The contribution of memory CD4+ T cell subset phenotype to latency reversal efficiency
Bibliographic record
Abstract
Background: Accurate measurement of the latent reservoir is essential for the evaluation of cure strategies.Widely used PCR strategies mainly detect defective proviruses and vastly overestimate reservoir size while the viral outgrowth assay and other assays that measure viral RNA, protein or virion production following a single round of T cell activation miss proviruses that are only induced after multiple rounds.Therefore we developed a novel approach to reservoir measurement that directly quantitates intact proviruses that are capable of causing viral rebound.Methods: A large database of full genome sequences was used to design a digital droplet PCR assay that directly and separately quantitates intact and defective proviruses.Results: This approach gives infected cells frequencies that correlate well with results of full genome sequencing.Application to interesting patient populations has provided new insights into reservoir dynamics.Conclusions: Direct measurement of all of the intact proviruses capable of causing viral rebound with a novel rapid and scalable assay may provide the best way to evaluate cure strategies targeting the latent reservoir.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".