The Nuclear Progesterone Receptor Reduces Post-Sigh Apneas during Sleep and Increases the Ventilatory Response to Hypercapnia in Adult Female Mice
Bibliographic record
Abstract
We tested the hypothesis that the nuclear progesterone receptor (nPR) is involved in respiratory control and mediates the respiratory stimulant effect of progesterone. Adult female mice carrying a mutation in the nPR gene (PRKO mice) and wild-type controls (WT) were implanted with an osmotic pump delivering vehicle or progesterone (4 mg/kg/day). The mice were instrumented with EEG and neck EMG electrodes connected to a telemetry transmitter. The animals were placed in a whole body plethysmograph 7 days after surgery to record ventilation, metabolic rate, EEG and neck EMGs for 4 consecutive hours. The animals were exposed to hypercapnia (5% CO2), hypoxia (12% O2) and hypoxic-hypercapnia (5% CO2+12% O2-5 min each) to assess chemoreflex responses. EEG and EMG signals were used to characterize vigilance states (e.g., wake, non-REM, and REM sleep). PRKO mice exhibited similar levels of minute ventilation during non-REM and REM sleep, and higher frequencies of sighs and post-sigh apneas during non-REM sleep compared to WT. Progesterone treatment increased minute ventilation and metabolic rate in WT and PRKO mice during non-REM sleep. In WT mice, but not in PRKO mice, the ventilation under hypercapnia and hypoxic hypercapnia was enhanced after progesterone treatment. We conclude that the nPR reduces apnea frequency during non-REM sleep and enhances chemoreflex responses to hypercapnia after progesterone treatment. These results also suggest that mechanisms other than nPR activation increase metabolic rate in response to progesterone treatment in adult female mice.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".