Adapting Indirect Calorimetry to measure metabolic status of healthy and septic neonatal mice
Bibliographic record
Abstract
Abstract Commercially available platforms to measure murine pulmonary gas exchange have long been used to measure metabolic status of adult animals, thus providing insights into metabolic disease, diabetes, and infection. Metabolic status is increasingly being recognized as an important modulator of neonatal immunity, and capturing pulmonary gas exchange in neonatal animals provides a non-invasive way to capture physiological information in health and disease and may reveal metabolic determinants of immune-mediated diseases unique to this life stage. We evaluated an indirect calorimetry (IC) system, the Promethion Core CGF system outfitted with Respirometry Chambers (RC3) as a tool to accurately capture pulmonary gas exchange from individual healthy and septic murine neonatal pups in the first week of life. We assessed the technical variance of the platform, impact of the procedure of animal welfare, compared measurements performed either at room temperature or at 30°C, and determined the platform’s sensitivity to measure gas exchange from pups with very small lung capacity or low respiratory rate. While gas exchange was not captured above background levels in all pups with either very small lung volume (pups less than 4 days old) or with very low respiratory rates (septic pups with the most depressed respiratory status), measurements did capture physiologically relevant changes in gas exchange across age and disease states. The impost associated with frequent handling of septic animals for IC did not negatively impact clinical outcomes among pups challenged with a polymicrobial slurry. Further, while performing the IC readings at 30°C successfully stabilized animal body temperature, the VO2 and VCO2 values differed across temperature states for older pups. In conclusion, the Promethion Core system outfitted with RC3 chambers is a viable platform to integrate IC into murine neonatal health research.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".