Slow disease progression and robust therapy-mediated CD4+ T-cell recovery are associated with efficient thymopoiesis during HIV-1 infection
Bibliographic record
Abstract
In chronic HIV infection, most untreated patients lose naive CD4+ and CD8+ T cells, whereas a minority preserve them despite persistent high viremia. Although antiretroviral therapy (ART)-mediated viral suppression generally results in a rise of naive and total CD4+ T cells, certain patients experience very little or no T-cell reconstitution. High peripheral T-cell activation has been linked to poor clinical outcomes, interfering with previous evaluations of thymic function in disease progression and therapy-mediated T-cell recovery. To circumvent this, we used the sj/betaTREC ratio, a robust index of thymopoiesis that is independent of peripheral T-cell proliferation, to evaluate the thymic contribution to the preservation and restoration of naive CD4+ T cells. We show that the loss of naive and total CD4+ T cells is the result of or is exacerbated by a sustained thymic defect, whereas efficient thymopoiesis supports naive and total CD4+ T-cell maintenance in slow progressor patients. In ART-treated patients, CD4+ T-cell recovery was associated with the normalization of thymopoiesis, whereas the thymic defect persisted in aviremic patients who failed to recover CD4+ T-cell counts. Overall, we demonstrate that efficient thymopoiesis is key in the natural maintenance and in therapy-mediated recovery of naive and total 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 | 0.001 |
| 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) | 0.001 | 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".