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Record W2157084135 · doi:10.1152/jappl.2001.91.1.316

Ultrasound measurements of fetal breathing movements in the rat

2001· article· en· W2157084135 on OpenAlexaff
Koichi Kobayashi, Robert P. Lemke, John J. Greer

Bibliographic record

VenueJournal of Applied Physiology · 2001
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience of respiration and sleep
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAminophyllineRespiratory systemMedicineInternal medicineNeuroscienceBiology

Abstract

fetched live from OpenAlex

The goal of this study was to determine when fetal breathing movements (FBMs) commence in the rat and to characterize age-dependent changes of FBMs in utero. These data provide a frame of reference for parallel in vitro studies of the cellular, synaptic, and network properties of the perinatal rat respiratory system. Ultrasound recordings were made from unanesthetized Sprague-Dawley rats from embryonic (E) day 15 (E15) to E20. Furthermore, the effects of respiratory stimulants (doxapram and aminophylline) and hypoxia on FBMs were studied. Single FBMs, occurring at a very low frequency (approximately 8 FBMs/h), commenced at E16. The incidence of single FBMs increased to approximately 80 FBMs/h by E20. Episodes of clustered rhythmic FBMs were first observed at E18 (approximately 40 FBMs/h). The incidence of episodic clustered FBMs increased to approximately 300 FMBs/h by E20, with the duration of each episode ranging from approximately 40 to 180 s. Doxapram, presumably acting to stimulate carotid body receptors, did not increase FBMs until E20, when the incidence of episodic clustered FBMs increased twofold. Aminophylline, a central-acting stimulant, caused an increase in episodic clustered FBMs after E17, reaching significance at E20 (3-fold increase). Exposing the dam to 10% O(2) caused a rapid, marked suppression of FBMs (5-fold decrease) that was readily reversed on exposure to room air.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.224

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.056
GPT teacher head0.291
Teacher spread0.235 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations89
Published2001
Admission routes1
Has abstractyes

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