Sympathetic neural recruitment strategies following acute intermittent hypoxia in humans
Bibliographic record
Abstract
We examined the effect of acute intermittent hypoxia (IH) on sympathetic neural firing patterns and the role of the carotid chemoreceptors. We hypothesized exposure to acute IH would increase muscle sympathetic nerve activity (MSNA) via an increase in action potential (AP) discharge rates and within-burst firing. We further hypothesized any change in discharge patterns would be attenuated during acute chemoreceptor deactivation (hyperoxia). MSNA (microneurography) was assessed in 17 healthy adults (11 male/6 female; 31 ± 1 yr) during normoxic rest before and after 30 min of experimental IH. Prior to and following IH, participants were exposed to 2 min of 100% oxygen (hyperoxia). AP patterns were studied from the filtered raw MSNA signal using wavelet-based methodology. Compared with baseline, multiunit MSNA burst incidence ( P < 0.01), AP incidence ( P = 0.01), and AP content per burst ( P = 0.01) were increased following IH. There was an increase in the probability of a particular AP cluster firing once ( P < 0.01) and more than once ( P = 0.03) per burst following IH. There was no effect of hyperoxia on multiunit MSNA at baseline or following IH ( P > 0.05); however, hyperoxia following IH attenuated the probability of particular AP clusters firing more than once per burst ( P < 0.01). Acute IH increases MSNA by increasing AP discharge rates and within-burst firing. A portion of the increase in within-burst firing following IH can be attributed to the carotid chemoreceptors. These data advance the mechanistic understanding of sympathetic activation following acute IH in humans.
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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 | 0.000 | 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".