Noninvasive Measurement of Exhaled Nitric Oxide in a Spontaneously Breathing Mouse
Bibliographic record
Abstract
Nitric oxide (NO) has been detected in the exhaled gas of animals and humans. In previous work, investigators have used anesthetized, mechanically ventilated animals to obtain exhaled NO (E(NO)) measurements, which has unclear effects on the levels of E(NO) and does not allow for repeated analysis of E(NO). We sought to measure E(NO) from a single, spontaneously breathing mouse. The mouse was placed in a small Plexiglas chamber and allowed to acclimatize before exhaled gas was collected for E(NO) analysis. Under optimal operating conditions of flow and pressure, the mean concentration of exhaled NO (FE(NO)) of 25 mice was 10.1 +/- 1.0 ppb. The maximal variation of FE(NO) when repeatedly measured daily in individual animals was 2.1 ppb. Administration of L-NAME, a nonselective NOS inhibitor, reduced FE(NO) by 51 +/- 6% (p < 0.01). Intraperitoneally administered lipopolysaccharide induced acute lung injury and increased FE(NO) by 30 +/- 7% (p < 0.05). We have demonstrated that it is possible to noninvasively measure E(NO) from a single, spontaneously breathing mouse. This novel technique provides a stable, reproducible, and responsive measure of E(NO) in mice. This technique will be of use in determining cellular and isoform sources of E(NO), as well as the role of endogenous NO in lung disease.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".