Vagal contributions to fetal heart rate variability: an omics approach
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVE: Fetal heart rate variability (fHRV) is an important indicator of health and disease, yet its physiological origins, neural contributions, in particular, are not well understood. We aimed to develop novel experimental and data analytical approaches to identify fHRV measures reflecting the vagus nerve contributions to fHRV. APPROACH: In near-term ovine fetuses, a comprehensive set of 46 fHRV measures was computed from fetal pre-cordial electrocardiogram recorded during surgery and 72 h later without (n = 24) and with intra-surgical bilateral cervical vagotomy (n = 15). MAIN RESULTS: , Kullback-Leibler permutation entropy (KLPE) and scale-dependent Lyapunov exponent slope (SDLE α). SIGNIFICANCE: We provide a systematic delineation of vagal contributions to fHRV across signal-analytical domains which should be relevant for the emerging field of bioelectronic medicine and the deciphering of the 'vagus code'. Our findings also have clinical significance for in utero monitoring of fetal health during surgery. Key points •Fetal surgery causes a complex pattern of changes in heart rate variability measures with an overall reduction of complexity or variability. •At 72 h after surgery, many of the HRV measures recover and this recovery is delayed by an intrasurgical cervical bilateral vagotomy. •We identify HRV pattern representing complete vagal withdrawal that can be understood as part of 'HRV code', rather than any single HRV measure. •We identify HRV biomarkers of recovery from fetal surgery and discuss the effect of anticholinergic medication on this recovery.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
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 it