Emergence of a Complex Relationship between HIV-1 and the microRNA Pathway
Bibliographic record
Abstract
Recent experimental evidences support the existence of an increasingly complex and multifaceted interaction between viruses and the microRNA-guided RNA silencing machinery of human cells. The discovery of small interfering RNAs (siRNAs), which are designed to mediate cleavage of specific messenger RNAs (mRNAs), prompted virologists to establish therapeutic strategies based on siRNAs with the aim to suppress replication of several viruses, including human immunodeficiency virus type 1 (HIV-1). It has been appreciated only recently that viral RNAs can also be processed endogenously by the microRNA-generating enzyme Dicer or recognized by cellular miRNAs, in processes that could be viewed as an adapted antiviral defense mechanism. Known to repress mRNA translation through recognition of specific binding sites usually located in their 3' untranslated region, miRNAs of host or viral origin may exert regulatory effects towards host and/or viral genes and influence viral replication and/or the host response to viral infection. This article summarizes our current state of knowledge on the relationship between HIV-1 and miRNA-guided RNA silencing, and discusses the different aspects of their interaction.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".