Functional Blockage of S100A8/A9 Ameliorates Ischemia–Reperfusion Injury in the Lung
Bibliographic record
Abstract
(1) Background: Lung ischemia-reperfusion (IR) injury increases the mortality and morbidity of patients undergoing lung transplantation. The objective of this study was to identify the key initiator of lung IR injury and to evaluate pharmacological therapeutic approaches using a functional inhibitor against the identified molecule. (2) Methods: Using a mouse hilar clamp model, the combination of RNA sequencing and histological investigations revealed that neutrophil-derived S100A8/A9 plays a central role in inflammatory reactions during lung IR injury. Mice were assigned to sham and IR groups with or without the injection of anti-S100A8/A9 neutralizing monoclonal antibody (mAb). (3) Results: Anti-S100A8/A9 mAb treatment significantly attenuated plasma S100A8/A9 levels compared with control IgG. As evaluated by oxygenation capacity and neutrophil infiltration, the antibody treatment dramatically ameliorated the IR injury. The gene expression levels of cytokines and chemokines induced by IR injury were significantly reduced by the neutralizing antibody. Furthermore, the antibody treatment significantly reduced TUNEL-positive cells, indicating the presence of apoptotic cells. (4) Conclusions: We identified S100A8/A9 as a novel therapeutic target against lung IR injury.
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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 | 0.001 |
| 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".