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Record W3183386301 · doi:10.1038/s41392-021-00704-2

N501Y mutation imparts cross-species transmission of SARS-CoV-2 to mice by enhancing receptor binding

2021· letter· en· W3183386301 on OpenAlexaff
Zubiao Niu, Zhengrong Zhang, Xiaoyan Gao, Peng Du, Jingjing Lu, Bohua Yan, Chenxi Wang, You Zheng, Hongyan Huang, Qiang Sun

Bibliographic record

VenueSignal Transduction and Targeted Therapy · 2021
Typeletter
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsBiotechnology Research Institute
FundersBeijing Municipal Administration of Hospitals
KeywordsSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Coronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakVirologyMutationTransmission (telecommunications)Sars virusReceptorBetacoronavirusBiologyMolecular biologyMedicineGeneticsPathologyGeneDisease

Abstract

fetched live from OpenAlex

According to the World Health Organization (WHO), as of March 8, 2021, the pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) had infected more than 116 million patients with coronavirus disease 2019 (COVID-19) ( https://covid19.who.int ). The high infectivity of SARS-CoV-2 is largely attributable to the unique sequence composition of its spike (S) glycoprotein. During the process of viral infection, this glycoprotein can be processed into two fragments. The N terminal S1 fragment is responsible for receptor binding, and the C terminal S2 fragment promotes membrane fusion. 1 SARS-CoV-2 is an RNA virus, and it has undergone frequent mutations, which have produced several variants over the past year. On top of this, the D614G mutation in the S glycoprotein has been shown to enhance viral infectivity. 2 , 3 However, the functional implications of most mutations are largely speculative and not clear.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0130.005
Insufficient payload (model declined to judge)0.0020.001

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 head0.053
GPT teacher head0.342
Teacher spread0.290 · 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 designBench or experimental
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

Citations113
Published2021
Admission routes1
Has abstractno

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