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Record W2109122063 · doi:10.2741/singwi

Potential nuclease-based strategies for HIV gene therapy

2000· review· en· W2109122063 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

VenueFrontiers in bioscience · 2000
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsUniversity of Toronto
FundersMedical Research CouncilMedical Research Council Canada
KeywordsGenetic enhancementNucleaseGeneBiologyGenome editingRNAVirusGene deliveryVirologyLentivirusComputational biologyGenomeGeneticsViral disease

Abstract

fetched live from OpenAlex

Gene therapy for HIV (human immunodeficiency virus) involves the introduction of a therapeutic gene into the infected individual for the purposes of reducing viral load and ultimately reconstituting a healthy immune system. Clinical trials for HIV gene therapy have not yet reported therapeutic benefit. In addition to improving the efficiency of gene delivery and the maintenance of gene expression, better therapeutic genes must be designed before this therapy becomes available to patients. A new class of therapeutic genes expressing nucleases may be designed. These nucleases may be classified into three categories based on their mode of action: (i) 'targeted nucleases' for specifically cleaving HIV RNA within the cell, (ii) 'colocalized nucleases' for cleaving HIV genomic DNA or RNA present within the cell or progeny virus, and (iii) 'cytotoxic nucleases' for conferring selective toxicity to HIV-infected cells. The focus of this review is on the design and application of these nucleases for HIV gene therapy.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.989
Threshold uncertainty score1.000

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 science0.0010.000
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 head0.015
GPT teacher head0.314
Teacher spread0.299 · 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