γ/δ T cell subsets in human aging using the classical α/β T cell model
Bibliographic record
Abstract
Aging is associated with an increased susceptibility to infections and diseases. It has also been associated with reduced functionality and altered distribution of immune cells, especially T cells. Whereas classical α/β T cells, especially CD8(+) T cells, were shown to be highly susceptible to aging, the effects of viral persistent stimulations on the fate of γ/δ T cells are much less documented. Healthy, elderly individuals of Chinese ethnical background were recruited under the aegis of SLAS-II. In this observational study, γ/δ T cell populations were characterized by flow cytometry and compared with the α/β CD4(+) and CD8(+) T cells in elderly and young controls. In our study, we identified a reduced frequency of γ/δ T cells but not α/β T cells with aging. The classical markers of α/β T cell aging, including CD28, CD27, and CD57, did not prove significant for γ/δ T cells. The extreme range of expression of these markers in γ/δ T cells was responsible for the lack of relationship between γ/δ T cell subsets, CD4/CD8 ratio, and anti-CMV titers that was significant for α/β T cells and, especially, CD8(+) T cells. Although markers of aging for γ/δ T cells are not clearly identified, our data collectively suggest that the presence of CD27 γ/δ T cells is associated with markers of α/β T cell aging.
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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 | 0.001 | 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".