MétaCan
Menu
Back to cohort
Record W3040656837 · doi:10.1093/emph/eoaa021

Lions, tigers and kittens too: ACE2 and susceptibility to COVID-19

2020· article· en· W3040656837 on OpenAlexafffund
Sabateeshan Mathavarajah, Graham Dellaire

Bibliographic record

VenueEvolution Medicine and Public Health · 2020
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaKillam TrustsDalhousie University
KeywordsCATSDomesticationVirusCoronavirusBiologySevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Angiotensin-converting enzyme 2VirologyCoronavirus disease 2019 (COVID-19)Amino acid residueReceptor2019-20 coronavirus outbreakTransmission (telecommunications)Peptide sequenceZoologyGeneticsGeneInfectious disease (medical specialty)MedicineOutbreakDisease

Abstract

fetched live from OpenAlex

Abstract SARS-CoV-2 (Severe Acute Respiratory Syndrome coronavirus 2) has been reported to infect domesticated animals in a species-specific manner, where cats were susceptible but not dogs. Using the recently published crystal structure of the SARS-CoV-2 spike protein complexed with the human host cell receptor angiotensin converting enzyme 2 (ACE2), we characterized the structure and evolution of ACE2 in several of these species and identify a single interacting amino acid residue conserved between human and Felidae ACE2 but not in Canidae that correlates with virus susceptibility. Using computational analyses we describe how this site likely affects ACE2 targeting by the virus. Thus, we highlight how evolution-based approaches can be used to form hypotheses and study animal transmission of such viruses in the future.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.033

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 studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.149
GPT teacher head0.423
Teacher spread0.274 · 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

Citations48
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueEvolution Medicine and Public HealthSame topicSARS-CoV-2 and COVID-19 ResearchFrench-language works237,207