Impact of Sex Steroid Ablation on Viral, Tumour and Vaccine Responses in Aged Mice
Bibliographic record
Abstract
Recent evidence suggests that the decline in resistance to viral infections with age occurs predominantly as a result of a gradual loss of naïve antigen-specific T cells. As such, restoration of the naïve T cell repertoire to levels seen in young healthy adults may improve defence against infection in the aged. We have previously shown that sex steroid ablation (SSA) rejuvenates the ageing thymus and increases thymic export of naïve T cells, but it remains unclear whether T cell responses are improved. Using mouse models of clinically relevant diseases, we now demonstrate that SSA increases the number of naïve T cells able to respond to antigen, thereby enhancing effector responses in aged mice. Specifically, aged mice exhibit a delay in clearing influenza A virus, which correlates with diminished specific cytotoxic activity. This is due to a decreased magnitude of response and not an intrinsic defect in effector T cell function. Upon SSA, aged mice exhibit increased T cell responsiveness that restores efficient viral clearance. We further demonstrate that SSA decreases the incidence of an inducible tumour in aged mice and can potentially increase their responsiveness to a low-dose human papillomavirus vaccine in clearing pre-formed tumours. As thymectomy abrogates the increase in T cell numbers and responsiveness following SSA, we propose that the T cell effects of SSA are dependent on thymic reactivation and subsequent replenishment of the peripheral T cell pool with newly emigrated naïve T cells. These findings have important implications for strategies to improve protection from infection and responsiveness to vaccination in the aged.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".