Vagal contributions to fetal heart rate variability: an omics approach
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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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".