Multiple levels of PKR inhibition during HIV‐1 replication
Bibliographic record
Abstract
Recent therapeutic approaches against HIV-1 include IFN in combination therapy for patients with coinfections or as an alternative strategy against the virus. These treatment options require a better understanding of the weak efficacy of the IFN-stimulated genes, such as the protein kinase RNA-activated (PKR), which results in viral progression. Activated PKR has a strong antiviral activity on HIV-1 expression and production in cell culture. However, PKR is not activated upon HIV-1 infection when the virus reaches high levels of replication, due to viral and cellular controls. PKR is activated by low levels of the HIV-1 trans-activation response (TAR) RNA element, but is inhibited by high levels of this double-stranded RNA. The viral Tat protein also counteracts PKR activation by several mechanisms. In addition, HIV-1 replicates only in cells that have a high level of the TAR RNA binding protein (TRBP), a strong inhibitor of PKR activation. Furthermore, increased levels of adenosine deaminase acting on RNA (ADAR1) are observed when HIV-1 replicates at high levels and the protein binds to PKR and inhibits its activation. Finally, the PKR activator (PACT) also binds to PKR during HIV-1 replication with no subsequent kinase activation. The combination of all the inhibiting pathways that prevent PKR phosphorylation contributes to a high HIV-1 production in permissive cells. Enhancing PKR activation by counteracting its inhibitory partners could establish an increased innate immune antiviral pathway against HIV-1 and could enhance the efficacy of the IFN treatment.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".