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Record W2328943595 · doi:10.1097/pcc.0000000000000643

Can Monitoring Fetal Intestinal Inflammation Using Heart Rate Variability Analysis Signal Incipient Necrotizing Enterocolitis of the Neonate?

2016· article· en· W2328943595 on OpenAlexafffund
Hai Lun Liu, Luca Garzoni, Christophe L. Herry, Lucien Daniel Durosier, Mingju Cao, Patrick Burns, Gilles Fecteau, André Desrochers, Natalie Patey, Andrew Seely, Christophe Fauré, Martin G. Frasch

Bibliographic record

VenuePediatric Critical Care Medicine · 2016
Typearticle
Languageen
FieldMedicine
TopicHeart Rate Variability and Autonomic Control
Canadian institutionsUniversité de MontréalCentre Hospitalier Universitaire Sainte-Justine
FundersCanadian Institutes of Health Research
KeywordsMedicineNecrotizing enterocolitisChorioamnionitisFetusSepsisInternal medicineLipopolysaccharide binding proteinProinflammatory cytokineTumor necrosis factor alphaEnterocolitisLipopolysaccharideInflammationPathologyCardiologyAcute-phase proteinPregnancyBiology

Abstract

fetched live from OpenAlex

OBJECTIVE: Necrotizing enterocolitis of the neonate is an acute inflammatory intestinal disease that can cause necrosis and sepsis. Chorioamnionitis is a risk factor of necrotizing enterocolitis. The gut represents the biggest vagus-innervated organ. Vagal activity can be measured via fetal heart rate variability. We hypothesized that fetal heart rate variability can detect fetuses with incipient gut inflammation. DESIGN: Prospective animal study. SETTING: University research laboratory. SUBJECTS: Chronically instrumented near-term fetal sheep (n = 21). MEASUREMENTS AND MAIN RESULTS: Animals were surgically instrumented with vascular catheters and electrocardiogram to allow manipulation and recording from nonanesthetized animals. In 14 fetal sheep, inflammation was induced with lipopolysaccharide (IV) to mimic chorioamnionitis. Fetal arterial blood samples were drawn at selected time points over 54 hours post lipopolysaccharide for blood gas and cytokines (interleukin-6 and tumor necrosis factor-α enzymelinked immunosorbent assay). Fetal heart rateV was quantified throughout the experiment. The time-matched fetal heart rate variability measures were correlated to the levels of interleukin-6 and tumor necrosis factor-α. Upon necropsy, ionized calcium binding adaptor molecule 1+ (Iba1+), CD11c+ (M1), CD206+ (M2 macrophages), and occludin (leakiness marker) immunofluorescence in the terminal ileum was quantified along with regional Iba1+ signal in the brain (microglia). Interleukin-6 peaked at 3 hours post lipopolysaccharide accompanied by mild cardiovascular signs of sepsis. At 54 hours, we identified an increase in Iba1+ and, specifically, M1 macrophages in the ileum accompanied by increased leakiness, with no change in Iba1 signal in the brain. Preceding this change on tissue level, at 24 hours, a subset of nine fetal heart rate variability measures correlated exclusively to the Iba+ markers of ileal, but not brain, inflammation. An additional fetal heart rate variability measure, mean of the differences of R-R intervals, correlated uniquely to M1 ileum macrophages increasing due to lipopolysaccharide. CONCLUSIONS: We identified a unique subset of fetal heart rate variability measures reflecting 1.5 days ahead of time the levels of macrophage activation and increased leakiness in terminal ileum. We propose that such subset of fetal heart rate variability measures reflects brain-gut communication via the vagus nerve. Detecting such noninvasively obtainable organ-specific fetal heart rate variability signature of inflammation would alarm neonatologists about neonates at risk of developing necrotizing enterocolitis and sepsis. Clinical validation studies are required.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.023
GPT teacher head0.304
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations27
Published2016
Admission routes2
Has abstractyes

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