Mechanism of pulmonary immunosuppression: extrapulmonary burn injury suppresses bacterial endotoxin‐induced pulmonary neutrophil recruitment and neutrophil extracellular trap (NET) formation
Bibliographic record
Abstract
Pulmonary immunosuppression often occurs after burn injury (BI). However, the reasons for BI-induced pulmonary immunosuppression are not clearly understood. Neutrophil recruitment and neutrophil extracellular trap (NET) formation (NETosis) are important components of a robust pulmonary immune response, and we hypothesized that pulmonary inflammation and NETosis are defective after BI. To test this hypothesis, we established a mouse model with intranasal LPS instillation in the presence or absence of BI (15% of body surface burn) and determined the degree of immune cell infiltration, NETosis, and the cytokine levels in the airways and blood on d 2. Presence of LPS recruited monocytes and large numbers of neutrophils to the airways and induced NETosis (citrullinated histone H3, DNA, myeloperoxidase). By contrast, BI significantly reduced LPS-mediated leukocyte recruitment and NETosis. This BI-induced immunosuppression is attributable to the reduction of chemokine (C-C motif) ligand (CCL) 2 (monocyte chemoattractant protein 1) and CCL3 (macrophage inflammatory protein 1α). BI also suppressed LPS-induced increase in IL-17A, IL-17C, and IL-17E/IL-25 levels in the airways. Therefore, BI-mediated reduction in leukocyte recruitment and NETosis in the lungs are attributable to these cytokines. Regulating the levels of some of these key cytokines represents a potential therapeutic option for mitigating BI-mediated pulmonary immunosuppression.-Sakuma, M., Khan, M. A. S., Yasuhara, S., Martyn, J. A., Palaniyar, N. Mechanism of pulmonary immunosuppression: extrapulmonary burn injury suppresses bacterial endotoxin-induced pulmonary neutrophil recruitment and neutrophil extracellular trap (NET) formation.
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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".