Multiple Inhibitory Factors Act in the Late Phase of HIV-1 Replication: a Systematic Review of the Literature
Bibliographic record
Abstract
The use of lentiviral vectors for therapeutic purposes has shown promising results in clinical trials. The ability to produce a clinical-grade vector at high yields remains a critical issue. One possible obstacle could be cellular factors known to inhibit human immunodeficiency virus (HIV). To date, five HIV restriction factors have been identified, although it is likely that more factors are involved in the complex HIV-cell interaction. Inhibitory factors that have an adverse effect but do not abolish virus production are much less well described. Therefore, a gap exists in the knowledge of inhibitory factors acting late in the HIV life cycle (from transcription to infection of a new cell), which are relevant to the lentiviral vector production process. The objective was to review the HIV literature to identify cellular factors previously implicated as inhibitors of the late stages of lentivirus production. A search for publications was conducted on MEDLINE via the PubMed interface, using the keyword sequence "HIV restriction factor" or "HIV restriction" or "inhibit HIV" or "repress HIV" or "restrict HIV" or "suppress HIV" or "block HIV," with a publication date up to 31 December 2016. Cited papers from the identified records were investigated, and additional database searches were performed. A total of 260 candidate inhibitory factors were identified. These factors have been identified in the literature as having a negative impact on HIV replication. This study identified hundreds of candidate inhibitory factors for which the impact of modulating their expression in lentiviral vector production could be beneficial.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".