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Record W2101549718 · doi:10.1086/339706

Broad Nucleoside‐Analogue Resistance Implications for Human Immunodeficiency Virus Type 1 Reverse‐Transcriptase Mutations at Codons 44 and 118

2002· article· en· W2101549718 on OpenAlexaff
Laura Romanó, Giulietta Venturi, Stuart Bloor, Richard Harrigan, Brendan Larder, Jennifer C. Major, Maurizio Zazzi

Bibliographic record

VenueThe Journal of Infectious Diseases · 2002
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS drug development and treatment
Canadian institutionsAIDS Vancouver
Fundersnot available
KeywordsDidanosineZalcitabineStavudineAbacavirLamivudineVirologyReverse transcriptaseZidovudineBiologyNucleoside analogueNucleosideNucleoside Reverse Transcriptase InhibitorResistance mutationReverse-transcriptase inhibitorVirusGeneticsGeneViral diseaseRNASida

Abstract

fetched live from OpenAlex

Two large, independent human immunodeficiency virus type 1 resistance databases containing >7700 reverse-transcriptase (RT) sequences were used to analyze the epidemiology of amino acid substitutions at codons 44 and 118, which confer moderate lamivudine resistance in the presence of zidovudine resistance. As expected, E44A/D and V118I mutations were strongly associated with M41L, D67N, L210W, and T215Y but also with other mutations, including K43E/N/Q, T69D, V75M, H208Y, R211K, and K219R. Both E44D and V118I were more frequently associated with stavudine and didanosine than with zidovudine and lamivudine treatment. However, selection of E44A/D and V118I was also detected in association with a switch to other nucleoside RT inhibitors, including zalcitabine and abacavir. Site-directed mutagenesis confirmed that 44D and 118I can decrease phenotypic susceptibility not only to lamivudine but also to most other nucleoside analogues, particularly stavudine and abacavir. Thus, substitutions at RT codons 44 and 118 have broad implications in nucleoside RT inhibitor resistance in the setting of several nucleoside-associated mutations.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.356
Threshold uncertainty score0.503

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.025
GPT teacher head0.281
Teacher spread0.256 · 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 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

Citations56
Published2002
Admission routes1
Has abstractyes

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