RESUMEN
BACKGROUND: Artificial intelligence (AI) systems have showed promising results in digital pathology, including digital nephropathology and specifically also kidney transplant pathology. AIM: Summarize the current state of research and limitations in the field of AI in kidney transplant pathology diagnostics and provide a future outlook. MATERIALS AND METHODS: Literature search in PubMed and Web of Science using the search terms "deep learning", "transplant", and "kidney". Based on these results and studies cited in the identified literature, a selection was made of studies that have a histopathological focus and use AI to improve kidney transplant diagnostics. RESULTS AND CONCLUSION: Many studies have already made important contributions, particularly to the automation of the quantification of some histopathological lesions in nephropathology. This likely can be extended to automatically quantify all relevant lesions for a kidney transplant, such as Banff lesions. Important limitations and challenges exist in the collection of representative data sets and the updates of Banff classification, making large-scale studies challenging. The already positive study results make future AI support in kidney transplant pathology appear likely.
Asunto(s)
Inteligencia Artificial , Trasplante de Riñón , Humanos , Riñón/patología , Aprendizaje ProfundoRESUMEN
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.
Asunto(s)
Autopsia , COVID-19 , Sistema de Registros , Humanos , COVID-19/epidemiología , COVID-19/patología , Alemania/epidemiología , Pandemias , SARS-CoV-2RESUMEN
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.
Asunto(s)
COVID-19 , Subtipo H1N1 del Virus de la Influenza A , Miocarditis , Humanos , Remodelación Vascular , SARS-CoV-2 , InflamaciónRESUMEN
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.
Asunto(s)
Inteligencia Artificial , Patología , Humanos , Predicción , Conjuntos de Datos como AsuntoRESUMEN
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.
Asunto(s)
Trasplante de Riñón , Ratones , Animales , Trasplante de Riñón/efectos adversos , Linfocitos T CD8-positivos , Riñón , Envejecimiento/fisiología , Inflamación/etiología , Rechazo de Injerto/etiologíaRESUMEN
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.
RESUMEN
BACKGROUND AND AIMS: Perihilar cholangiocarcinoma (pCCA) is a hepatobiliary malignancy, with a dismal prognosis. Nerve fiber density (NFD)-a novel prognostic biomarker-describes the density of small nerve fibers without cancer invasion and is categorized into high numbers and low numbers of small nerve fibers (high vs low NFD). NFD is different than perineural invasion (PNI), defined as nerve fiber trunks invaded by cancer cells. Here, we aim to explore differences in immune cell populations and survival between high and low NFD patients. APPROACH AND RESULTS: We applied multiplex immunofluorescence (mIF) on 47 pCCA patients and investigated immune cell composition in the tumor microenvironment (TME) of high and low NFD. Group comparison and oncological outcome analysis was performed. CD8+PD-1 expression was higher in the high NFD than in the low NFD group (12.24 × 10-6 vs. 1.38 × 10-6 positive cells by overall cell count, p = 0.017). High CD8+PD-1 expression was further identified as an independent predictor of overall (OS; Hazard ratio (HR) = 0.41; p = 0.031) and recurrence-free survival (RFS; HR = 0.40; p = 0.039). Correspondingly, the median OS was 83 months (95% confidence interval (CI): 18-48) in patients with high CD8+PD-1+ expression compared to 19 months (95% CI: 5-93) in patients with low CD8+PD-1+ expression (p = 0.018 log rank). Furthermore, RFS was significantly lower in patients with low CD8+PD-1+ expression (14 months (95% CI: 6-22)) compared to patients with high CD8+PD-1+ expression (83 months (95% CI: 17-149), p = 0.018 log rank). CONCLUSIONS: PD-1+ T-cells correlate with high NFD as a prognostic biomarker and predict good survival; the biological pathway needs to be investigated.
RESUMEN
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.
Asunto(s)
COVID-19 , Oxigenación por Membrana Extracorpórea , Síndrome de Dificultad Respiratoria , Insuficiencia Respiratoria , COVID-19/complicaciones , COVID-19/terapia , Oxigenación por Membrana Extracorpórea/efectos adversos , Humanos , Hemorragias Intracraneales/complicaciones , Hemorragias Intracraneales/epidemiología , Masculino , Síndrome de Dificultad Respiratoria/terapia , Insuficiencia Respiratoria/etiología , Insuficiencia Respiratoria/terapia , SARS-CoV-2RESUMEN
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.
Asunto(s)
COVID-19/patología , COVID-19/virología , Fibrosis Pulmonar Idiopática/patología , Fibrosis Pulmonar Idiopática/virología , Macrófagos/patología , Macrófagos/virología , SARS-CoV-2/fisiología , Antígenos CD/metabolismo , Antígenos de Diferenciación Mielomonocítica/metabolismo , COVID-19/diagnóstico por imagen , Comunicación Celular , Estudios de Cohortes , Fibroblastos/patología , Regulación de la Expresión Génica , Humanos , Fibrosis Pulmonar Idiopática/diagnóstico por imagen , Fibrosis Pulmonar Idiopática/genética , Células Madre Mesenquimatosas/patología , Fenotipo , Proteoma/metabolismo , Receptores de Superficie Celular/metabolismo , Síndrome de Dificultad Respiratoria/diagnóstico por imagen , Síndrome de Dificultad Respiratoria/patología , Síndrome de Dificultad Respiratoria/virología , Tomografía Computarizada por Rayos X , Transcripción GenéticaRESUMEN
IgA nephropathy (IgAN) is the most common glomerulonephritis. It is characterized by the deposition of immune complexes containing immunoglobulin A (IgA) in the kidney's glomeruli, triggering an inflammatory process. In many patients, the disease has a progressive course, eventually leading to end-stage kidney disease. The current understanding of IgAN's pathophysiology is incomplete, with the involvement of several potential players, including the mucosal immune system, the complement system, and the microbiome. Dissecting this complex pathophysiology requires an integrated analysis across molecular, cellular, and organ scales. Such data can be obtained by employing emerging technologies, including single-cell sequencing, next-generation sequencing, proteomics, and complex imaging approaches. These techniques generate complex "big data," requiring advanced computational methods for their analyses and interpretation. Here, we introduce such methods, focusing on the broad areas of bioinformatics and artificial intelligence and discuss how they can advance our understanding of IgAN and ultimately improve patient care. The close integration of advanced experimental and computational technologies with medical and clinical expertise is essential to improve our understanding of human diseases. We argue that IgAN is a paradigmatic disease to demonstrate the value of such a multidisciplinary approach.
Asunto(s)
Glomerulonefritis por IGA , Complejo Antígeno-Anticuerpo , Inteligencia Artificial , Biología Computacional , Glomerulonefritis por IGA/etiología , Glomerulonefritis por IGA/genética , Humanos , Inmunoglobulina ARESUMEN
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.
Asunto(s)
COVID-19 , Pandemias , Autopsia , Humanos , Sistema de Registros , SARS-CoV-2RESUMEN
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.
Asunto(s)
COVID-19 , Pandemias , Autopsia , Humanos , Sistema de Registros , SARS-CoV-2RESUMEN
The WHO declared the global outbreak of SARS-CoV-2 a pandemic on March 11, 2020, and "call(ed) on all countries to exchange country experiences and practices in a transparent and timely way" (http://www.euro.who.int/en/health-topics/health-emergencies/pages/news/news/2020/03/who-announces-covid-19-outbreak-a-pandemic). To date, many medical societies have announced their intention to collect and analyze data from COVID-19 patients and some large-scale prospective data collections are already running, such as the LEOSS registry (Lean European Open Survey on SARS-CoV-2 Infected Patients) or the CAPACITYCOVID registry (registry of patients with COVID-19 including cardiovascular risk and complications). The necessity to mobilize and harmonize basic and applied research worldwide is of utmost importance (Sansonetti, 2020).
Asunto(s)
Autopsia , Betacoronavirus , Infecciones por Coronavirus/patología , Pandemias , Neumonía Viral/patología , Sistema de Registros , COVID-19 , Infecciones por Coronavirus/mortalidad , Recolección de Datos , Alemania/epidemiología , Salud Global , Humanos , Cooperación Internacional , Neumonía Viral/mortalidad , Investigación , SARS-CoV-2RESUMEN
Kidney fibrosis is the common histological end-point of progressive, chronic kidney diseases (CKDs) regardless of the underlying etiology. The hallmark of renal fibrosis, similar to all other organs, is pathological deposition of extracellular matrix (ECM). Renal ECM is a complex network of collagens, elastin, and several glycoproteins and proteoglycans forming basal membranes and interstitial space. Several ECM functions beyond providing a scaffold and organ stability are being increasingly recognized, for example, in inflammation. ECM composition is determined by the function of each of the histological compartments of the kidney, that is, glomeruli, tubulo-interstitium, and vessels. Renal ECM is a dynamic structure undergoing remodeling, particularly during fibrosis. From a clinical perspective, ECM proteins are directly involved in several rare renal diseases and indirectly in CKD progression during renal fibrosis. ECM proteins could serve as specific non-invasive biomarkers of fibrosis and scaffolds in regenerative medicine. The gold standard and currently only specific means to measure renal fibrosis is renal biopsy, but new diagnostic approaches are appearing. Here, we discuss the localization, function, and remodeling of major renal ECM components in healthy and diseased, fibrotic kidneys and the potential use of ECM in diagnostics of renal fibrosis and in tissue engineering.