Persistent human immunodeficiency virus-1 antigenaemia affects the expression of interleukin-7Rα on central and effector memory CD4+ and CD8+ T cell subsets
Bibliographic record
Abstract
Interleukin (IL)-7 and its receptor (IL-7Ralpha) play important roles in regulating lymphopoiesis. Previous studies have reported that human immunodeficiency virus-1 (HIV-1) viraemia affects the expression of IL-7Ralpha, but its effects on CD4+ and CD8+ T cell memory subsets have not been studied. Using eight-colour flow cytometry, we compared the immunophenotypic patterns of CD4+ and CD8+ T cell subsets expressing IL-7Ralpha and activation markers, as well as circulating IL-7 levels, in three well-defined groups of HIV-1-infected subjects: successfully treated, viraemic and long-term non-progressor (LTNP). Compared with successfully treated and LTNP subjects, viraemic patients had reduced expression of IL-7Ralpha on both CD4+ and CD8+ T cells, particularly on central and effector memory T cell compartments, and substantially elevated expression of activation markers on CD8+ T cell subsets. Circulating IL-7 levels were correlated negatively with the number of CD4+ and CD8+ T cell subsets expressing IL-7Ralpha; these associations were stronger with CD4+ T cell subsets and mainly with central and effector memory cells. The expression of activation markers on CD4+ and CD8+ cell T subsets was not related to circulating IL-7 levels. A strong negative correlation was observed between central memory CD4+ or CD8+ T cells expressing IL-7Ralpha and those expressing activation markers, independently of IL-7 levels. Collectively, these results provide further insight on the role of unsuppressed viral load in disrupting the IL-7/IL-7Ralpha system and contributing to HIV-1 disease progression.
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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 | 0.000 |
| 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".