Bibliographic record
Abstract
Infection with HIV results in the modulation of circulating levels of many host factors. Several host proteins that are up-regulated in HIV infection have the potential to influence virus replication. More specifically, the transcription of HIV-1 can be modulated in vivo by host proteins, including cytokines and chemokines. Cytokines modulate transcription mediated by the HIV-1 long terminal repeat (LTR) via multiple signal transduction pathways with resulting recruitment of numerous transcription factors, including NFkappaB, C/EBP, AP-1, TCF-1alpha, NF-IL-6 and ISGF-3. The effects on transcription may vary depending upon the cell type studied and upon the timing of the exposure of infected or transfected cells to cytokines. Furthermore, studies of cytokine mediated activation or inhibition of LTR mediated transcription may also be affected by the presence of the HIV-1 trans-activating protein, Tat, which has significant impact upon the redox state of the cell. This review will examine the complexities of the positive and negative control of HIV transcription by cytokines and chemokines.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".