An Unhealthy Relationship: Viral Manipulation of the Nuclear Receptor Superfamily
Bibliographic record
Abstract
The nuclear receptor (NR) superfamily is a diverse group of over 50 proteins whose function is to regulate the transcription of a vast array of cellular genes. These proteins are able to tune transcription over an extremely dynamic range due to the fact that they may act as either transcriptional activators or repressors depending on promoter context and ligand status. Due to these unique properties, diverse families of viruses have evolved strategies to exploit NRs in order to regulate expression of their own genes and to optimize the cellular milieu to facilitate the viral lifecycle. While the specific NRs targeted by these viruses vary, the strategies used to target them are common. This is accomplished at the cis-level by incorporation of nuclear receptor response elements into the viral genome and at the trans-level by viral proteins that target NRs directly or indirectly to modulate their function. The specific NR(s) targeted by a particular virus are likely to be reflective of the tissue tropism of the virus in question. Thus, the essential role played by NRs in the replication cycles of such diverse viruses underscores the importance of understanding their functions in the context of specific infections. This knowledge will allow appropriate considerations to be made when treating infected individuals with hormone-associated diseases and will potentially assist in the rational design of novel antiviral therapeutics.
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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 | 0.001 | 0.001 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".