Restoration of the CD4 T Cell Compartment after Long-Term Highly Active Antiretroviral Therapy without Phenotypical Signs of Accelerated Immunological Aging
Bibliographic record
Abstract
It remains uncertain whether full T cell reconstitution can be established in HIV-infected children and adults with long-term sustained virological control by highly active antiretroviral therapy (HAART). In this study, we comprehensively analyzed various phenotypical markers of CD4 T cell recovery. In addition to measuring T cell activation and proliferation markers, CD4 T cell generation and aging of the CD4 T cell compartment were assessed by measuring TCR excision circles and the fraction of CD31-expressing naive CD4 T cells. In all children and in adults with relatively high CD4 T cell counts at start of therapy (>200 cells/microl), total CD4 T cell numbers normalized within 1 year of therapy. After long-term HAART (4.4-9.6 years), naive CD4 T cell counts had normalized in both groups. Although in adults with low baseline CD4 T cell counts (<200 cells/microl) total CD4 T cell numbers normalized eventually after at least 7 years of HAART, naive CD4 T cell counts had still not recovered. TCR excision circle data showed that thymic T cell production contributed to naive T cell recovery at all ages. The fraction of CD31-expressing naive CD4 T cells was found to be normal, suggesting that the CD4 T cell repertoire was diverse after long-term HAART. Hence, under sustained viral suppression during long-term HAART, the T cell compartment has the potential to fully recover by generating new naive T cells both in children and in adults with high baseline CD4 T cells counts. Irrespective of baseline CD4 T cell counts, reconstitution occurred without a significant effect on T cell aging as reflected by markers for replicative history.
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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 | 0.000 |
| 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".