Differentiation into an Effector Memory Phenotype Potentiates HIV-1 Latency Reversal in CD4<sup>+</sup>T Cells
Bibliographic record
Abstract
By performing phenotypic analysis of latency reversal in CD4+T cells from virally suppressed individuals, we identify the TEMsubset as the largest contributor to the inducible HIV reservoir. Differential responses of memory CD4+T cell subsets to latency-reversing agents (LRAs) demonstrate that HIV gene expression is associated with heightened expression of transcriptional pathways associated with differentiation, acquisition of effector function, and cell cycle entry.In vitromodeling of the latent HIV reservoir in memory CD4+T cell subsets identify LRAs that reverse latency with ranges of efficiency and specificity. We found that therapeutic induction of latency reversal is associated with upregulation of identical sets of TEM-associated genes and cell surface markers shown to be associated with latency reversal in ourex vivoandin vitromodels. Together, these data support the idea that the effector memory phenotype supports HIV latency reversal in CD4+T cells.
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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) | 0.000 | 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) | 0.001 | 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".