IMPAIRED INDUCTION OF IL-10 EXPRESSION IN THE LUNG FOLLOWING HEMORRHAGIC SHOCK
Bibliographic record
Abstract
The balance between pro- and anti-inflammatory cytokines is considered to be an important determinant of the magnitude of inflammation in a number of disease states. We previously showed that resuscitated hemorrhagic shock augmented LPS-induced release of proinflammatory molecules by alveolar macrophages (AM). In the present studies, we evaluated the expression and regulation of the counter inflammatory cytokine IL-10 in the lung using this model. We hypothesized that impaired up-regulation of IL-10 in shock/resuscitated animals might serve as a mechanism contributing to accentuated lung inflammation. In a rodent model, animals exposed to LPS alone exhibited enhanced IL-10 mRNA levels in lung tissue as well as in AM, but antecedent shock/resuscitation delayed and attenuated the LPS-induced IL-10 mRNA levels. The ability of shock to attenuate LPS-stimulated IL-10 was also seen in the protein levels. This effect correlated with an augmented expression of cytokine-induced neutrophil chemoattractant (CINC) mRNA. Shock/resuscitated animals given exogenous IL-10 had reduced proinflammatory response, as shown by decreased expression of CINC mRNA and decreased neutrophil sequestration in the lung. Shock/resuscitation plus LPS markedly reduced the transcription rate of IL-10 mRNA compared to LPS alone but did not affect IL-10 mRNA stability. Reduced IL-10 transcription was not caused solely by impaired nuclear translocation of STAT3 and Sp1/Sp3 transcription factors because LPS-induced nuclear translocation of these factors was augmented by antecedent shock. Considered together, these findings show that shock/resuscitation suppresses LPS-induced IL-10 expression by AM in the lung by inhibiting IL-10 gene transcription. Failed up-regulation of counter inflammatory cytokines may contribute to augmented organ dysfunction in 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 | 0.001 |
| 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".