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Role of COVID-19 Genotype in Pathogenesis

2021· article· en· W4212965753 on OpenAlexaff
Mehnaz Tanveer, Syed A. Aziz

Bibliographic record

VenueINTERNATIONAL JOURNAL OF ENDORSING HEALTH SCIENCE RESEARCH (IJEHSR) · 2021
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsGenotypeVirologyCoronavirus disease 2019 (COVID-19)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)PathogenesisBiologyGenotypingTransmission (telecommunications)GeneticsAlleleCoronavirusVirusMedicineImmunologyGeneDiseasePathology

Abstract

fetched live from OpenAlex

Background: Corona viruses are not new to us, and there are 15 different variants known to us. In the last 20 years, this is the fourth coronavirus outbreak, and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) seems to be the deadliest among all, with the ability to continue producing more contagious variants. In this mini-review, we highlighted the genotypic variance of the pathogenesis of COVID-19. 
 Methodology: The article tracks the history and the genotypic variance of Corona virus. The literature was searched using the terms COVID-19, SARS-CoV-2, Corona virus, genotypic variance etc. via Google Scholar, and PubMed.
 Results: Comparative modelling and molecular studies revealed some essential variations in the intermolecular interaction between Angiotensin-converting enzyme 2 (ACE-2) alleles and SARS-CoV-2 spike protein. This shows an interesting result for two ACE-2 alleles, rs73635825 (S19P) and rs143936283 (E329G), their low binding affinity and lack of some of the critical residues in the complex formation with SARS-CoV-2 spike protein. This may be suggestive of intrinsic resistance to some scale against the SARS-CoV-2 infection.
 Conclusion: The SARS-CoV-2 S protein appears to be a promising immunogen for protection, but its role in preventing transmission is still unclear.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.198
Threshold uncertainty score0.976

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.152
GPT teacher head0.522
Teacher spread0.370 · 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 teacher head, 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

Citations2
Published2021
Admission routes1
Has abstractyes

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