Neural signature of cerebral activity of the fetal cholinergic anti‐inflammatory pathway derived from heart rate variability
Bibliographic record
Abstract
Fetal cholinergic anti‐inflammatory pathway (CAP) activity parallels changes in fetal heart rate variability (fHRV) in response to acidemia or inflammation. We hypothesized that 1) a multi‐domain fHRV measures approach will detect CAP activation early and 2) the degree of CAP activation will reflect the level of cerebral inflammation independent of the type of stimulus ‐ spontaneous hypoxia (n=5, arterial O2sat<50%), LPS (n=6) or control (n=9). Chronically‐instrumented near term fetal sheep underwent increasingly intense umbilical cord occlusions until fetal pH<7.00 (~3–4 h). FHRV measures Skewness, Assymetry Index (AsymI), RMSSD, Sample Entropy (SampEn) and Fuzzy Entropy (FuzEn) were calculated continuously using the automated and standardized Continuous Individualized Multiorgan Variability Analysis, and correlated to microglia (MG) counts in specific brain regions. RMSSD and FuzEn increased and SampEn decreased early. Measures of fHRV symmetry did not change with worsening acidemia but correlated to MG counts, with CA1 MG counts correlating only to AsymI. SampEn correlated most often to MG counts in all regions except the DG, which correlated to FuzEn, Skewness and RMSSD. The spatiotemporal pattern of correlation to fHRV measures of different property domains provides a neural signature of cerebral CAP activity. Funding: Women's Development Council, FRSQ, CIHR, MITACS/NeuroDevNet
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".