Sympathetic Action Potential Firing and Recruitment Patterns Are Abnormal in Gestational Hypertension
Bibliographic record
Abstract
Background: We tested the hypothesis that women who develop gestational hypertension (GH) display abnormal sympathetic action potential (AP) discharge patterns during late pregnancy (32–36 weeks), both at supine rest and during postural stress. Methods: Thirteen nonpregnant, female controls (nonpregnant controls [CTRL]) and 32 pregnant women participated; 14 had low-risk (no personal history of GH) normal pregnancies (LR-NP), 10 had high-risk (personal history of GH) normal pregnancies (HR-NP), and 8 developed GH. We measured heart rate, blood pressure, and muscle sympathetic nerve activity (microneurography) at supine rest and 60° head-up tilt. Sympathetic AP patterns were studied using wavelet-based methodology. Results: At rest, muscle sympathetic nerve activity burst frequency was elevated in LR-NP, HR-NP, and GH versus CTRL (all P ≤0.01); however, the AP content per integrated burst was augmented only in GH (20±5 spikes/burst), compared with CTRL (8±3 spikes/burst), LR-NP (9±2 spikes/burst) and HR-NP (11±4 spikes/burst; all P <0.0001). Thus, total AP firing frequency was greater in GH versus each of CTRL, LR-NP, and HR-NP (all P <0.0001). In pregnancy, AP frequency is related directly to systolic (R 2 =46%) and diastolic (R 2 =20%) blood pressure (both P ≤0.01). Unlike CTRL (both P <0.01), women who developed GH were unable to increase within-burst AP firing ( P =0.71) or recruit latent subpopulations of larger-sized APs ( P =0.72) in response to head-up tilt, perhaps related to a ceiling-effect; however, total AP firing frequency in the upright posture was elevated in the GH cohort versus CTRL, LR-NP, and HR-NP (all P <0.05). Conclusions: Women who develop GH display aberrant sympathetic AP firing patterns in both the supine and upright postures.
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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 | 0.002 |
| 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".