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Assenmacher, C.-A.

Publications and source records attributed to Assenmacher, C.-A..

2 recordsLinked to original sources
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↗
bioRxiv2021

RIPK1 activates distinct gasdermins in macrophages and neutrophils upon pathogen blockade of innate immune signalling

Injection of effector proteins to block host innate immune signalling is a common strategy used by many pathogenic organisms to establish an infection. Pathogenic Yersinia species for example inject the acetyltransferase YopJ into target cells to inhibit NF-{kappa}B and MAPK signalling. To counteract this, detection of YopJ activity in myeloid cells promotes the assembly of a RIPK1-caspase-8 death-inducing platform that confers antibacterial defence. While recent studies revealed that caspase-8 cleaves the pore-forming protein, gasdermin D (GSDMD) to trigger pyroptosis in macrophages, whether RIPK1 activates additional substrates downstream of caspase-8 to promote host defence is unclear. Here, we report that the related gasdermin family member gasdermin E (GSDME) is activated upon detection of YopJ activity in a RIPK1 kinase-dependent manner. Specifically, GSDME promotes neutrophil pyroptosis and IL-1{beta} release, which is critical for anti-Yersinia defence. During in vivo infection, IL-1{beta} neutralisation increases bacterial burden in wild type but not Gsdme-deficient mice. Thus, our study establishes GSDME as an important mediator that counteracts pathogen blockade of innate immune signalling.

immunology↗