Latency-Reversing Agents Induce Differential Responses in Distinct Memory CD4 T Cell Subsets in Individuals on Antiretroviral Therapy
Bibliographic record
Abstract
Latent proviruses persist in central (T CM ), transitional (T TM ), and effector (T EM ) memory cells. We measured the levels of cellular factors involved in HIV gene expression in these subsets. The highest levels of acetylated H4, active nuclear factor κB (NF-κB), and active positive transcription elongation factor b (P-TEFb) were measured in T EM , T CM , and T TM cells, respectively. Vorinostat and romidepsin display opposite abilities to induce H4 acetylation across subsets. Protein kinase C (PKC) agonists are more efficient at inducing NF-κB phosphorylation in T CM cells but more potent at activating PTEF-b in the T EM subset. We selected the most efficient latency-reversing agents (LRAs) and measured their ability to reverse latency in each subset. While ingenol alone has modest activities in the three subsets, its combination with a histone deacetylase inhibitor (HDACi) dramatically increases latency reversal in T CM cells. Altogether, these results indicate that cellular HIV reservoirs are differentially responsive to common LRAs and suggest that combination of compounds will be required to achieve latency reversal in all subsets.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".