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The duration of infectivity of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) in living patients has been demarcated. In contrast, a possible SARS-CoV-2 infectivity of corpses and subsequently its duration under post mortem circumstances remain to be elucidated. The aim of this study was to investigate the infectivity and its duration of deceased COVID-19 (coronavirus disease) patients. Four SARS-CoV-2 infected deceased patients were subjected to medicolegal autopsy. Post mortem intervals (PMI) of 1, 4, 9 and 17 days, respectively, were documented. During autopsy, swabs and organ samples were taken and examined by RT-qPCR (real-time reverse transcription-polymerase chain reaction) for the detection of SARS-CoV-2 ribonucleic acid (RNA). Determination of infectivity was performed by means of virus isolation in cell culture. In two cases, virus isolation was successful for swabs and tissue samples of the respiratory tract (PMI 4 and 17 days). The two infectious cases showed a shorter duration of COVID-19 until death than the two non-infectious cases (2 and 11 days, respectively, compared to > 19 days), which correlates with studies of living patients, in which infectivity could be narrowed to about 6 days before to 12 days after symptom onset. Most notably, infectivity was still present in one of the COVID-19 corpses after a post-mortem interval of 17 days and despite already visible signs of decomposition. To prevent SARS-CoV-2 infections in all professional groups involved in the handling and examination of COVID-19 corpses, adequate personal safety standards (reducing or avoiding aerosol formation and wearing FFP3 [filtering face piece class 3] masks) have to be enforced for routine procedures.
The Gleason grading system remains the most powerful prognostic predictor for patients with prostate cancer since the 1960s. Its application requires highly-trained pathologists, is tedious and yet suffers from limited inter-pathologist reproducibility, especially for the intermediate Gleason score 7. Automated annotation procedures constitute a viable solution to remedy these limitations. In this study, we present a deep learning approach for automated Gleason grading of prostate cancer tissue microarrays with Hematoxylin and Eosin (H&E) staining. Our system was trained using detailed Gleason annotations on a discovery cohort of 641 patients and was then evaluated on an independent test cohort of 245 patients annotated by two pathologists. On the test cohort, the inter-annotator agreements between the model and each pathologist, quantified via Cohen’s quadratic kappa statistic, were 0.75 and 0.71 respectively, comparable with the inter-pathologist agreement (kappa = 0.71). Furthermore, the model’s Gleason score assignments achieved pathology expert-level stratification of patients into prognostically distinct groups, on the basis of disease-specific survival data available for the test cohort. Overall, our study shows promising results regarding the applicability of deep learning-based solutions towards more objective and reproducible prostate cancer grading, especially for cases with heterogeneous Gleason patterns.