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Record W4319027073 · doi:10.1016/j.cell.2023.01.039

Angiotensin-converting enzyme 2—at the heart of the COVID-19 pandemic

2023· review· en· W4319027073 on OpenAlexafffund
Gavin Y. Oudit, Kaiming Wang, Anissa Viveiros, Max J. Kellner, Josef Penninger

Bibliographic record

VenueCell · 2023
Typereview
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsUniversity of British ColumbiaUniversity of Alberta
FundersCanadian Institutes of Health ResearchEuropean CommissionUniversity of AlbertaEuropean Federation of Pharmaceutical Industries and AssociationsÖsterreichischen Akademie der WissenschaftenHeart and Stroke Foundation of Canada
KeywordsBiologyAngiotensin-converting enzyme 2PandemicCoronavirus disease 2019 (COVID-19)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)VirologyTropism2019-20 coronavirus outbreakCoronavirusReceptorViral entryPeptidyl-Dipeptidase AEnzymeVirusViral replicationInfectious disease (medical specialty)GeneticsBiochemistryDiseaseInternal medicineMedicine

Abstract

fetched live from OpenAlex

ACE2 is the indispensable entry receptor for SARS-CoV and SARS-CoV-2. Because of the COVID-19 pandemic, it has become one of the most therapeutically targeted human molecules in biomedicine. ACE2 serves two fundamental physiological roles: as an enzyme, it alters peptide cascade balance; as a chaperone, it controls intestinal amino acid uptake. ACE2's tissue distribution, affected by co-morbidities and sex, explains the broad tropism of coronaviruses and the clinical manifestations of SARS and COVID-19. ACE2-based therapeutics provide a universal strategy to prevent and treat SARS-CoV-2 infections, applicable to all SARS-CoV-2 variants and other emerging zoonotic coronaviruses exploiting ACE2 as their cellular receptor.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.002

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 head0.186
GPT teacher head0.419
Teacher spread0.233 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations124
Published2023
Admission routes2
Has abstractyes

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