SARS-CoV-2 infection triggers profibrotic macrophage responses and lung fibrosis.

COVID-19-induced "acute respiratory distress syndrome" (ARDS) is associated with prolonged respiratory failure and high mortality, but the mechanistic basis of lung injury remains incompletely understood. Here, we analyze pulmonary immune responses and lung pathology in two cohorts of patients with COVID-19 ARDS using functional single-cell genomics, immunohistology, and electron microscopy. We describe an accumulation of CD163-expressing monocyte-derived macrophages that acquired a profibrotic transcriptional phenotype during COVID-19 ARDS. Gene set enrichment and computational data integration revealed a significant similarity between COVID-19-associated macrophages and profibrotic macrophage populations identified in idiopathic pulmonary fibrosis. COVID-19 ARDS was associated with clinical, radiographic, histopathological, and ultrastructural hallmarks of pulmonary fibrosis. Exposure of human monocytes to SARS-CoV-2, but not influenza A virus or viral RNA analogs, was sufficient to induce a similar profibrotic phenotype in vitro. In conclusion, we demonstrate that SARS-CoV-2 triggers profibrotic macrophage responses and pronounced fibroproliferative ARDS.

Stain-Independent Deep Learning-Based Analysis of Digital Kidney Histopathology.

Convolutional neural network (CNN)-based image analysis applications in digital pathology (eg, tissue segmentation) require a large amount of annotated data and are mostly trained and applicable on a single stain. Here, a novel concept based on stain augmentation is proposed to develop stain-independent CNNs requiring only one annotated stain. In this benchmark study on stain independence in digital pathology, this approach is comprehensively compared with state-of-the-art techniques including image registration and stain translation, and several modifications thereof. A previously developed CNN for segmentation of periodic acid-Schiff-stained kidney histology was used and applied to various immunohistochemical stainings. Stain augmentation showed very high performance in all evaluated stains and outperformed all other techniques in all structures and stains. Without the need for additional annotations, it enabled segmentation on immunohistochemical stainings with performance nearly comparable to that of the annotated periodic acid-Schiff stain and could further uphold performance on several held-out stains not seen during training. Herein, examples of how this framework can be applied for compartment-specific quantification of immunohistochemical stains for inflammation and fibrosis in animal models and patient biopsy specimens are presented. The results show that stain augmentation is a highly effective approach to enable stain-independent applications of deep-learning segmentation algorithms. This opens new possibilities for broad implementation in digital pathology.

High-Throughput GPCRome Screen of Pollutants Reveals the Activity of Polychlorinated Biphenyls at Melatonin and Sphingosine-1-phosphate Receptors.

Exposure to environmental pollutants is linked to numerous toxic outcomes, warranting concern about the effect of pollutants on human health. To assess the threat of pollutant exposure, it is essential to understand their biological activity. Unfortunately, gaps remain for many pollutants' specific biological activity and molecular targets. A superfamily of signaling proteins, G-protein-coupled receptors (GPCRs), has been shown as potential targets for pollutant activity. However, research investigating the pollutant activity at the GPCRome is scarce. This work explores pollutant activity across a library of human GPCRs by leveraging modern high-throughput screening techniques devised for drug discovery and pharmacology. We designed and implemented a pilot screen of eight pollutants at 314 human GPCRs and discovered specific polychlorinated biphenyl (PCB) activity at sphingosine-1-phosphate and melatonin receptors. The method utilizes open-source resources available to academic and governmental institutions to enable future campaigns that screen large numbers of pollutants. Thus, we present a novel high-throughput approach to assess the biological activity and specific targets of pollutants.

Bioactive Compounds and Potential Health Benefits through Cosmetic Applications of Cherry Stem Extract.

Cherry stems, prized in traditional medicine for their potent antioxidant and anti-inflammatory properties, derive their efficacy from abundant polyphenols and anthocyanins. This makes them an ideal option for addressing skin aging and diseases. This study aimed to assess the antioxidant and anti-inflammatory effects of cherry stem extract for potential skincare use. To this end, the extract was first comprehensively characterized by HPLC-ESI-qTOF-MS. The extract's total phenolic content (TPC), antioxidant capacity, radical scavenging efficiency, and its ability to inhibit enzymes related to skin aging were determined. A total of 146 compounds were annotated in the cherry stem extract. The extract effectively fought against NO· and HOCl radicals with IC50 values of 2.32 and 5.4 mg/L. Additionally, it inhibited HYALase, collagenase, and XOD enzymes with IC50 values of 7.39, 111.92, and 10 mg/L, respectively. Based on the promising results that were obtained, the extract was subsequently gently integrated into a cosmetic gel at different concentrations and subjected to further stability evaluations. The accelerated stability was assessed through temperature ramping, heating-cooling cycles, and centrifugation, while the long-term stability was evaluated by storing the formulations under light and dark conditions for three months. The gel formulation enriched with cherry stem extract exhibited good stability and compatibility for topical application. Cherry stem extract may be a valuable ingredient for creating beneficial skincare cosmeceuticals.

Structure Revision of a Widespread Marine Sulfonolipid Class Based on Isolation and Total Synthesis.

The cosmopolitan marine Roseobacter clade is of global biogeochemical importance. Members of this clade produce sulfur-containing amino lipids (SALs) involved in biofilm formation and marine surface colonization processes. Despite their physiological relevance and abundance, SALs have only been explored through genomic mining approaches and lipidomic studies based on mass spectrometry, which left the relative and absolute structures of SALs unresolved, hindering progress in biochemical and functional investigations. Herein, we report the structural revision of a new group of SALs, which we named cysteinolides, using a combination of analytical techniques, isolation and degradation experiments and total synthetic efforts. Contrary to the previously proposed homotaurine-based structures, cysteinolides are composed of an N,O-acylated cysteinolic acid-containing head group carrying various different (α-hydroxy)carboxylic acids. We also performed the first validated targeted-network based analysis, which allowed us to map the distribution and structural diversity of cysteinolides across bacterial lineages. Beyond offering structural insight, our research provides SAL standards and validated analytical data. This information holds significance for forthcoming investigations into bacterial sulfonolipid metabolism and biogeochemical nutrient cycling within marine environments.

Inflammation and vascular remodeling in COVID-19 hearts.

A wide range of cardiac symptoms have been observed in COVID-19 patients, often significantly influencing the clinical outcome. While the pathophysiology of pulmonary COVID-19 manifestation has been substantially unraveled, the underlying pathomechanisms of cardiac involvement in COVID-19 are largely unknown. In this multicentre study, we performed a comprehensive analysis of heart samples from 24 autopsies with confirmed SARS-CoV-2 infection and compared them to samples of age-matched Influenza H1N1 A (n = 16), lymphocytic non-influenza myocarditis cases (n = 8), and non-inflamed heart tissue (n = 9). We employed conventional histopathology, multiplexed immunohistochemistry (MPX), microvascular corrosion casting, scanning electron microscopy, X-ray phase-contrast tomography using synchrotron radiation, and direct multiplexed measurements of gene expression, to assess morphological and molecular changes holistically. Based on histopathology, none of the COVID-19 samples fulfilled the established diagnostic criteria of viral myocarditis. However, quantification via MPX showed a significant increase in perivascular CD11b/TIE2 + -macrophages in COVID-19 over time, which was not observed in influenza or non-SARS-CoV-2 viral myocarditis patients. Ultrastructurally, a significant increase in intussusceptive angiogenesis as well as multifocal thrombi, inapparent in conventional morphological analysis, could be demonstrated. In line with this, on a molecular level, COVID-19 hearts displayed a distinct expression pattern of genes primarily coding for factors involved in angiogenesis and epithelial-mesenchymal transition (EMT), changes not seen in any of the other patient groups. We conclude that cardiac involvement in COVID-19 is an angiocentric macrophage-driven inflammatory process, distinct from classical anti-viral inflammatory responses, and substantially underappreciated by conventional histopathologic analysis. For the first time, we have observed intussusceptive angiogenesis in cardiac tissue, which we previously identified as the linchpin of vascular remodeling in COVID-19 pneumonia, as a pathognomic sign in affected hearts. Moreover, we identified CD11b + /TIE2 + macrophages as the drivers of intussusceptive angiogenesis and set forward a putative model for the molecular regulation of vascular alterations.

Gross anatomical description of the extrinsic and intrinsic scapular and brachial muscles of Sapajus apella (Linnaeus, 1758).

BACKGROUND: The robust brown capuchin monkey (Sapajus apella) is a South American primate with preferences for arboreal locomotion, which requires specific thoracic limb muscle adaptations. The present investigation studied the gross anatomy of the extrinsic and intrinsic scapular and brachial muscles. METHODS: Gross dissections were performed in both thoracic limbs of four formaldehyde-fixed specimens. RESULTS: Three rhomboideus muscles were present (capitis, cervicis, and thoracis). The trapezius muscle was divided into two parts (cervicis and thoracis). The pectoralis abdominalis and omotransversarius muscles were present. The anconeus muscle was found as an individual muscle or fused to the caput mediale of the triceps brachii muscle. The brachialis muscle had among one and two heads. The anconeus epitrochlearis was absent. CONCLUSION: These muscles of Sapajus apella are adapted for arboreal locomotion and some terrestrial habits, since these have many similarities with other primates with a similar locomotor patterns.

OpenVNT: An Open Platform for VIS-NIR Technology.

Spectrometers measure diffuse reflectance and create a "molecular fingerprint" of the material under investigation. Ruggedized, small scale devices for "in-field" use cases exist. Such devices might for example be used by companies in the food supply chain for inward inspection of goods. However, their application for the industrial Internet of Things workflows or scientific research is limited due to their proprietary nature. We propose an open platform for visible and near-infrared technology (OpenVNT), an open platform for capturing, transmitting, and analysing spectral measurements. It is built for use in the field, as it is battery-powered and transmits data wireless. To achieve high accuracy, the OpenVNT instrument contains two spectrometers covering a wavelength range of 400-1700 nm. We conducted a study on white grapes to compare the performance of the OpenVNT instrument against the Felix Instruments F750, an established commercial instrument. Using a refractometer as ground truth, we built and validated models to estimate the Brix value. As a quality measure, we used coefficient of determination of the cross-validation (R2CV) between the instrument estimation and ground truth. With 0.94 for the OpenVNT and 0.97 for the F750, a comparable R2CV was achieved for both instruments. OpenVNT matches the performance of commercially available instruments at one tenth of the price. We provide an open bill of materials, building instructions, firmware, and analysis software to enable research and industrial IOT solutions without the limitations of walled garden platforms.

Detection of Plastic Granules and Their Mixtures.

Chemically pure plastic granulate is used as the starting material in the production of plastic parts. Extrusion machines rely on purity, otherwise resources are lost, and waste is produced. To avoid losses, the machines need to analyze the raw material. Spectroscopy in the visible and near-infrared range and machine learning can be used as analyzers. We present an approach using two spectrometers with a spectral range of 400-1700 nm and a fusion model comprising classification, regression, and validation to detect 25 materials and proportions of their binary mixtures. one dimensional convolutional neural network is used for classification and partial least squares regression for the estimation of proportions. The classification is validated by reconstructing the sample spectrum using the component spectra in linear least squares fitting. To save time and effort, the fusion model is trained on semi-empirical spectral data. The component spectra are acquired empirically and the binary mixture spectra are computed as linear combinations. The fusion model achieves very a high accuracy on visible and near-infrared spectral data. Even in a smaller spectral range from 400-1100 nm, the accuracy is high. The visible and near-infrared spectroscopy and the presented fusion model can be used as a concept for building an analyzer. Inexpensive silicon sensor-based spectrometers can be used.

Renal inflamm-aging provokes intra-graft inflammation following experimental kidney transplantation.

Donor age is a major risk factor for allograft outcome in kidney transplantation. The underlying cellular mechanisms and the recipient's immune response within an aged allograft have yet not been analyzed. A comprehensive immunophenotyping of naïve and transplanted young versus aged kidneys revealed that naïve aged murine kidneys harbor significantly higher frequencies of effector/memory T cells, whereas regulatory T cells were reduced. Aged kidney-derived CD8+ T cells produced more IFNγ than their young counterparts. Senescent renal CD8+ T and NK cells upregulated the cytotoxicity receptor NKG2D and the enrichment of memory-like CD49a+ CXCR6+ NK cells was documented in aged naïve kidneys. In the C57BL/6 to BALB/c kidney transplantation model, recipient-derived T cells infiltrating an aged graft produced significantly more IFNγ, granzyme B and perforin on day 7 post-transplantation, indicating an enhanced inflammatory, cytotoxic response towards the graft. Pre-treatment of aged kidney donors with the senolytic drug ABT-263 changed the recipient-derived effector molecule profile to significantly reduced levels of IFNγ and IL-10 compared to controls. Graft function after ABT-263 pre-treatment was significantly improved 28 days post kidney transplantation. In conclusion, renal senescence also occurs at the immunological level (inflamm-aging) and aged organs provoke an altered recipient-dominated immune response in the graft.

Synthesis of Functionalized δ-Hydroxy-ß-keto Esters and Evaluation of Their Anti-inflammatory Properties.

δ-Hydroxy-ß-keto esters and δ,ß-dihydroxy esters are characteristic structural motifs of statin-type natural products and drug candidates. Here, we describe the synthesis of functionalized δ-hydroxy-ß-keto esters in good yields and excellent enantioselectivities using Chan's diene and modified Mukaiyama-aldol reaction conditions. Diastereoselective reduction of δ,ß-dihydroxy esters afforded the respective syn- and anti-diols, and saponification yielded the corresponding acids. All products were evaluated for their anti-inflammatory properties, which uncovered a surprising structure-activity relationship.

Recommendations on compiling test datasets for evaluating artificial intelligence solutions in pathology.

Artificial intelligence (AI) solutions that automatically extract information from digital histology images have shown great promise for improving pathological diagnosis. Prior to routine use, it is important to evaluate their predictive performance and obtain regulatory approval. This assessment requires appropriate test datasets. However, compiling such datasets is challenging and specific recommendations are missing. A committee of various stakeholders, including commercial AI developers, pathologists, and researchers, discussed key aspects and conducted extensive literature reviews on test datasets in pathology. Here, we summarize the results and derive general recommendations on compiling test datasets. We address several questions: Which and how many images are needed? How to deal with low-prevalence subsets? How can potential bias be detected? How should datasets be reported? What are the regulatory requirements in different countries? The recommendations are intended to help AI developers demonstrate the utility of their products and to help pathologists and regulatory agencies verify reported performance measures. Further research is needed to formulate criteria for sufficiently representative test datasets so that AI solutions can operate with less user intervention and better support diagnostic workflows in the future.

Deep learning detects genetic alterations in cancer histology generated by adversarial networks.

Deep learning can detect microsatellite instability (MSI) from routine histology images in colorectal cancer (CRC). However, ethical and legal barriers impede sharing of images and genetic data, hampering development of new algorithms for detection of MSI and other biomarkers. We hypothesized that histology images synthesized by conditional generative adversarial networks (CGANs) retain information about genetic alterations. To test this, we developed a 'histology CGAN' which was trained on 256 patients (training cohort 1) and 1457 patients (training cohort 2). The CGAN synthesized 10 000 synthetic MSI and non-MSI images which contained a range of tissue types and were deemed realistic by trained observers in a blinded study. Subsequently, we trained a deep learning detector of MSI on real or synthetic images and evaluated the performance of MSI detection in a held-out set of 142 patients. When trained on real images from training cohort 1, this system achieved an area under the receiver operating curve (AUROC) of 0.742 [0.681, 0.854]. Training on the larger cohort 2 only marginally improved the AUROC to 0.757 [0.707, 0.869]. Training on purely synthetic data resulted in an AUROC of 0.743 [0.658, 0.801]. Training on both real and synthetic data further increased AUROC to 0.777 [0.715, 0.821]. We conclude that synthetic histology images retain information reflecting underlying genetic alterations in colorectal cancer. Using synthetic instead of real images to train deep learning systems yields non-inferior classifiers. This approach can be used to create large shareable data sets or to augment small data sets with rare molecular features. © 2021 The Authors. The Journal of Pathology published by John Wiley & Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland.

Intracranial hemorrhage in COVID-19 patients during extracorporeal membrane oxygenation for acute respiratory failure: a nationwide register study report.

BACKGROUND: In severe cases, SARS-CoV-2 infection leads to acute respiratory distress syndrome (ARDS), often treated by extracorporeal membrane oxygenation (ECMO). During ECMO therapy, anticoagulation is crucial to prevent device-associated thrombosis and device failure, however, it is associated with bleeding complications. In COVID-19, additional pathologies, such as endotheliitis, may further increase the risk of bleeding complications. To assess the frequency of bleeding events, we analyzed data from the German COVID-19 autopsy registry (DeRegCOVID). METHODS: The electronic registry uses a web-based electronic case report form. In November 2021, the registry included N = 1129 confirmed COVID-19 autopsy cases, with data on 63 ECMO autopsy cases and 1066 non-ECMO autopsy cases, contributed from 29 German sites. FINDINGS: The registry data showed that ECMO was used in younger male patients and bleeding events occurred much more frequently in ECMO cases compared to non-ECMO cases (56% and 9%, respectively). Similarly, intracranial bleeding (ICB) was documented in 21% of ECMO cases and 3% of non-ECMO cases and was classified as the immediate or underlying cause of death in 78% of ECMO cases and 37% of non-ECMO cases. In ECMO cases, the three most common immediate causes of death were multi-organ failure, ARDS and ICB, and in non-ECMO cases ARDS, multi-organ failure and pulmonary bacterial ± fungal superinfection, ordered by descending frequency. INTERPRETATION: Our study suggests the potential value of autopsies and a joint interdisciplinary multicenter (national) approach in addressing fatal complications in COVID-19.

Weakly Supervised Violence Detection in Surveillance Video.

Automatic violence detection in video surveillance is essential for social and personal security. Monitoring the large number of surveillance cameras used in public and private areas is challenging for human operators. The manual nature of this task significantly increases the possibility of ignoring important events due to human limitations when paying attention to multiple targets at a time. Researchers have proposed several methods to detect violent events automatically to overcome this problem. So far, most previous studies have focused only on classifying short clips without performing spatial localization. In this work, we tackle this problem by proposing a weakly supervised method to detect spatially and temporarily violent actions in surveillance videos using only video-level labels. The proposed method follows a Fast-RCNN style architecture, that has been temporally extended. First, we generate spatiotemporal proposals (action tubes) leveraging pre-trained person detectors, motion appearance (dynamic images), and tracking algorithms. Then, given an input video and the action proposals, we extract spatiotemporal features using deep neural networks. Finally, a classifier based on multiple-instance learning is trained to label each action tube as violent or non-violent. We obtain similar results to the state of the art in three public databases Hockey Fight, RLVSD, and RWF-2000, achieving an accuracy of 97.3%, 92.88%, 88.7%, respectively.

Determination of the Bioactive Effect of Custard Apple By-Products by In Vitro Assays.

Annona cherimola fruit, known as cherimoya or custard apple, is an exotic fruit from South America but is strongly produced in Andalusia, Spain. Its by-products (seeds and peel) are recognised as important sources of antioxidants, including phenolic acids, flavonoids and procyanidins. Therefore, the aim of this study was to carry out the characterization of its phenolic composition and to in vitro evaluate the bioactivity of custard apple seed and peel. Therefore, high performance liquid chromatography coupled to mass spectrometry (HPLC-ESI-qTOF-MS) was performed in order to tentatively identify their phenolic composition. In the end, 19 compounds were identified and quantified, some of them for the first time in the custard apple matrix. Then, seed and peel total phenolic content, as well as antioxidant properties, radical scavenging capacity (O2, NO, HOCl) and inhibition of enzymes involved in different pathologies (hyaluronidase, elastase, collagenase, tyrosinase, acetylcholinesterase and xanthine oxidase), were evaluated. Although both extracts showed almost similar antioxidant capacities, custard apple seed stood out slightly more than peel (171 ± 2 vs. 130.0 ± 0.4 µmol TE/g DE, resp.), especially as ·NO scavenger (IC50 1.5 ± 0.2 vs. 11.8 ± 0.3 mg/L, resp.) and hyaluronidase inhibitor (IC50 170 ± 10 vs. 460 ± 20mg/L, resp.). Finally, the application of extracts on a real human model of platelet aggregation was performed, reporting antiaggregatory effects in agonist-promoted platelet thrombus formation. All these results show that custard apple by-products are stated as interesting sources of bioactive compounds with multiple industrial applications for the development of high-added-value products, such as functional foods, nutraceuticals and cosmeceuticals, promoting the circular bioeconomy of these by-products.

Recent Analytical Approaches for the Study of Bioavailability and Metabolism of Bioactive Phenolic Compounds.

The study of the bioavailability of bioactive compounds is a fundamental step for the development of applications based on them, such as nutraceuticals, functional foods or cosmeceuticals. It is well-known that these compounds can undergo metabolic reactions before reaching therapeutic targets, which may also affect their bioactivity and possible applications. All recent studies that have focused on bioavailability and metabolism of phenolic and terpenoid compounds have been developed because of the advances in analytical chemistry and metabolomics approaches. The purpose of this review is to show the role of analytical chemistry and metabolomics in this field of knowledge. In this context, the different steps of the analytical chemistry workflow (design study, sample treatment, analytical techniques and data processing) applied in bioavailability and metabolism in vivo studies are detailed, as well as the most relevant results obtained from them.

Fragment-Based Nuclear Magnetic Resonance Screen against a Regulator of G Protein Signaling Identifies a Binding "Hot Spot".

Regulator of G protein signaling (RGS) proteins have attracted attention as a result of their primary role in directing the specificity as well as the temporal and spatial aspects of G protein-coupled receptor signaling. In addition, alterations in RGS protein expression have been observed in a number of disease states, including certain cancers. In this area, RGS17 is of particular interest. It has been demonstrated that, while RGS17 is expressed primarily in the central nervous system, it has been found to be inappropriately expressed in lung, prostate, breast, cervical, and hepatocellular carcinomas. Overexpression of RGS17 leads to dysfunction in inhibitory G protein signaling and an overproduction of the intracellular second messenger cAMP, which in turn alters the transcription patterns of proteins known to promote various cancer types. Suppressing RGS17 expression with RNA interference (RNAi) has been found to decrease tumorigenesis and sufficiently prevents cancer cell migration, leading to the hypothesis that pharmacological blocking of RGS17 function could be useful in anticancer therapies. We have identified small-molecule fragments capable of binding the RGS homology (RH) domain of RGS17 by using a nuclear magnetic resonance fragment-based screening approach. By chemical shift mapping of the two-dimensional 15 N,1 H heteronuclear single quantum coherence (HSQC) spectra of the backbone-assigned 15 N-labeled RGS17-RH, we determined the fragment binding sites to be distant from the Gα interface. Thus, our study identifies a putative fragment binding site on RGS17 that was previously unknown.

[Cooperative approach of pathology and neuropathology in the COVID-19 pandemic : German registry for COVID-19 autopsies (DeRegCOVID) and German network for autopsies in pandemics (DEFEAT PANDEMIcs)]. / Kooperatives Vorgehen der Pathologie und Neuropathologie in der COVID-19-Pandemie : Deutsches Register für COVID-19-Obduktionen (DeRegCOVID) und Deutsches Netzwerk für Autopsien in Pandemien (DEFEAT PANDEMIcs).

BACKGROUND: Autopsy is an important tool for understanding the pathogenesis of diseases, including COVID-19. MATERIAL AND METHODS: On 15 April 2020, together with the German Society of Pathology and the Federal Association of German Pathologists, the German Registry of COVID-19 Autopsies (DeRegCOVID) was launched ( www.DeRegCOVID.ukaachen.de ). Building on this, the German Network for Autopsies in Pandemics (DEFEAT PANDEMIcs) was established on 1 September 2020. RESULTS: The main goal of DeRegCOVID is to collect and distribute de facto anonymized data on potentially all autopsies of people who have died from COVID-19 in Germany in order to meet the need for centralized, coordinated, and structured data collection and reporting during the pandemic. The success of the registry strongly depends on the willingness of the respective centers to report the data, which has developed very positively so far and requires special thanks to all participating centers. The rights to own data and biomaterials (stored decentrally) remain with each respective center. The DEFEAT PANDEMIcs network expands on this and aims to strengthen harmonization and standardization as well as nationwide implementation and cooperation in the field of pandemic autopsies. CONCLUSIONS: The extraordinary cooperation in the field of autopsies in Germany during the COVID-19 pandemic is impressively demonstrated by the establishment of DeRegCOVID, the merger of the registry of neuropathology (CNS-COVID19) with DeRegCOVID and the establishment of the autopsy network DEFEAT PANDEMIcs. It gives a strong signal for the necessity, readiness, and expertise to jointly help manage current and future pandemics by autopsy-derived knowledge.

Cooperative approach of pathology and neuropathology in the COVID-19 pandemic : German registry for COVID-19 autopsies (DeRegCOVID) and German network for autopsies in pandemics (DEFEAT PANDEMIcs).

BACKGROUND: Autopsy is an important tool for understanding the pathogenesis of diseases, including COVID-19. MATERIAL AND METHODS: On 15 April 2020, together with the German Society of Pathology and the Federal Association of German Pathologists, the German Registry of COVID-19 Autopsies (DeRegCOVID) was launched ( www.DeRegCOVID.ukaachen.de ). Building on this, the German Network for Autopsies in Pandemics (DEFEAT PANDEMIcs) was established on 1 September 2020. RESULTS: The main goal of DeRegCOVID is to collect and distribute de facto anonymized data on potentially all autopsies of people who have died from COVID-19 in Germany in order to meet the need for centralized, coordinated, and structured data collection and reporting during the pandemic. The success of the registry strongly depends on the willingness of the respective centers to report the data, which has developed very positively so far and requires special thanks to all participating centers. The rights to own data and biomaterials (stored decentrally) remain with each respective center. The DEFEAT PANDEMIcs network expands on this and aims to strengthen harmonization and standardization as well as nationwide implementation and cooperation in the field of pandemic autopsies. CONCLUSIONS: The extraordinary cooperation in the field of autopsies in Germany during the COVID-19 pandemic is impressively demonstrated by the establishment of DeRegCOVID, the merger of the registry of neuropathology (CNS-COVID19) with DeRegCOVID and the establishment of the autopsy network DEFEAT PANDEMIcs. It gives a strong signal for the necessity, readiness, and expertise to jointly help manage current and future pandemics by autopsy-derived knowledge.