Evidence map›Paper›PMID 32831521›Full record

ArticleBioinformation2020

Putative Natural History of CoViD-19.

Francesco Chiappell

Abstract readEditorial
In one paragraph

Article in Bioinformation, 2020. 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
–field-weighted citation impact
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.

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

1 author.

Francesco ChiappellProfessor Emeritus, UCLA Center for the Health Sciences, Los Angeles, CA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The Severe Acute Respiratory Syndrome Corona Virus2 (SARS-CoV2) is responsible for Corona Virus Disease 2019 (CoViD-19), the pandemic that has afflicted close to two million people worldwide, and has taken the lives of over 120,000 patients since its first report in late December 2019. Per million people globally, the infection rate is close to 250 with a death rate of close to 14 (death rate average global death rate: 6.06%; for comparison, revised estimate of the 1918 influenza pandemic had an average global death rate of 5.4% [1]). About 400,000 SARS-CoV2-positive patients have been declared 'recovered', although it is not clear to date what exactly that entails. To be clear, the natural history of SARS-CoV2 infection and of the patho-physiology of CoViD-19 remains shrouded in relative confusion, in part due to the exceedingly virulent nature of the virus, as manifest by its elevated morbidity and mortality, and the fast accumulation of clinical observations and research evidence. Many pieces of a complex puzzle are emerging all at once and their organization into a coherent and cogent picture of the natural history of CoViD-19 is arduous and still wanting. Here, we discuss the recent findings in the context of the available evidence. We propose a putative prediction model of the natural history of CoViD-19. We highlight putative loci and modes of therapeutic intervention that may become beneficial preventive and treatment modalities for individuals at risk of SARS-CoV2 infection and CoViD-19 patients.

Indexed as

angiotensin-converting enzyme-2 (ACE2)Basigin CD147clustered regularly interspaced short palindromic repeats (CRISPR)Corona Virus Disease 2019 (CoViD-19)cytokine synthesis inhibitory factor (IL10)Exopeptidase CD26Peptidase Targeted Immunoregulation (PeTIr)platelet tissue factor CD142Severe Acute Respiratory Syndrome Corona Virus2 (SARS-CoV2)transferrin receptor CD71transmembrane protease serine-2 (TMPRSS2)

Identifiers

PMID32831521
PMCPMC7434958

What OpenQuestion holds

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LicenceCC BY
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.