Vaccination does not protect aged mice from influenza-induced lung inflammation (VAC9P.1062)
Bibliographic record
Abstract
Abstract Aging predisposes individuals to increased susceptibility to influenza infection and delays in viral clearance. Importantly, we show that age-related delays in viral clearance are correlated with lingering inflammation in the lungs of aged mice. This is critical since inflammation in the lungs is associated with an enhanced susceptibility to secondary bacterial infection, which is a significant sequela to flu infection and often causes death. Inflammation in young lungs following flu infection can be significantly reduced by prior flu immunity, such as that generated following vaccination with recombinant influenza nucleoprotein (rNP). This protection is characterized by reduced lung inflammation, reduced lung damage, reduced susceptibility to flu challenge and also correlates with increased levels of IL-10 and anti-NP antibodies. In contrast, vaccination of aged mice with rNP does not reduce lung inflammation and shows no protection from weight loss and no reduction in viral titers in the lungs following subsequent flu infection. Additionally, aged mice generate lower levels of NP-specific antibodies, which are absolutely critical for reducing lung inflammation. Consequently, unlike young mice, these NP vaccinated aged mice are not protected from death due to secondary bacterial infection. Thus, when considering a universal flu vaccine such as rNP, it is important to understand how efficacious it can be in highly susceptible populations such as the elderly.
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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".