Pathophysiologic Mapping of Chronic Liver Diseases With Longitudinal Multiparametric MRI in Animal Models.

OBJECTIVES: Chronic liver diseases (CLDs) have diverse etiologies. To better classify CLDs, we explored the ability of longitudinal multiparametric MRI (magnetic resonance imaging) in depicting alterations in liver morphology, inflammation, and hepatocyte and macrophage activity in murine high-fat diet (HFD)- and carbon tetrachloride (CCl4)-induced CLD models. MATERIALS AND METHODS: Mice were either untreated, fed an HFD for 24 weeks, or injected with CCl4 for 8 weeks. Longitudinal multiparametric MRI was performed every 4 weeks using a 7 T MRI scanner, including T1/T2 relaxometry, morphological T1/T2-weighted imaging, and fat-selective imaging. Diffusion-weighted imaging was applied to assess fibrotic remodeling and T1-weighted and T2*-weighted dynamic contrast-enhanced MRI and dynamic susceptibility contrast MRI using gadoxetic acid and ferucarbotran to target hepatocytes and the mononuclear phagocyte system, respectively. Imaging data were associated with histopathological and serological analyses. Principal component analysis and clustering were used to reveal underlying disease patterns. RESULTS: The MRI parameters significantly correlated with histologically confirmed steatosis, fibrosis, and liver damage, with varying importance. No single MRI parameter exclusively correlated with 1 pathophysiological feature, underscoring the necessity for using parameter patterns. Clustering revealed early-stage, model-specific patterns. Although the HFD model exhibited pronounced liver fat content and fibrosis, the CCl4 model indicated reduced liver fat content and impaired hepatocyte and macrophage function. In both models, MRI biomarkers of inflammation were elevated. CONCLUSIONS: Multiparametric MRI patterns can be assigned to pathophysiological processes and used for murine CLD classification and progression tracking. These MRI biomarker patterns can directly be explored clinically to improve early CLD detection and differentiation and to refine treatments.

Histopathological biomarkers for predicting the tumour accumulation of nanomedicines.

The clinical prospects of cancer nanomedicines depend on effective patient stratification. Here we report the identification of predictive biomarkers of the accumulation of nanomedicines in tumour tissue. By using supervised machine learning on data of the accumulation of nanomedicines in tumour models in mice, we identified the densities of blood vessels and of tumour-associated macrophages as key predictive features. On the basis of these two features, we derived a biomarker score correlating with the concentration of liposomal doxorubicin in tumours and validated it in three syngeneic tumour models in immunocompetent mice and in four cell-line-derived and six patient-derived tumour xenografts in mice. The score effectively discriminated tumours according to the accumulation of nanomedicines (high versus low), with an area under the receiver operating characteristic curve of 0.91. Histopathological assessment of 30 tumour specimens from patients and of 28 corresponding primary tumour biopsies confirmed the score's effectiveness in predicting the tumour accumulation of liposomal doxorubicin. Biomarkers of the tumour accumulation of nanomedicines may aid the stratification of patients in clinical trials of cancer nanomedicines.

[Development and progress of the National Autopsy Network (NATON)]. / Entwicklung und Fortschritte des Nationalen Obduktionsnetzwerks (NATON).

BACKGROUND: Autopsies have long been considered the gold standard for quality assurance in medicine, yet their significance in basic research has been relatively overlooked. The COVID-19 pandemic underscored the potential of autopsies in understanding pathophysiology, therapy, and disease management. In response, the German Registry for COVID-19 Autopsies (DeRegCOVID) was established in April 2020, followed by the DEFEAT PANDEMIcs consortium (2020-2021), which evolved into the National Autopsy Network (NATON). DEREGCOVID: DeRegCOVID collected and analyzed autopsy data from COVID-19 deceased in Germany over three years, serving as the largest national multicenter autopsy study. Results identified crucial factors in severe/fatal cases, such as pulmonary vascular thromboemboli and the intricate virus-immune interplay. DeRegCOVID served as a central hub for data analysis, research inquiries, and public communication, playing a vital role in informing policy changes and responding to health authorities. NATON: Initiated by the Network University Medicine (NUM), NATON emerged as a sustainable infrastructure for autopsy-based research. NATON aims to provide a data and method platform, fostering collaboration across pathology, neuropathology, and legal medicine. Its structure supports a swift feedback loop between research, patient care, and pandemic management. CONCLUSION: DeRegCOVID has significantly contributed to understanding COVID-19 pathophysiology, leading to the establishment of NATON. The National Autopsy Registry (NAREG), as its successor, embodies a modular and adaptable approach, aiming to enhance autopsy-based research collaboration nationally and, potentially, internationally.

Operational greenhouse-gas emissions of deep learning in digital pathology: a modelling study.

BACKGROUND: Deep learning is a promising way to improve health care. Image-processing medical disciplines, such as pathology, are expected to be transformed by deep learning. The first clinically applicable deep-learning diagnostic support tools are already available in cancer pathology, and their number is increasing. However, data on the environmental sustainability of these tools are scarce. We aimed to conduct an environmental-sustainability analysis of a theoretical implementation of deep learning in patient-care pathology. METHODS: For this modelling study, we first assembled and calculated relevant data and parameters of a digital-pathology workflow. Data were breast and prostate specimens from the university clinic at the Institute of Pathology of the Rheinisch-Westfälische Technische Hochschule Aachen (Aachen, Germany), for which commercially available deep learning was already available. Only specimens collected between Jan 1 and Dec 31, 2019 were used, to omit potential biases due to the COVID-19 pandemic. Our final selection was based on 2 representative weeks outside holidays, covering different types of specimens. To calculate carbon dioxide (CO2) or CO2 equivalent (CO2 eq) emissions of deep learning in pathology, we gathered relevant data for exact numbers and sizes of whole-slide images (WSIs), which were generated by scanning histopathology samples of prostate and breast specimens. We also evaluated different data input scenarios (including all slide tiles, only tiles containing tissue, or only tiles containing regions of interest). To convert estimated energy consumption from kWh to CO2 eq, we used the internet protocol address of the computational server and the Electricity Maps database to obtain information on the sources of the local electricity grid (ie, renewable vs non-renewable), and estimated the number of trees and proportion of the local and world's forests needed to sequester the CO2 eq emissions. We calculated the computational requirements and CO2 eq emissions of 30 deep-learning models that varied in task and size. The first scenario represented the use of one commercially available deep-learning model for one task in one case (1-task), the second scenario considered two deep-learning models for two tasks per case (2-task), the third scenario represented a future, potentially automated workflow that could handle 7 tasks per case (7-task), and the fourth scenario represented the use of a single potential, large, computer-vision model that could conduct multiple tasks (multitask). We also compared the performance (ie, accuracy) and CO2 eq emissions of different deep-learning models for the classification of renal cell carcinoma on WSIs, also from Rheinisch-Westfälische Technische Hochschule Aachen. We also tested other approaches to reducing CO2 eq emissions, including model pruning and an alternative method for histopathology analysis (pathomics). FINDINGS: The pathology database contained 35 552 specimens (237 179 slides), 6420 of which were prostate specimens (10 115 slides) and 11 801 of which were breast specimens (19 763 slides). We selected and subsequently digitised 140 slides from eight breast-cancer cases and 223 slides from five prostate-cancer cases. Applying large deep-learning models on all WSI tiles of prostate and breast pathology cases would result in yearly CO2 eq emissions of 7·65 metric tons (t; 95% CI 7·62-7·68) with the use of a single deep-learning model per case; yearly CO2 eq emissions were up to 100·56 t (100·21-100·99) with the use of seven deep-learning models per case. CO2 eq emissions for different deep-learning model scenarios, data inputs, and deep-learning model sizes for all slides varied from 3·61 t (3·59-3·63) to 2795·30 t (1177·51-6482·13. For the estimated number of overall pathology cases worldwide, the yearly CO2 eq emissions varied, reaching up to 16 megatons (Mt) of CO2 eq, requiring up to 86 590 km2 (0·22%) of world forest to sequester the CO2 eq emissions. Use of the 7-task scenario and small deep-learning models on slides containing tissue only could substantially reduce CO2 eq emissions worldwide by up to 141 times (0·1 Mt, 95% CI 0·1-0·1). Considering the local environment in Aachen, Germany, the maximum CO2 eq emission from the use of deep learning in digital pathology only would require 32·8% (95% CI 13·8-76·6) of the local forest to sequester the CO2 eq emissions. A single pathomics run on a tissue could provide information that was comparable to or even better than the output of multitask deep-learning models, but with 147 times reduced CO2 eq emissions. INTERPRETATION: Our findings suggest that widespread use of deep learning in pathology might have considerable global-warming potential. The medical community, policy decision makers, and the public should be aware of this potential and encourage the use of CO2 eq emissions reduction strategies where possible. FUNDING: German Research Foundation, European Research Council, German Federal Ministry of Education and Research, Health, Economic Affairs and Climate Action, and the Innovation Fund of the Federal Joint Committee.

Engineering Mesoscopic 3D Tumor Models with a Self-Organizing Vascularized Matrix.

Advanced in vitro systems such as multicellular spheroids and lab-on-a-chip devices have been developed, but often fall short in reproducing the tissue scale and self-organization of human diseases. A bioprinted artificial tumor model is introduced with endothelial and stromal cells self-organizing into perfusable and functional vascular structures. This model uses 3D hydrogel matrices to embed multicellular tumor spheroids, allowing them to grow to mesoscopic scales and to interact with endothelial cells. It is shown that angiogenic multicellular tumor spheroids promote the growth of a vascular network, which in turn further enhances the growth of cocultivated tumor spheroids. The self-developed vascular structure infiltrates the tumor spheroids, forms functional connections with the bioprinted endothelium, and can be perfused by erythrocytes and polystyrene microspheres. Moreover, cancer cells migrate spontaneously from the tumor spheroid through the self-assembled vascular network into the fluid flow. Additionally, tumor type specific characteristics of desmoplasia, angiogenesis, and metastatic propensity are preserved between patient-derived samples and tumors derived from this same material growing in the bioreactors. Overall, this modular approach opens up new avenues for studying tumor pathophysiology and cellular interactions in vitro, providing a platform for advanced drug testing while reducing the need for in vivo experimentation.

Amitriptyline inhibits bronchoconstriction and directly promotes dilatation of the airways.

INTRODUCTION: The standard therapy for bronchial asthma consists of combinations of acute (short-acting ß2-sympathomimetics) and, depending on the severity of disease, additional long-term treatment (including inhaled glucocorticoids, long-acting ß2-sympathomimetics, anticholinergics, anti-IL-4R antibodies). The antidepressant amitriptyline has been identified as a relevant down-regulator of immunological TH2-phenotype in asthma, acting-at least partially-through inhibition of acid sphingomyelinase (ASM), an enzyme involved in sphingolipid metabolism. Here, we investigated the non-immunological role of amitriptyline on acute bronchoconstriction, a main feature of airway hyperresponsiveness in asthmatic disease. METHODS: After stimulation of precision cut lung slices (PCLS) from mice (wildtype and ASM-knockout), rats, guinea pigs and human lungs with mediators of bronchoconstriction (endogenous and exogenous acetylcholine, methacholine, serotonin, endothelin, histamine, thromboxane-receptor agonist U46619 and leukotriene LTD4, airway area was monitored in the absence of or with rising concentrations of amitriptyline. Airway dilatation was also investigated in rat PCLS by prior contraction induced by methacholine. As bronchodilators for maximal relaxation, we used IBMX (PDE inhibitor) and salbutamol (ß2-adrenergic agonist) and compared these effects with the impact of amitriptyline treatment. Isolated perfused lungs (IPL) of wildtype mice were treated with amitriptyline, administered via the vascular system (perfusate) or intratracheally as an inhalation. To this end, amitriptyline was nebulized via pariboy in-vivo and mice were ventilated with the flexiVent setup immediately after inhalation of amitriptyline with monitoring of lung function. RESULTS: Our results show amitriptyline to be a potential inhibitor of bronchoconstriction, induced by exogenous or endogenous (EFS) acetylcholine, serotonin and histamine, in PCLS from various species. The effects of endothelin, thromboxane and leukotrienes could not be blocked. In acute bronchoconstriction, amitriptyline seems to act ASM-independent, because ASM-deficiency (Smdp1-/-) did not change the effect of acetylcholine on airway contraction. Systemic as well as inhaled amitriptyline ameliorated the resistance of IPL after acetylcholine provocation. With the flexiVent setup, we demonstrated that the acetylcholine-induced rise in central and tissue resistance was much more marked in untreated animals than in amitriptyline-treated ones. Additionally, we provide clear evidence that amitriptyline dilatates pre-contracted airways as effectively as a combination of typical bronchodilators such as IBMX and salbutamol. CONCLUSION: Amitriptyline is a drug of high potential, which inhibits acute bronchoconstriction and induces bronchodilatation in pre-contracted airways. It could be one of the first therapeutic agents in asthmatic disease to have powerful effects on the TH2-allergic phenotype and on acute airway hyperresponsiveness with bronchoconstriction, especially when inhaled.

Semiautomated pipeline for quantitative analysis of heart histopathology.

BACKGROUND: Heart diseases are among the leading causes of death worldwide, many of which lead to pathological cardiomyocyte hypertrophy and capillary rarefaction in both patients and animal models, the quantification of which is both technically challenging and highly time-consuming. Here we developed a semiautomated pipeline for quantification of the size of cardiomyocytes and capillary density in cardiac histology, termed HeartJ, by generating macros in ImageJ, a broadly used, open-source, Java-based software. METHODS: We have used modified Gomori silver staining, which is easy to perform and digitize in high throughput, or Fluorescein-labeled lectin staining. The latter can be easily combined with other stainings, allowing additional quantitative analysis on the same section, e.g., the size of cardiomyocyte nuclei, capillary density, or single-cardiomyocyte protein expression. We validated the pipeline in a mouse model of cardiac hypertrophy induced by transverse aortic constriction, and in autopsy samples of patients with and without aortic stenosis. RESULTS: In both animals and humans, HeartJ-based histology quantification revealed significant hypertrophy of cardiomyocytes reflecting other parameters of hypertrophy and rarefaction of microvasculature and enabling the analysis of protein expression in individual cardiomyocytes. The analysis also revealed that murine and human cardiomyocytes had similar diameters in health and extent of hypertrophy in disease confirming the translatability of our murine cardiac hypertrophy model. HeartJ enables a rapid analysis that would not be feasible by manual methods. The pipeline has little hardware requirements and is freely available. CONCLUSIONS: In summary, our analysis pipeline can facilitate effective and objective quantitative histological analyses in preclinical and clinical heart samples.

Anti-DNA-IgM Favors the Detection of NET-Associated Extracellular DNA.

During inflammatory responses, neutrophils enter the sites of attack where they execute various defense mechanisms. They (I) phagocytose microorganisms, (II) degranulate to release cytokines, (III) recruit various immune cells by cell-type specific chemokines, (IV) secrete anti-microbials including lactoferrin, lysozyme, defensins and reactive oxygen species, and (V) release DNA as neutrophil extracellular traps (NETs). The latter originates from mitochondria as well as from decondensed nuclei. This is easily detected in cultured cells by staining of DNA with specific dyes. However, in tissues sections the very high fluorescence signals emitted from the condensed nuclear DNA hamper the detection of the widespread, extranuclear DNA of the NETs. In contrast, when we employ anti-DNA-IgM antibodies, they are unable to penetrate deep into the tightly packed DNA of the nucleus, and we observe a robust signal for the extended DNA patches of the NETs. To validate anti-DNA-IgM, we additionally stained the sections for the NET-markers histone H2B, myeloperoxidase, citrullinated histone H3, and neutrophil elastase. Altogether, we have described a fast one-step procedure for the detection of NETs in tissue sections, which provides new perspectives to characterize neutrophil-associated immune reactions in disease.

Effect of Cellular and Microenvironmental Multidrug Resistance on Tumor-Targeted Drug Delivery in Triple-Negative Breast cancer.

Multidrug resistance (MDR) reduces the efficacy of chemotherapy. Besides inducing the expression of drug efflux pumps, chemotherapy treatment alters the composition of the tumor microenvironment (TME), thereby potentially limiting tumor-directed drug delivery. To study the impact of MDR signaling in cancer cells on TME remodeling and nanomedicine delivery, we generated multidrug-resistant 4T1 triple-negative breast cancer (TNBC) cells by exposing sensitive 4T1 cells to gradually increasing doxorubicin concentrations. In 2D and 3D cell cultures, resistant 4T1 cells are presented with a more mesenchymal phenotype and produced increased amounts of collagen. While sensitive and resistant 4T1 cells showed similar tumor growth kinetics in vivo, the TME of resistant tumors was enriched in collagen and fibronectin. Vascular perfusion was also significantly increased. Fluorophore-labeled polymeric (∼10 nm) and liposomal (∼100 nm) drug carriers were administered to mice with resistant and sensitive tumors. Their tumor accumulation and penetration were studied using multimodal and multiscale optical imaging. At the whole tumor level, polymers accumulate more efficiently in resistant than in sensitive tumors. For liposomes, the trend was similar, but the differences in tumor accumulation were insignificant. At the individual blood vessel level, both polymers and liposomes were less able to extravasate out of the vasculature and penetrate the interstitium in resistant tumors. In a final in vivo efficacy study, we observed a stronger inhibitory effect of cellular and microenvironmental MDR on liposomal doxorubicin performance than free doxorubicin. These results exemplify that besides classical cellular MDR, microenvironmental drug resistance features should be considered when aiming to target and treat multidrug-resistant tumors more efficiently.

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.

A Pleomorphic Puzzle: Heterogeneous Pulmonary Vascular Occlusions in Patients with COVID-19.

Vascular occlusions in patients with coronavirus diseases 2019 (COVID-19) have been frequently reported in severe outcomes mainly due to a dysregulation of neutrophils mediating neutrophil extracellular trap (NET) formation. Lung specimens from patients with COVID-19 have previously shown a dynamic morphology, categorized into three types of pleomorphic occurrence based on histological findings in this study. These vascular occlusions in lung specimens were also detected using native endogenous fluorescence or NEF in a label-free method. The three types of vascular occlusions exhibit morphology of DNA rich neutrophil elastase (NE) poor (type I), NE rich DNA poor (type II), and DNA and NE rich (type III) cohort of eleven patients with six males and five females. Age and gender have been presented in this study as influencing variables linking the occurrence of several occlusions with pleomorphic contents within a patient specimen and amongst them. This study reports the categorization of pleomorphic occlusions in patients with COVID-19 and the detection of these occlusions in a label-free method utilizing NEF.

[Update on collaborative autopsy-based research in German pathology, neuropathology, and forensic medicine]. / Update zur kooperativen autopsiebasierten Forschung in der deutschen Pathologie, Neuropathologie und Gerichtsmedizin.

BACKGROUND: Autopsies are a valuable tool for understanding disease, including COVID-19. MATERIALS AND METHODS: The German Registry of COVID-19 Autopsies (DeRegCOVID), established in April 2020, serves as the electronic backbone of the National Autopsy Network (NATON), launched in early 2022 following DEFEAT PANDEMIcs. RESULTS: The NATON consortium's interconnected, collaborative autopsy research is enabled by an unprecedented collaboration of 138 individuals at more than 35 German university and non-university autopsy centers through which pathology, neuropathology, and forensic medicine autopsy data including data on biomaterials are collected in DeRegCOVID and tissue-based research and methods development are conducted. More than 145 publications have now emerged from participating autopsy centers, highlighting various basic science and clinical aspects of COVID-19, such as thromboembolic events, organ tropism, SARS-CoV­2 detection methods, and infectivity of SARS-CoV-2 at autopsy. CONCLUSIONS: Participating centers have demonstrated the high value of autopsy and autopsy-derived data and biomaterials to modern medicine. The planned long-term continuation and further development of the registry and network, as well as the open and participatory design, will allow the involvement of all interested partners.

Assessing and improving the validity of COVID-19 autopsy studies - A multicentre approach to establish essential standards for immunohistochemical and ultrastructural analyses.

BACKGROUND: Autopsy studies have provided valuable insights into the pathophysiology of COVID-19. Controversies remain about whether the clinical presentation is due to direct organ damage by SARS-CoV-2 or secondary effects, such as overshooting immune response. SARS-CoV-2 detection in tissues by RT-qPCR and immunohistochemistry (IHC) or electron microscopy (EM) can help answer these questions, but a comprehensive evaluation of these applications is missing. METHODS: We assessed publications using IHC and EM for SARS-CoV-2 detection in autopsy tissues. We systematically evaluated commercially available antibodies against the SARS-CoV-2 proteins in cultured cell lines and COVID-19 autopsy tissues. In a multicentre study, we evaluated specificity, reproducibility, and inter-observer variability of SARS-CoV-2 IHC. We correlated RT-qPCR viral tissue loads with semiquantitative IHC scoring. We used qualitative and quantitative EM analyses to refine criteria for ultrastructural identification of SARS-CoV-2. FINDINGS: Publications show high variability in detection and interpretation of SARS-CoV-2 abundance in autopsy tissues by IHC or EM. We show that IHC using antibodies against SARS-CoV-2 nucleocapsid yields the highest sensitivity and specificity. We found a positive correlation between presence of viral proteins by IHC and RT-qPCR-determined SARS-CoV-2 viral RNA load (N= 35; r=-0.83, p-value <0.0001). For EM, we refined criteria for virus identification and provide recommendations for optimized sampling and analysis. 135 of 144 publications misinterpret cellular structures as virus using EM or show only insufficient data. We provide publicly accessible digitized EM sections as a reference and for training purposes. INTERPRETATION: Since detection of SARS-CoV-2 in human autopsy tissues by IHC and EM is difficult and frequently incorrect, we propose criteria for a re-evaluation of available data and guidance for further investigations of direct organ effects by SARS-CoV-2. FUNDING: German Federal Ministry of Health, German Federal Ministry of Education and Research, Berlin University Alliance, German Research Foundation, German Center for Infectious Research.

Platelet-derived growth factor (PDGF)-BB regulates the airway tone via activation of MAP2K, thromboxane, actin polymerisation and Ca2+-sensitisation.

BACKGROUND: PDGFR-inhibition by the tyrosine kinase inhibitor (TKI) nintedanib attenuates the progress of idiopathic pulmonary fibrosis (IPF). However, the effects of PDGF-BB on the airway tone are almost unknown. We studied this issue and the mechanisms beyond, using isolated perfused lungs (IPL) of guinea pigs (GPs) and precision-cut lung slices (PCLS) of GPs and humans. METHODS: IPL: PDGF-BB was perfused after or without pre-treatment with the TKI imatinib (perfused/nebulised) and its effects on the tidal volume (TV), the dynamic compliance (Cdyn) and the resistance were studied. PCLS (GP): The bronchoconstrictive effects of PDGF-BB and the mechanisms beyond were evaluated. PCLS (human): The bronchoconstrictive effects of PDGF-BB and the bronchorelaxant effects of imatinib were studied. All changes of the airway tone were measured by videomicroscopy and indicated as changes of the initial airway area. RESULTS: PCLS (GP/human): PDGF-BB lead to a contraction of airways. IPL: PDGF-BB decreased TV and Cdyn, whereas the resistance did not increase significantly. In both models, inhibition of PDGFR-(ß) (imatinib/SU6668) prevented the bronchoconstrictive effect of PDGF-BB. The mechanisms beyond PDGF-BB-induced bronchoconstriction include activation of MAP2K and TP-receptors, actin polymerisation and Ca2+-sensitisation, whereas the increase of Ca2+ itself and the activation of EP1-4-receptors were not of relevance. In addition, imatinib relaxed pre-constricted human airways. CONCLUSIONS: PDGFR regulates the airway tone. In PCLS from GPs, this regulatory mechanism depends on the ß-subunit. Hence, PDGFR-inhibition may not only represent a target to improve chronic airway disease such as IPF, but may also provide acute bronchodilation in asthma. Since asthma therapy uses topical application. This is even more relevant, as nebulisation of imatinib also appears to be effective.

First report from the German COVID-19 autopsy registry.

Background: Autopsies are an important tool in medicine, dissecting disease pathophysiology and causes of death. In COVID-19, autopsies revealed e.g., the effects on pulmonary (micro)vasculature or the nervous system, systemic viral spread, or the interplay with the immune system. To facilitate multicentre autopsy-based studies and provide a central hub supporting autopsy centres, researchers, and data analyses and reporting, in April 2020 the German COVID-19 Autopsy Registry (DeRegCOVID) was launched. Methods: The electronic registry uses a web-based electronic case report form. Participation is voluntary and biomaterial remains at the respective site (decentralized biobanking). As of October 2021, the registry included N=1129 autopsy cases, with 69271 single data points including information on 18674 available biospecimens gathered from 29 German sites. Findings: In the N=1095 eligible records, the male-to-female ratio was 1·8:1, with peaks at 65-69 and 80-84 years in males and >85 years in females. The analysis of the chain of events directly leading to death revealed COVID-19 as the underlying cause of death in 86% of the autopsy cases, whereas in 14% COVID-19 was a concomitant disease. The most common immediate cause of death was diffuse alveolar damage, followed by multi-organ failure. The registry supports several scientific projects, public outreach and provides reports to the federal health authorities, leading to legislative adaptation of the German Infection Protection Act, facilitating the performance of autopsies during pandemics. Interpretation: A national autopsy registry can provide multicentre quantitative information on COVID-19 deaths on a national level, supporting medical research, political decision-making and public discussion. Funding: German Federal Ministries of Education and Research and Health.Hintergrund: Obduktionen sind ein wichtiges Instrument in der Medizin, um die Pathophysiologie von Krankheiten und Todesursachen zu untersuchen. Im Rahmen von COVID-19 wurden durch Obduktionen z.B. die Auswirkungen auf die pulmonale Mikrovaskulatur, das Nervensystem, die systemische Virusausbreitung, und das Zusammenspiel mit dem Immunsystem untersucht. Um multizentrische, auf Obduktionen basierende Studien zu erleichtern und eine zentrale Anlaufstelle zu schaffen, die Obduktionszentren, Forscher sowie Datenanalysen und -berichte unterstützt, wurde im April 2020 das deutsche COVID-19-Autopsieregister (DeRegCOVID) ins Leben gerufen.Methoden: Das elektronische Register verwendet ein webbasiertes elektronisches Fallberichtsformular. Die Teilnahme ist freiwillig und das Biomaterial verbleibt am jeweiligen Standort (dezentrales Biobanking). Im Oktober 2021 umfasste das Register N=1129 Obduktionsfälle mit 69271 einzelnen Datenpunkten, die Informationen über 18674 verfügbare Bioproben enthielten, die von 29 deutschen Standorten gesammelt wurden.Ergebnisse: In den N=1095 ausgewerteten Datensätzen betrug das Verhältnis von Männern zu Frauen 1,8:1 mit Spitzenwerten bei 65-69 und 80-84 Jahren bei Männern und >85 Jahren bei Frauen. Die Analyse der Sequenz der unmittelbar zum Tod führenden Ereignisse ergab, dass in 86 % der Obduktionsfälle COVID-19 die zugrunde liegende Todesursache war, während in 14 % der Fälle COVID-19 eine Begleiterkrankung war. Die häufigste unmittelbare Todesursache war der diffuse Alveolarschaden, gefolgt von Multiorganversagen. Das Register unterstützt mehrere wissenschaftliche Projekte, die Öffentlichkeitsarbeit und liefert Berichte an die Bundesgesundheitsbehörden, was zu einer Anpassung des deutschen Infektionsschutzgesetzes führte und die Durchführung von Obduktionen in Pandemien erleichtert.Interpretation: Ein nationales Obduktionsregister kann multizentrische quantitative Informationen über COVID-19-Todesfälle auf nationaler Ebene liefern und damit die medizinische Forschung, die politische Entscheidungsfindung und die öffentliche Diskussion unterstützen.Finanzierung: Bundesministerien für Bildung und Forschung und für Gesundheit.

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.

On the Acceptance of "Fake" Histopathology: A Study on Frozen Sections Optimized with Deep Learning.

BACKGROUND: The fast acquisition process of frozen sections allows surgeons to wait for histological findings during the interventions to base intrasurgical decisions on the outcome of the histology. Compared with paraffin sections, however, the quality of frozen sections is often strongly reduced, leading to a lower diagnostic accuracy. Deep neural networks are capable of modifying specific characteristics of digital histological images. Particularly, generative adversarial networks proved to be effective tools to learn about translation between two modalities, based on two unconnected data sets only. The positive effects of such deep learning-based image optimization on computer-aided diagnosis have already been shown. However, since fully automated diagnosis is controversial, the application of enhanced images for visual clinical assessment is currently probably of even higher relevance. METHODS: Three different deep learning-based generative adversarial networks were investigated. The methods were used to translate frozen sections into virtual paraffin sections. Overall, 40 frozen sections were processed. For training, 40 further paraffin sections were available. We investigated how pathologists assess the quality of the different image translation approaches and whether experts are able to distinguish between virtual and real digital pathology. RESULTS: Pathologists' detection accuracy of virtual paraffin sections (from pairs consisting of a frozen and a paraffin section) was between 0.62 and 0.97. Overall, in 59% of images, the virtual section was assessed as more appropriate for a diagnosis. In 53% of images, the deep learning approach was preferred to conventional stain normalization (SN). CONCLUSION: Overall, expert assessment indicated slightly improved visual properties of converted images and a high similarity to real paraffin sections. The observed high variability showed clear differences in personal preferences.