Hemorrhagic Shock/Resuscitation Reduces the M2 Phenotype of Alveolar Macrophages: A Potential Mechanism Contributing to Increased LPS-Induced Lung Injury
Bibliographic record
Abstract
Major hemorrhage is a significant contributor to the morbidity and mortality resulting from traumatic injury. In addition to its role in in early mortality, hemorrhagic shock followed by resuscitation (HS/R) is known to initiate immunological events that contribute to the development of organ dysfunction. The pathogenesis of acute lung injury following HS/R involves macrophage activation. Recent studies have shown that macrophage function may in part be regulated by polarization toward classical M1 pro-inflammatory cells or alternatively activated anti-inflammatory M2 cells. We hypothesized that alteration in the M1/M2 phenotypic balance of alveolar macrophages in the lung may contribute to a pro-inflammatory state following HS/R. Using a murine model, we show that HS/R causes a rapid reduction in surface cluster of differentiation (CD)206 and CD36, markers of M2 cells, as well as in CD206 messenger ribonucleic acid (mRNA). M1 markers including surface CD80 and tumour necrosis factor alpha and inducible nitric oxide synthase mRNA were increased, albeit in a somewhat delayed time course. The prostaglandin 5-deoxyDelta12,14 prostaglandin J2 (15d-PGJ2), known to polarize cells toward M2, restored levels of M2 macrophages toward control and prevented lung injury, as assessed by bronchoalveolar protein content. Adoptive cell transfer of in vitro M2 polarized macrophages also reduced lung inflammation/injury following hemorrhagic shock. Together, these studies demonstrate that HS/R increases M1/M2 ratio, predominantly by lowering M2 cells, and thus enhances the proinflammatory state. Various strategies aimed at promoting M2 polarization may lessen the magnitude of inflammation and injury. This represents a novel approach to the prevention/treatment of lung injury in critically ill trauma patients.
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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 | 0.000 |
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".