Spirituality: The Legacy of Parapsychology

Spirituality is a topic of recent interest. Mindfulness, for example, a concept derived from the Buddhist tradition, has captivated the imagination of clinicians who package it in convenient intervention programs for patients. Spirituality and religion have been researched with reference to potential health benefi ts. Spirituality can be conceptualised as the alignment of the individual with the whole, experientially, motivationally and in action. For spirituality to unfold its true potential it is necessary to align this new movement with the mainstream of science, and vice versa. Hence, both a historical review, and a systematic attempt at integration is called for, which we are trying to give here. It is useful to go back to one of the roots: parapsychology. Parapsychology was founded as a counter movement to the rising materialist paradigm in the 19th century. Adopting the methods of the natural sciences, it tried to prove the direct infl uence of consciousness on matter. After 125 years this mission must be declared unaccomplished. Surveying the database of parapsychological research it is obvious that it will not convince sceptics: Although there are enough exceptional fi ndings, it has in general not been possible to reproduce them in replication experiments. Th is is, however, a characteristic signature of a category of eff ects which we call eff ects of generalised entanglement, predicted by a theoretical model analogous to quantum theory. Using this perspective, parapsychological eff ects can be understood, and the original aim of the founding fathers can be recovered, as well as a new, systematic understanding of spirituality be gained. Generalised entanglement is a formal and scientifi c way of explaining spirituality as alignment of an individual with a whole, which, according to the model, inevitably leads to non-local correlations

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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13054-022-03945-x

Deep-Learning based segmentation and quantification in experimental kidney histopathology

BACKGROUND: Nephropathologic analyses provide important outcomes-related data in experiments with the animal models that are essential for understanding kidney disease pathophysiology. Precision medicine increases the demand for quantitative, unbiased, reproducible, and efficient histopathologic analyses, which will require novel high-throughput tools. A deep learning technique, the convolutional neural network, is increasingly applied in pathology because of its high performance in tasks like histology segmentation. METHODS: We investigated use of a convolutional neural network architecture for accurate segmentation of periodic acid-Schiff-stained kidney tissue from healthy mice and five murine disease models and from other species used in preclinical research. We trained the convolutional neural network to segment six major renal structures: glomerular tuft, glomerulus including Bowman\u27s capsule, tubules, arteries, arterial lumina, and veins. To achieve high accuracy, we performed a large number of expert-based annotations, 72,722 in total. RESULTS: Multiclass segmentation performance was very high in all disease models. The convolutional neural network allowed high-throughput and large-scale, quantitative and comparative analyses of various models. In disease models, computational feature extraction revealed interstitial expansion, tubular dilation and atrophy, and glomerular size variability. Validation showed a high correlation of findings with current standard morphometric analysis. The convolutional neural network also showed high performance in other species used in research-including rats, pigs, bears, and marmosets-as well as in humans, providing a translational bridge between preclinical and clinical studies. CONCLUSIONS: We developed a deep learning algorithm for accurate multiclass segmentation of digital whole-slide images of periodic acid-Schiff-stained kidneys from various species and renal disease models. This enables reproducible quantitative histopathologic analyses in preclinical models that also might be applicable to clinical studies

Contamination of personal protective equipment during COVID-19 autopsies

Confronted with an emerging infectious disease at the beginning of the COVID-19 pandemic, the medical community faced concerns regarding the safety of autopsies on those who died of the disease. This attitude has changed, and autopsies are now recognized as indispensable tools for understanding COVID-19, but the true risk of infection to autopsy staff is nevertheless still debated. To clarify the rate of SARS-CoV-2 contamination in personal protective equipment (PPE), swabs were taken at nine points in the PPE of one physician and one assistant after each of 11 full autopsies performed at four centers. Swabs were also obtained from three minimally invasive autopsies (MIAs) conducted at a fifth center. Lung/bronchus swabs of the deceased served as positive controls, and SARS-CoV-2 RNA was detected by real-time RT-PCR. In 9 of 11 full autopsies, PPE samples tested RNA positive through PCR, accounting for 41 of the 198 PPE samples taken (21%). The main contaminated items of the PPE were gloves (64% positive), aprons (50% positive), and the tops of shoes (36% positive) while the fronts of safety goggles, for example, were positive in only 4.5% of the samples, and all the face masks were negative. In MIAs, viral RNA was observed in one sample from a glove but not in other swabs. Infectious virus isolation in cell culture was performed on RNA-positive swabs from the full autopsies. Of all the RNA-positive PPE samples, 21% of the glove samples, taken in 3 of 11 full autopsies, tested positive for infectious virus. In conclusion, PPE was contaminated with viral RNA in 82% of autopsies. In 27% of autopsies, PPE was found to be contaminated even with infectious virus, representing a potential risk of infection to autopsy staff. Adequate PPE and hygiene measures, including appropriate waste deposition, are therefore essential to ensure a safe work environment. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00428-021-03263-7

Emergence of qualia from brain activity or from an interaction of proto-consciousness with the brain: which one is the weirder? Available evidence and a research agenda

This contribution to the science of consciousness aims at comparing how two different theories can explain the emergence of different qualia experiences, meta-awareness, meta-cognition, the placebo effect, out-of-body experiences, cognitive therapy and meditation-induced brain changes, etc. The first theory postulates that qualia experiences derive from specific neural patterns, the second one, that qualia experiences derive from the interaction of a proto-consciousness with the brain\u2019s neural activity. From this comparison it will be possible to judge which one seems to better explain the different qualia experiences and to offer a more promising research agenda

Revisiting the assumptions and implementation details of the BAY model for vortex generator flows

Today, Vortex Generators (VGs) are becoming an integral part of a Wind Turbine blade design. However, the challenges involved in the computation of the flow around VGs are yet to be dealt with in a satisfactory manner. A large number of VG models for Reynolds Averaged Navier Stokes (RANS) solvers has been proposed and, among them, the Bender–Anderson–Yagle (BAY) model (ASME Pap. FEDSM99-6919) is one of the most popular, due to its ease of use and relatively low requirements for user input. In the present paper a thorough investigation on the performance and application of the BAY model for aerodynamic VG flows is presented. A fully resolved RANS simulation is validated against experiments and then used as a benchmark for the BAY model simulations. A case relevant to wind turbines is examined, which deals with the flow past a wind turbine airfoil at Reynolds number 0.87e6. When the grid related errors are excluded, it is found that the generated vortices are weaker in the BAY model simulations than in the fully resolved computation. The latter finding is linked to an inherent deficiency of the model, which is first found in this study and which is explained in detail

Deep learning-based classification of kidney transplant pathology: a retrospective, multicentre, proof-of-concept study

Background Histopathological assessment of transplant biopsies is currently the standard method to diagnose allograft rejection and can help guide patient management, but it is one of the most challenging areas of pathology, requiring considerable expertise, time, and effort. We aimed to analyse the utility of deep learning to preclassify histology of kidney allograft biopsies into three main broad categories (ie, normal, rejection, and other diseases) as a potential biopsy triage system focusing on transplant rejection.Methods We performed a retrospective, multicentre, proof-of-concept study using 5844 digital whole slide images of kidney allograft biopsies from 1948 patients. Kidney allograft biopsy samples were identified by a database search in the Departments of Pathology of the Amsterdam UMC, Amsterdam, Netherlands (1130 patients) and the University Medical Center Utrecht, Utrecht, Netherlands (717 patients). 101 consecutive kidney transplant biopsies were identified in the archive of the Institute of Pathology, RWTH Aachen University Hospital, Aachen, Germany. Convolutional neural networks (CNNs) were trained to classify allograft biopsies as normal, rejection, or other diseases. Three times cross-validation (1847 patients) and deployment on an external real-world cohort (101 patients) were used for validation. Area under the receiver operating characteristic curve (AUROC) was used as the main performance metric (the primary endpoint to assess CNN performance).Findings Serial CNNs, first classifying kidney allograft biopsies as normal (AUROC 0.87 [ten times bootstrapped CI 0.85-0.88]) and disease (0.87 [0.86-0.88]), followed by a second CNN classifying biopsies classified as disease into rejection (0.75 [0.73-0.76]) and other diseases (0.75 [0.72-0.77]), showed similar AUROC in cross-validation and deployment on independent real-world data (first CNN normal AUROC 0.83 [0.80-0.85], disease 0.83 [0.73-0.91]; second CNN rejection 0.61 [0.51-0.70], other diseases 0.61 [0.50-4.74]). A single CNN classifying biopsies as normal, rejection, or other diseases showed similar performance in cross-validation (normal AUROC 0.80 [0.73-0.84], rejection 0.76 [0.66-0.80], other diseases 0.50 [0.36-0.57]) and generalised well for normal and rejection classes in the real-world data. Visualisation techniques highlighted rejection-relevant areas of biopsies in the tubulointerstitium.Interpretation This study showed that deep learning-based classification of transplant biopsies could support pathological diagnostics of kidney allograft rejection. Copyright (C) 2021 The Author(s). Published by Elsevier Ltd.Immunopathology of vascular and renal diseases and of organ and celltransplantationIP