Progesterone enhances respiratory frequency and reduces apnea frequency during sleep in mice KO for the nuclear progesterone receptor
Bibliographic record
Abstract
We used adult female mice knocked‐out for the nuclear progesterone receptor gene (PRKO) and wild‐type (WT) controls to record breathing pattern and apnea frequency across sleep/wake states (determined by EEG/EMG analysis). Respiratory frequency (fR) was similar in WT and PRKO mice during quiet wakefulness. During nREM and REM sleep, fR declined respectively by 31 ± 3 % and 27 ± 5 % in PRKO mice and by 17 ± 7 % and 11 ± 15 % in WT mice (p<0.0001 sleep state effect: p=0.03 group effect). Apnea frequency during sleep (nREM+REM) was 13.8 ± 6 h −1 in WT and 30.4 ± 18 h −1 in PRKO mice. A group of PRKO female mice was treated with progesterone for 7 days (eq. 4mg/kg/day ‐ subcutaneous pump), and compared to the PRKO and WT females treated with vehicle. After progesterone treatment in PRKO mice, fR values were 12 ± 5 % and 7 ± 5 % lower during nREM and REM sleep compared to wakefulness (no significant sleep state effect). Apnea frequency during sleep in PRKO mice treated with progesterone was 2.8 ± 2 h −1 , markedly lower than in PRKO mice treated with vehicle. We conclude that deletion of the nuclear progesterone receptor aggravates the respiratory depression during sleep and enhances apnea frequency. Since progesterone treatment in PRKO mice enhances fR and reduces apnea frequency during sleep, progesterone also acts on the respiratory control system through other signalling pathways. Founded by CIHR.
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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 | 0.001 | 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) | 0.002 | 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".