Evidence map›Paper›PMID 34341671›Full record

ArticleDiagnostic histopathology (Oxford, England)2021

COVID-19 post-mortem findings: how the departed can teach us.

Mohammed Nimir, Atisha Tank, David Snead

Open access · bronzeAbstract readCase Reports
In one paragraph

Article in Diagnostic histopathology (Oxford, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.1field-weighted citation impact, top 58% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 1 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 2 institutions in 1 country.

Mohammed NimirHistopathology Specialty Trainee, Department of Cellular Pathology, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK. Conflict of interest: none declared.
Atisha TankHistopathology Specialty Trainee, Department of Cellular Pathology, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK. Conflict of interest: none declared.
David SneadHistopathology Specialty Trainee, Department of Cellular Pathology, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK. Conflict of interest: none declared.
Coventry (United Kingdom) · GBUniversity Hospitals Coventry and Warwickshire NHS Trust · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We present an asthmatic 39 year old female frontline healthcare worker in contact with COVID-19 patients, who suffered from respiratory problems for a few days, after which she was found dead by her spouse. Post-mortem (PM) examination showed lung consolidation and histological evidence of diffuse alveolar damage (DAD), in form of hyaline membrane disease, as well as atypical cells, thrombi and fibrin plugs inside pulmonary capillaries. Swab sample testing for SARS-CoV-2 confirmed the infection status. SARS-CoV-2 is a novel coronavirus which first emerged in Wuhan, China, and PM is contributing immensely to uncovering the pathophysiological mechanism of this disease. SARS-CoV-2 enters host cells via the angiotensin-converting enzyme 2 (ACE2), and can lead to a fatal pneumonia characterized histologically by DAD (the most consistent PM finding). This is due to invasion by the virus of type II pneumocytes, which leads to the death of both type I and II pneumocytes and increased capillary permeability. This compromises gas exchange which can lead eventually to cardiorespiratory failure and death.

Indexed as

ACE2DADhyaline membranepost-mortemSARS-CoV-2

Identifiers

PMID34341671
PMCPMC8318695
OpenAlexW3183145350

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.