Biology subjects

Klopfleisch, R.

Publications and source records attributed to Klopfleisch, R..

3 recordsLinked to original sources

bioRxiv2022

Cytologic Scoring of Equine Exercise-Induced Pulmonary Hemorrhage (EIPH): Performance of Human Experts and a Deep Learning-Based Algorithm

Exercise-induced pulmonary hemorrhage (EIPH) is a relevant respiratory disease in sport horses which can be diagnosed by examination of bronchoalveolar lavage fluid (BALF) cells using the total hemosiderin score (THS). The aim of this study was to evaluate the diagnostic accuracy and reproducibility of trained annotators and to validate a deep learning-based algorithm for the THS. Digitized, iron-stained cytological specimens were prepared from 52 equine BALF samples. Ten annotators produced a THS for each slide according to published methods. The reference methods for comparing annotators and algorithmic performance included a ground truth dataset, the mean annotators THSs, and chemical iron measurements. Results of the study showed that annotators had marked inter-observer variability of the THS, which was mostly due to a systematic error between annotators in grading the intracytoplasmatic hemosiderin content of individual macrophages. Regarding overall measurement error between the annotators, 87.7% of the variance of the could be reduced by using standardized grades based on the ground truth. The algorithm was highly consistent with the ground truth in assigning hemosiderin grades. Compared to the ground truth THS, annotators had an accuracy of diagnosing EIPH (THS of < or [≥] 75) of 75.7% whereas the algorithm had an accuracy of 92.3% with no marked difference in correlation to chemical iron measurements. The results show that deep learning-based algorithms are useful for improving reproducibility and routine applicability of the THS. For THS by experts, a diagnostic uncertainty interval of 40 to 110 is proposed. THSs within this interval have insufficient reproducibility regarding the EIPH diagnosis.

pathology↗

bioRxiv2021

Immune competence and spleen size scale with colony status in the naked mole-rat

Naked mole-rats (NM-R; Heterocephalus glaber) live in multi-generational colonies with a social hierarchy, show low cancer incidence and long life-spans. Here we asked if such extreme physiology might have an immune component. The spleen is the largest lymphoid organ and plays an essential role in response to immunological insults and may participate in combating cancer and slowing ageing. We investigated the anatomy, molecular composition and function of the NM-R spleen using RNA-sequencing and histological analysis in healthy animals. We found that spleen size in healthy NM-Rs varies considerably. We therefore classified NM-Rs according to spleen size as NM-Rs with small spleens or enlarged spleens. Animals with enlarged spleens showed potentially better anti-microbial profiles and were much more likely to have a high rank within the colony. Splenomegaly was associated with infection in sick NM-Rs, but not in NM-Rs with enlarged spleens. In all healthy NM-Rs splenic erythropoiesis, megakaryopoiesis and myelopoiesis were increased, but B lymphopoiesis was reduced and splenic marginal zone showed markedly altered morphology when compared to other rodents. However, in NM-Rs lymphocytes were found in secondary sites such as lymph nodes, gut lymphoid nodules and thymus. Thus, the NM-R spleen is a major site of adult hematopoiesis under normal physiological conditions. Overall, the NM-R immune system seems to rely mainly on innate immune responses with a more restricted adaptive immune response. We propose that the anatomical plasticity of the spleen might be regulated by social interaction and gives immunological advantage to increase the life-span of higher ranked animals.

immunology↗

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↗