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Record W3038407673 · doi:10.1101/2020.07.02.20145003

Tissue-specific tolerance in fatal Covid-19

2020· preprint· en· W3038407673 on OpenAlexfundno aff
David A. Dorward, Clark D Russell, In Hwa Um, Mustafa Elshani, Stuart D. Armstrong, Rebekah Penrice-Randal, Tracey Millar, Chris E. B. Lerpiniere, Giulia Tagliavini, Catherine Hartley, Nadine Randle, Naomi N. Gachanja, Philippe MD Potey, Alison Anderson, Victoria Campbell, Alasdair J. Duguid, Wael Al‐Qsous, Ralph BouHaidar, J. Kenneth Baillie, Kevin Dhaliwal, W. Angus Wallace, Christopher OC Bellamy, Sandrine Prost, Colin Smith, Julian A. Hiscox, David J. Harrison, Christopher D. Lucas

Bibliographic record

VenuemedRxiv · 2020
Typepreprint
Languageen
FieldMedicine
TopicCOVID-19 Clinical Research Studies
Canadian institutionsnot available
FundersMedical Research CouncilU.S. Food and Drug AdministrationHamilton Health Sciences FoundationPublic Health EnglandUniversity of EdinburghNational Institute for Health and Care ResearchMedical Research ScotlandKidney Research UKNational Institute for Health Research Health Protection Research UnitUK Research and InnovationUniversity of OxfordLifeArcWellcome TrustMelville Charitable Trust
KeywordsInflammationPathologyLungMedicineImmunologyARDSDiffuse alveolar damageBone marrowImmunophenotypingImmune systemBiologyInternal medicineAntigen

Abstract

fetched live from OpenAlex

ABSTRACT Background Tissue inflammation is associated with organ dysfunction and death in Covid-19. The efficacy of dexamethasone in preventing mortality in critical Covid-19 suggests that inflammation has a causal role in death. Whether this deleterious inflammation is a direct response to the presence of SARS-CoV-2, or an independent immuno-pathologic process, is unknown. Methods Tissue was acquired from detailed post-mortem examinations conducted on 11 well characterised hospitalised patients with fatal Covid-19. SARS-CoV-2 organotropism was mapped at an organ level by multiplex PCR and sequencing, with cellular resolution achieved by in situ viral spike (S) protein detection. Histological evidence of inflammation and organ injury was systematically examined, and the pulmonary immune response characterized with multiplex immunofluorescence. Findings SARS-CoV-2 was detected across a wide variety of organs, most frequently in the respiratory tract but also in numerous extra-pulmonary sites. Minimal histological evidence of inflammation was identified in non-pulmonary organs despite frequent detection of viral RNA and protein. At a cellular level, viral protein was identified without adjacent inflammation in the intestine, liver and kidney. Severe inflammatory change was restricted to the lung and reticulo-endothelial system. Diffuse alveolar damage, pulmonary thrombi and a monocyte/myeloid-predominant vasculitis were the predominant pulmonary findings, though there was not a consistent association between viral presence and either the presence or nature of the inflammatory response within the lung. Immunophenotyping revealed an influx of macrophages, monocytes and T cells into pulmonary parenchyma. Bone marrow examination revealed plasmacytosis, erythroid dysplasia and iron-laden macrophages. Plasma cell excess was also present in lymph node, spleen and lung. These stereotyped reticulo-endothelial responses occurred largely independently of the presence of virus in lymphoid tissues. Conclusions Tissue inflammation and organ dysfunction in fatal Covid-19 do not map to the tissue and cellular distribution of SARS-CoV-2, demonstrating tissue-specific tolerance. We conclude that death in Covid-19 is primarily a consequence of immune-mediated, rather than pathogen-mediated, organ inflammation and injury. Funding The Chief Scientist Office, LifeArc, Medical Research Scotland, UKRI (MRC).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.151
GPT teacher head0.458
Teacher spread0.307 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations50
Published2020
Admission routes1
Has abstractyes

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