Facilitation of phrenic motor output following sustained hypocapnia in rats
Bibliographic record
Abstract
Phrenic motoneurons exhibit serotonin‐dependent synaptic plasticity, particularly following intermittent hypoxia. Another means of altering synaptic strength in the central nervous system is synaptic scaling, a process whereby efficacy is increased in inverse proportion to activity. We hypothesized that phrenic motoneuron inactivity increases synaptic efficacy, resulting in a long‐lasting increase in phrenic motor output. Phrenic nerve inactivity was induced by modest hypocapnia in anesthetized, paralyzed, vagotomized and ventilated rats (4 to 6 mmHg below CO 2 ‐apneic threshold, 25 min). After restoring normocapnic arterial CO 2 levels, an early and lasting facilitation in phrenic nerve burst amplitude was observed (n = 8; 15 min: 87%±21%; 60 min: 110%±29% baseline; p<0.05), a response significantly different from control rats (n=6; 15 min: −1%±3%; 60 min: 7%±7%). To confirm that inactivity versus respiratory alkalosis per se caused the facilitation, we restored baseline phrenic activity during hypocapnia with steady carotid sinus nerve (CSN) stimulation (2–5 Hz). This combination attenuated, but did not eliminate the phrenic facilitation (n = 5; 15 min: 39%±11%; 60 min: 45%±18%, both p<0.05). The results are consistent with the hypothesis that inactivity induces synaptic plasticity in the phrenic motor system, but do not rule out independent effects of hypocapnia and/or CSN stimulation. Inactivity‐induced phrenic facilitation may have important implications: it may play a role in progressive central sleep apnea, since central apneas are among the few instances of spontaneous respiratory motor inactivity; and it may contribute to patient recovery following prolonged ventilatory support. Supported by NIH 69064 and CIHR (Canada).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".