CRISPR/Cas9: a double-edged sword when used to combat HIV infection
Bibliographic record
Abstract
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].
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".