Granzyme B: a biomarker of influenza infection in older adults
Bibliographic record
Abstract
Abstract The importance of a T-cell mediated response against influenza infection in older adults has growing support. It has been previously shown that effector cells in older adults challenged with influenza virus produce granzyme B (GrB) in the absence of perforin. This lack of perforin could impair CTL directed killing and result in high extracellular GrB levels. GrB has been associated with various inflammatory and age related conditions and could also play an important role in influenza infection severity. We postulate that GrzB is a key mediator of inflammatory processes involved in severe influenza illness and could be used as a marker of influenza infection in older adults. Subjects over the age of 65 admitted to hospital with influenza like symptoms were recruited and grouped into cases of laboratory confirmed influenza illness (LCII) and influenza like illness (ILI). PBMCs were isolated from blood samples collected from participants on hospital admission and one month post-discharge. PBMCs were challenge with live influenza virus. Real-time PCR was used to quantify IFNγ, GrB and IL10 in PBMCs and isolated T-cells. We found that in vivo GrB levels in T-cells at the time of influenza infection in older adults were significantly higher in the LCII than the ILI group. Furthermore, ex vivo influenza virus challenge resulted in higher IFNγ, GrB and IL10 responses in subjects with LCII than ILI. Our study supports the association of elevated GrB and influenza infection status as well as the importance of T-cell mediated responses to infection. Future work will characterize GrB levels in older adults with varying severities of influenza illness with the aim to develop a point of care test to identify patients likely to develop serious outcomes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".