Regulation of pro-inflammatory responses by S100A9 (P6124)
Bibliographic record
Abstract
Abstract Several Damage-Associated Molecular Patterns have been known to play an important role in inflammation. However, whether DAMPs are involved in lung inflammation during infection is not clear. In order to examine the important mechanisms of DAMPs during virus infection, influenza A virus infection model was used to study the role of S100A9, one of DAMPs, during lung pathogenesis. Here, we show that S100A9 is induced and secreted from flu-infected macrophages via a "non-damage" pathway. Since DAMPs are known to activate pro-inflammatory responses, we next examined whether purified recombinant S100A9 protein triggers pro-inflammatory responses in macrophages. Indeed, treatment of macrophages with S100A9 resulted in production of pro-inflammatory cytokines. The role of S100A9 during flu infection was also assessed by studying production of S100A9 in the respiratory tract of flu-infected mice. Upon flu infection, the production of S100A9 was induced in the lung and bronchoalveolar fluid. Moreover, the pro-inflammatory activity of extracellular S100A9 in the airway was obvious from robust production of pro-inflammatory cytokines in the lung. Thus our studies have identified S100A9 as a potent activator of pro-inflammatory response in macrophages and in the respiratory tract of mice. In addition, we uncovered ability of flu to induce S100A9 expression and production in macrophages and in the lung.
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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) | 0.000 | 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) | 0.002 | 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".