morbidity and mortality globally. Mouse models have been indispensable in advancing our understanding of infectious diseases caused by intestinal pathogens and in identifying physical, chemical and immunological barriers that limit these infections. However, there are significant differences between laboratory mice and human intestinal microbiota and immunobiology that underscore the need to develop other models that recapitulate the disease pathology and mucosal immune responses of human enteric diseases. Here we report how the pathogenic expansion of Citrobacter braakii in naked mole rats (NMRs) leads to colonic inflammation and epithelial injury that mimics pathological features of human hemorrhagic colitis. We observe mucosal erosions, ulcerations, depletion of goblet cells, extension of proliferative compartments to the surface of the glands, and active inflammation in the colonic lamina propria of infected NMRs. Without intervention, systemic inflammation associated with sepsis ensues in infected NMRs and results in high mortality. Interestingly, we demonstrate a strong therapeutic effect of probiotics comprising Lactobacillus, Bifidobacterium, Streptococcus salvarius subsp. thermophilus and Enterococcus faecium strains. Treatment with probiotics induces mucosal healing and restores intestinal homeostasis, including suppression of excessive proliferation of epithelial cells, replenishment of goblet cells and also has an anti-inflammatory effect. Taken together, we demonstrate that NMRs, beyond their use as an anti-ageing and disease-resistance model, can also be used to address disease mechanisms underlying infectious colitis, including disruptions in the mucosal barrier permeability, gut microbial ecology and in local and systemic immune regulation; and in testing functional probiotics strains as potential therapeutics.

pathology↗

Development of a Deep Learning model Tailored for HER2 Detection in Breast Cancer to aid pathologists in interpreting HER2-Low cases

IntroductionOver 50% of breast cancer cases are "Human epidermal growth factor receptor 2 (HER2) low breast cancer (BC)", characterized by HER2 immunohistochemistry (IHC) scores of 1+ or 2+ alongside no amplification on fluorescence in situ hybridization (FISH) testing. The development of new anti-HER2 antibody-drug conjugates (ADCs) for treating HER2-low breast cancers illustrates the importance of accurately assessing HER2 status, particularly HER2-low breast cancer. In this study, we evaluated the performance of a deep learning (DL) model for the assessment of HER2, including an assessment of the causes of discordances of HER2-Null between a pathologist and the DL model. We specifically focussed on aligning the DL model rules with the ASCO/CAP guidelines, including stained cells staining intensity and completeness of membrane staining. MethodsWe trained a DL model on a multi-centric cohort of breast cancer cases with HER2- immunohistochemistry scores (n=299). The model was validated on 2 independent multi- centric validation cohorts (n=369 and n=92), with all cases reviewed by 3 senior breast pathologists. All cases underwent a thorough review by three senior breast pathologists, with the ground truth determined by a majority consensus on the final HER2 score among the pathologists. In total, 760 breast cancer cases were utilized throughout the training and validation phases of the study. ResultsThe models concordance with the ground truth (ICC = 0.77 [0.68 - 0.83]; Fisher P = 1.32e-10) is higher than the average agreement among the 3 senior pathologists (ICC = 0.45 [0.17 - 0.65]; Fisher P = 2e-3). In the two validation cohorts, the DL model identifies 95% [93%- 98%] and 97% [91% - 100%] of HER2-low and HER2-positive tumors respectively. Discordant results were characterized by morphological features such as extended fibrosis, a high number of tumor-infiltrating lymphocytes, and necrosis, whilst some artifacts such as non- specific background cytoplasmic stain in the cytoplasm of tumor cells also cause discrepancy. ConclusionDeep learning can support pathologists interpretation of difficult HER2-low cases. Morphological variables and some specific artifacts can cause discrepant HER2-scores between the pathologist and the DL Model.

pathology↗