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Record W2893821129 · doi:10.1113/ep086995

Genioglossus muscle activity during sniff and reverse sniff in healthy men

2018· article· en· W2893821129 on OpenAlexaff
Tsuyoshi Ichikawa, Masanori Yokoba, Masahiko Kimura, Manaka Shibuya, Paul A. Easton, Masato Katagiri

Bibliographic record

VenueExperimental Physiology · 2018
Typearticle
Languageen
FieldMedicine
TopicObstructive Sleep Apnea Research
Canadian institutionsUniversity of Calgary
FundersKitasato University
KeywordsGenioglossusMedicineIntercostal muscleAnesthesiaAirwayNoseAnatomyRespiratory system

Abstract

fetched live from OpenAlex

New Findings What is the central question of this study? Maximal sniff nasal inspiratory and reverse sniff nasal expiratory pressures are measured as inspiratory and expiratory muscle strength, respectively. Is the genioglossus muscle activated during short maximal inspiratory and expiratory efforts through the nose? What is the main finding and its importance? Genioglossus muscle activity occurred with inspiratory muscle activity during a maximal sniff and with expiratory muscle activity during a maximal reverse sniff. These results indicate that genioglossus muscle activity is closely related to the generation of maximal sniff nasal inspiratory and reverse sniff nasal expiratory pressures. Abstract Maximal sniff nasal inspiratory pressure (SNIP max ) is widely used to assess inspiratory muscle strength. The sniff nasal inspiratory pressure (SNIP) is lower in patients with neuromuscular disease with bulbar involvement compared with those without, possibly owing to impaired upper airway muscle function. However, the degree to which the genioglossus (GG) muscle, one of the upper airway muscles, is activated during inspiratory and expiratory efforts through the nose remains unclear. Therefore, we examined GG activity during short and sharp inspiratory and expiratory efforts through the nose, i.e. sniff and reverse sniff manoeuvres. In eight normal young subjects, we inserted fine wire electrodes into the GG muscle, parasternal intercostal and scalene (inspiratory) muscles and transversus abdominis (expiratory) muscle. We assessed EMG activity of each muscle and measured SNIP and reverse sniff nasal expiratory pressure (RSNEP) during sniffs and reverse sniffs from low to high intensities in the sitting position. The highest SNIP and RSNEP were analysed as SNIP max and maximal RSNEP (RSNEP max ), respectively. In each subject, GG EMG activity increased linearly with increasing SNIP and RSNEP. The SNIP max and RSNEP max were −85.1 ± 15.9 and 83.2 ± 24.2 cmH 2 O, respectively. Genioglossus EMG activity varied with EMG activity of the parasternal intercostal and scalene muscles during generation of SNIP max and with EMG activity of the transversus abdominis muscle during RSNEP max . Genioglossus EMG activity during generation of SNIP max was higher than during RSNEP max (62.9 ± 31.1% EMG of SNIP max , P = 0.012). These results suggested that GG activity was closely related to the generation of both SNIP max and RSNEP max .

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.575
Threshold uncertainty score0.619

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.021
GPT teacher head0.343
Teacher spread0.322 · 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

Citations3
Published2018
Admission routes1
Has abstractyes

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