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Record W4408385886 · doi:10.14814/phy2.70142

Identification of multiple hypoxia‐independent triggers of upper airway long‐term facilitation in a rat model of upper airway motor plasticity

2025· article· en· W4408385886 on OpenAlexaff
Simon Lui, Arash Tadjalli, Jimmy J. Fraigne, John Peever

Bibliographic record

VenuePhysiological Reports · 2025
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience of respiration and sleep
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsOptogeneticsNeuroscienceBrainstemLocus coeruleusRespiratory systemIntermittent hypoxiaAirwayStimulationHypoglossal nerveReceptorBiologyMedicineChemistryAnesthesiaInternal medicineAnatomyObstructive sleep apneaCentral nervous systemPathology

Abstract

fetched live from OpenAlex

The respiratory control system can exhibit neuronal plasticity following exposures to repetitive respiratory challenges. For example, repeated obstructive apneas can trigger a form of respiratory plasticity that results in the enhancement of inspiratory hypoglossal (XII) motoneuron activity. This increase in respiratory motor output is known as hypoglossal long-term facilitation (hLTF). In adult male Sprague-Dawley rats, we demonstrate that hLTF can also be triggered in the absence of repeated apneas by intermittent optogenetic stimulation of locus coeruleus (LC) neurons, or through pharmacological activation of adenosine-A2a-receptors at the level of brainstem XII motor pool. Both our pharmacological and optogenetic approaches that trigger hLTF require noradrenergic signaling through activation of α1-noradrenergic receptors on hypoglossal motoneurons. We also use optical LC inhibition to reaffirm the importance of the LC in mediating apnea-induced hLTF. These results demonstrate that hLTF can be triggered by multiple hypoxia-independent stimuli, and for the first time, identify the LC as a key brainstem source for noradrenaline necessary for the expression of hLTF.

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)0.000
metaresearch head score (Gemma)0.005
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 score0.140
Threshold uncertainty score0.603

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.005
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 head0.046
GPT teacher head0.299
Teacher spread0.253 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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