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Record W2399342261 · doi:10.1186/s12977-016-0270-0

CRISPR/Cas9: a double-edged sword when used to combat HIV infection

2016· article· en· W2399342261 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueRetrovirology · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsMcGill UniversityJewish General HospitalMcGill University Health Centre
FundersCanadian Institutes of Health Research
KeywordsCRISPRSWORDHuman immunodeficiency virus (HIV)MedicineVirologyCas9Computational biologyBiologyImmunologyBioinformaticsGeneticsComputer scienceWorld Wide WebGene

Abstract

fetched live from OpenAlex

The major barrier to eradication of HIV infection is the latent viral reservoir that persists despite long-term highly active antiretroviral therapy (HAART). The main reason for the existence of latently infected cells is that proviral DNA becomes integrated into the cellular genome. Theoretically, the elimination of proviral DNA from every infected cell should therefore be able to cure HIV infection. This concept has been tested in studies that employed designed recombinases [1], zinc finger nucleases (ZFNs) and transcription activator-like effector nucleases (TALENs) bearing sequence-specific DNA-binding modules that recognize HIV DNA sequences [2]. In addition, the recent development of the bacterial adaptive immune system CRISPR/Cas9 for editing of genes in mammalian cells [3, 4] quickly led to the use of this new genome editing technology to try to inhibit and eliminate infection by different viruses, including HIV-1 [5].

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.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.278
Threshold uncertainty score0.576

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 studies0.0000.000
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 head0.014
GPT teacher head0.300
Teacher spread0.286 · 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