Effects of aging and coronary artery disease on sympathetic neural recruitment strategies during end-inspiratory and end-expiratory apnea
Bibliographic record
Abstract
In response to acute physiological stress, the sympathetic nervous system modifies neural outflow through increased firing frequency of lower-threshold axons, recruitment of latent subpopulations of higher-threshold axons, and/or acute modifications of synaptic delays. Aging and coronary artery disease (CAD) often modify efferent muscle sympathetic nerve activity (MSNA). Therefore, we investigated whether CAD (n = 14; 61 ± 10 yr) and/or healthy aging without CAD (OH; n = 14; 59 ± 9 yr) modified these recruitment strategies that normally are observed in young healthy (YH; n = 14; 25 ± 3 yr) individuals. MSNA (microneurography) was measured at baseline and during maximal voluntary end-inspiratory (EI) and end-expiratory (EE) apneas. Action potential (AP) patterns were studied using a novel AP analysis technique. AP frequency increased in all groups during both EI- and EE-apnea (all P < 0.05). The mean AP content per integrated burst increased during EI- and EE-apnea in YH (EI: Δ6 ± 4 APs/burst; EE: Δ10 ± 6 APs/burst; both P < 0.01) and OH (EI: Δ3 ± 3 APs/burst; EE: Δ4 ± 5 APs/burst; both P < 0.01), but not in CAD (EI: Δ1 ± 3 APs/burst; EE: Δ2 ± 3 APs/burst; both P = NS). When APs were binned into "clusters" according to peak-to-peak amplitude, total clusters increased during EI- and EE-apnea in YH (EI: Δ5 ± 2; EE: Δ6 ± 4; both P < 0.01), during EI-apnea only in OH (EI: Δ1 ± 2; P < 0.01; EE: Δ1 ± 2; P = NS), and neither apnea in CAD (EI: Δ -2 ± 2; EE: Δ -1 ± 2; both P = NS). In all groups, the AP cluster size-latency profile was shifted downwards for every corresponding cluster during EI- and EE-apnea (all P < 0.01). As such, inherent dysregulation exists within the central features of apnea-related sympathetic outflow in aging and CAD.
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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) | 0.000 | 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".