Nitric oxide regulates cytokine induction in the diaphragm in response to inspiratory resistive breathing
Bibliographic record
Abstract
Resistive breathing (encountered in chronic obstructive pulmonary disease and asthma) results in cytokine upregulation and decreased nitric oxide (NO) levels in the strenuously contracting diaphragm. NO can regulate gene expression. We hypothesized that endogenously produced NO downregulates cytokine production triggered by strenuous diaphragmatic contraction. Wistar rats treated with vehicle, the nonselective NO synthase inhibitor NG-nitro-l-arginine-methylester (l-NAME), or the NO donor diethylenetriamine-NONOate (DETA) were subjected to inspiratory resistive breathing (IRB; 50% of maximal inspiratory pressure) for 6 h or sham operation. Additional groups of rats were subjected to IRB for 6 h with concurrent administration of l-NAME and inhibitors of NF-κB (BAY-11-7082), ERK1/2 (PD98059), or P38 (SB203580). Inhibition of NO production (with l-NAME) resulted in upregulation of IRB-induced diaphragmatic IL-6, IL-10, IL-2, TNF-α, and IL-1β levels by 50%, 53%, 60%, 47%, and 45%, respectively. In contrast, the NO donor (DETA) attenuated the IRB-induced cytokine upregulation to levels characteristic of quietly breathing animals. l-NAME augmented IRB-induced activation of MAPKs (P38 and ERK1/2) and NF-κB, whereas DETA triggered the opposite effect. NF-κB and ERK1/2 inhibition in l-NAME-treated animals blunted the l-NAME-induced cytokine upregulation except IL-6, whereas P38 inhibition blunted all (including IL-6) cytokine upregulation. NO downregulates IRB-induced cytokine production in the strenuously contracting diaphragm through its action on MAPKs and NF-κB.
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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".