Impact of IL-10 on Diaphragmatic Cytokine Expression and Contractility during <i>Pseudomonas</i> Infection
Bibliographic record
Abstract
Severe weakness of the respiratory muscles, with attendant respiratory failure and death, has been documented in sepsis. In this study, we show that during murine pulmonary infection with Pseudomonas aeruginosa, multiple proinflammatory genes are up-regulated not only within the lungs, but also within the diaphragm. Significant induction of TNF-alpha, IL-1alpha, IL-1beta, IL-6, and IL-18 gene expression occurred within the diaphragm in a bacterial dose-dependent manner. We determined whether the anti-inflammatory cytokine IL-10 could blunt proinflammatory gene expression within the diaphragm under these conditions. The IL-10 receptor was found to be expressed by the diaphragm in vivo as well as in primary diaphragmatic muscle cell cultures. Transduction of myoblasts with an adenoviral vector (Ad-IL-10) induced strong IL-10 expression, and intramuscular injection of the same vector in vivo produced significant increases in IL-10 serum levels. Ad-IL-10 treatment of mice infected with P. aeruginosa significantly inhibited the induction of proinflammatory cytokines within the diaphragm, but not in the infected lungs. Ad-IL-10 treatment also led to greatly improved diaphragmatic force production in infected mice. These results suggest that pulmonary infection triggers proinflammatory gene expression by the diaphragm along with diaphragmatic weakness. Shifting the balance between pro- and anti-inflammatory mediators in favor of the latter by IL-10 gene delivery was able to restore normal diaphragmatic force-generating capacity under these conditions, suggesting a possible avenue for therapeutic intervention.
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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) | 0.001 | 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".