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Record W4393313516 · doi:10.1016/j.isci.2024.109597

Longitudinal analysis of genomic mutations in SARS-CoV-2 isolates from persistent COVID-19 patient

2024· article· en· W4393313516 on OpenAlexfundno aff
Hiroki Futatsusako, Rina Hashimoto, Masaki Yamamoto, Jumpei Ito, Yasufumi Matsumura, Hajime Yoshifuji, Kotaro Shirakawa, Akifumi Takaori‐Kondo, Kei Sato, Miki Nagao, Kazuo Takayama

Bibliographic record

VenueiScience · 2024
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsnot available
FundersKyoto UniversityCanadian Internet Registration AuthorityJapan Agency for Medical Research and Development
KeywordsVirologyMutationCoronavirus disease 2019 (COVID-19)GenomeSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Viral replicationBiologyMutantWhole genome sequencingCoronavirusGeneticsAntiviral drugVirusSequence analysisPoint mutationGeneMedicineDiseaseInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

A primary reason for the ongoing spread of coronavirus disease 2019 (COVID-19) is the continuous acquisition of mutations by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, the mechanism of acquiring mutations is not fully understood. In this study, we isolated SARS-CoV-2 from an immunocompromized patient persistently infected with Omicron strain BF.5 for approximately 4 months to analyze its genome and evaluate drug resistance. Although the patient was administered the antiviral drug remdesivir (RDV), there were no acquired mutations in RDV binding site, and all isolates exhibited susceptibility to RDV. Notably, upon analyzing the S protein sequence of the day 119 isolate, we identified mutations acquired by mutant strains emerging from the BF.5 variant, suggesting that viral genome analysis in persistent COVID-19 patients may be useful in predicting viral evolution. These results suggest mutations in SARS-CoV-2 are acquired during long-term viral replication rather than in response to antiviral drugs.

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)0.000
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.504
Threshold uncertainty score0.639

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.077
GPT teacher head0.379
Teacher spread0.302 · 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

Citations5
Published2024
Admission routes1
Has abstractyes

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