HIV‐specific CD8<sup>+</sup> T‐cell proliferation is prospectively associated with delayed disease progression
Bibliographic record
Abstract
Human immunodeficiency virus (HIV)-specific CD8(+) T-cell proliferation is consistently correlated with enhanced host HIV immune control, but whether proliferative responses are a cause or consequence of immune protection is unclear. We measured Env-specific CD8(+) T-cell proliferation and interferon (IFN)-γ secretion in HIV-infected participants with CD4 counts >200, who then completed 121 person-years of prospective follow-up to monitor HIV disease progression. In all, 13 of 31 participants (42%) reached end point during longitudinal follow-up. Strong Env-specific CD8(+) T-cell proliferation (>10% of CD8(+) T cells) was observed in 14/31 participants at baseline, and this was associated with a longer time to HIV disease progression end point, stratified baseline CD4 count (P=0.016). No associations were observed for IFN-γ ELISPOT responses and progression (P>0.2). Strong proliferation remained significant in multivariate Cox regression analyses (P=0.044) as an independent predictor of delayed HIV disease progression, along with baseline CD4 count (P=0.04). Duration of HIV infection was associated with more rapid progression in univariate, but not multivariate, analysis (P=0.112). Age and baseline viral load were not predictive of progression. HIV-specific CD8(+) T-cell proliferation was a correlate of protective immunity in this prospective study; such responses may be important for HIV vaccine protection.
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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 | 0.001 | 0.002 |
| 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".