The administration of oseltamivir results in reduced effector and memory CD8 <sup>+</sup> T cell responses to influenza and affects protective immunity
Bibliographic record
Abstract
The clinical benefits of oseltamivir (Tamiflu) are well established, but the effects of antiviral treatment on the immune response are poorly understood. By use of flow cytometric analyses and the mouse model, we thoroughly investigated the impact of such a treatment on the immune response and the generation of protective immunity to influenza. We demonstrated that influenza-specific CD8(+) effector T cell recruitment was reduced up to 81% in the lungs of mice treated with oseltamivir (5 or 50 mg/kg twice daily; EC50 49 nM in vitro) compared to saline controls, but cell generation was unaffected in draining lymph nodes. Importantly, we showed that oseltamivir administration significantly decreased the pools of tissue-resident and circulating effector memory (93.7%) and central memory CD8(+) T cells (45%) compared to saline controls. During heterologous secondary infection, a decreased memory CD8(+) T cell pool combined with reduced generation of secondary influenza-specific effectors in the lymph nodes resulted in 10-fold decreased CD8(+) T cell recall responses, which increased mouse morbidity and delayed viral clearance. Furthermore, antiviral administration led to a significant 5.7-fold decreased production of functional anti-influenza antibodies. Thus, our study demonstrates that antiviral treatment affects the development of the adaptive immune response and protective immunity against influenza.
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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 | 0.000 | 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 | 0.001 |
| 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".