Regulation of breathing pattern by IL-10
Bibliographic record
Abstract
Proinflammatory cytokines like interleukin-1β (IL-1β) affect the control of breathing. Our aim is to determine the effect of the anti-inflammatory cytokine IL-10 οn the control of breathing. IL-10 knockout mice (IL-10−/−, n = 10) and wild-type mice (IL-10+/+, n = 10) were exposed to the following test gases: hyperoxic hypercapnia 7% CO2-93% O2, normoxic hypercapnia 7% CO2-21% O2, hypoxic hypercapnia 7% CO2-10% O2, and hypoxic normocapnia 3% CO2-10% O2. The ventilatory function was assessed using whole body plethysmography. Recombinant mouse IL-10 (rIL-10; 10 μg/kg) was administered intraperitoneally to wild-type mice ( n = 10) 30 min before the onset of gas challenge. IL-10 was administered in neonatal medullary slices (10–30 ng/ml, n = 8). We found that IL-10−/−mice exhibited consistently increased frequency and reduced tidal volume compared with IL-10+/+mice during room air breathing and in all test gases (by 23.62 to 33.2%, P < 0.05 and −36.23 to −41.69%, P < 0.05, respectively). In all inspired gases, the minute ventilation of IL-10−/−mice was lower than IL-10+/+(by −15.67 to −22.74%, P < 0.05). The rapid shallow breathing index was higher in IL-10−/−mice compared with IL-10+/+mice in all inspired gases (by 50.25 to 57.5%, P < 0.05). The intraperitoneal injection of rIL-10 caused reduction of the respiratory rate and augmentation of the tidal volume in room air and also in all inspired gases (by −12.22 to −29.53 and 32.18 to 45.11%, P < 0.05, respectively). IL-10 administration in neonatal rat ( n = 8) in vitro rhythmically active medullary slice preparations did not affect either rhythmicity or peak amplitude of hypoglossal nerve discharge. In conclusion, IL-10 may induce a slower and deeper pattern of breathing.
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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".