Sympathetic Axonal Recruitment During Severe Chemoreflex Stress in Humans
Bibliographic record
Abstract
We tested the hypothesis that severe chemoreflex stress stimulates the recruitment of sub‐populations of larger, faster conducting sympathetic neurones that are otherwise silent at baseline. Muscle sympathetic nerve activity (MSNA; microneurography) was measured in 11 healthy individuals (6 females; 25 ± 3 yrs) at baseline and during a maximal end‐inspiratory apnea, which was preceded by a rebreathing period designed to maximize the chemoreflex stress during apnea (End‐apnea: PO 2 = 58 ± 4 Torr; PCO 2 = 55 ± 5 Torr). Compared to baseline, action potential (AP) frequency (174 ± 151 to 1083 ± 487 spikes/min; P < 0.001) and mean AP content per burst (11 ± 4 to 24 ± 12 spikes/burst; P < 0.01) were elevated during apnea. When detected APs were binned according to their peak‐to‐peak amplitude, the number of total ‘clusters' of APs detected (13 ± 4 to 20 ± 8 total clusters; P < 0.001), as well as the number of distinct ‘clusters' per burst (5 ± 2 to 8 ± 2 clusters/burst; P < 0.001), were increased during apnea. At baseline ( R 2 = 0.91; P < 0.001) and during apnea ( R 2 = 0.98; P < 0.001), a pattern emerged whereby AP cluster latency decreased as cluster amplitude increased. Furthermore, the cluster latency‐size profile was shifted downwards for every corresponding cluster during apnea versus baseline (range −14.9 to −99.4 ms; mean = −53.1 ms), such that APs with similar amplitudes were detected approximately 53.1 ms faster during apnea. In conclusion, there appears a unique ability of the sympathetic nervous system to recruit sub‐populations of previously dormant larger, faster conducting sympathetic neurones, as well as acutely modify reflex latency of all APs during severe chemoreflex stress. Supported by NSERC and 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".