Biological Sex Influences Human Bystander CD8<sup>+</sup> T Cell Activation
Bibliographic record
Abstract
ABSTRACT The recent COVID‐19 pandemic has highlighted a significant sex bias in disease outcome, where male sex is associated with greater disease severity and mortality. Interestingly, studies have also identified a role for antigen‐independent “bystander‐activated” CD8+ T cells in the severity of COVID‐19 and other viral infections. However, whether biological sex contributes to the magnitude of bystander T cell activation has not been investigated. To assess sex differences in bystander CD8+ T cell activation, we isolated PBMCs from age‐matched male and female donors and stimulated the cells with cytokines IL‐12/15/18 to induce bystander T cell activation. Male CD8+ T cells stimulated with IL‐15 exhibited greater bystander activation, including increased NKG2D expression and greater antigen‐independent cytotoxicity against tumor cells compared with female CD8+ T cells. In contrast, IL‐12/18 and IL‐12/15/18 stimulation of CD8+ T cells did not reveal evidence of sex differences in bystander IFN‐γ production. Our data suggest that underlying sex differences in bystander CD8+ T cell activation and cytotoxicity may contribute to the observed sex biases in disease severity of viral infections.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.007 | 0.001 |
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".