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Sympathetic Discharge Patterns and Neurovascular Transduction Following Acute Intermittent Hypoxia

2019· article· en· W3176681856 on OpenAlexaff
Dain W. Jacob, Sarah E. Baker, Zachariah E Scruggs, Elizabeth P. Ott, Jennifer L. Harper, Camila L Manrique, J. Kevin Shoemaker, Jacqueline K. Limberg

Bibliographic record

VenueThe FASEB Journal · 2019
Typearticle
Languageen
FieldMedicine
TopicHeart Rate Variability and Autonomic Control
Canadian institutionsWestern University
FundersNational Institutes of HealthAmerican Physiological Society
KeywordsMicroneurographyMedicineBlood pressureCardiologyAnesthesiaInternal medicineHypoxia (environmental)Heart rateChemistryBaroreflexOxygen

Abstract

fetched live from OpenAlex

Background Muscle sympathetic nerve activity (MSNA) is augmented in patients with sleep apnea. This increase in activity is due to increases in firing probability and mean firing rates of individual vasoconstrictor neurons, in addition to multiple within‐burst firing; however, contributing mechanisms and the impact of such firing patterns on peripheral vascular tone are less clear. We examined the effect of acute intermittent hypoxia (IH) on sympathetic neural firing and recruitment strategies and the relationship between changes in MSNA and neurovascular transduction following acute IH. We hypothesized exposure to acute IH would increase MSNA via an increase in action potential (AP) discharge rates and within‐burst firing. We further hypothesized any change in discharge patterns following acute IH would be positively related to a change in neurovascular transduction. Methods Blood pressure (brachial arterial catheter) and MSNA (microneurography of the peroneal nerve) were assessed in 17 healthy adults (11M/6F; 31 ± 1 yrs) during quiet normoxic rest prior to and following 30‐min of experimental IH [30‐s hypoxic (0.05 F i O 2 ) exposures alternating with 90‐s room air breathing]. AP patterns were studied from the filtered raw MSNA signal using wavelet‐based methodology. The slope of the relationship between diastolic blood pressure (DBP) and the preceding MSNA burst area at a fixed cardiac cycle lag was used to quantify neurovascular transduction. Results IH resulted in 15 events where oxygen saturation was reduced (S p O 2 : 99 ± 1% vs 92 ± 1%, P <0.01). Compared to baseline, multi‐unit MSNA burst incidence (33 ± 4 to 43 ± 5 bursts/100 heart beats), AP incidence (272 ± 50 to 501 ± 112 AP/100 heart beats) and AP content per integrated burst (7 ± 1 to 10 ± 1 AP/burst; 4 ± 1 to 5 ± 1 clusters/burst) were increased following IH (all P< 0.05). Neurovascular transduction was not altered following IH ( P =0.34); however, there was a positive relationship between the change (Δ) in neurovascular transduction (mmHg/%/s) and changes in AP discharge rate (AP/100 heart beats; R=0.52, P= 0.03) and within‐burst firing (AP/burst; R=0.38, P= 0.14) following IH. Conclusions Acute IH increases sympathetic neuronal discharge by increasing within‐burst firing of low‐threshold axons. A positive relationship exists between increases in the neural discharge rates and neurovascular transduction following acute IH, such that those individuals with the greatest increase in AP firing exhibited the greatest increase in transduction of sympathetic activity into blood pressure. These data may have important implications for mechanistic understanding of sympathetic activation in sleep apnea and its down‐stream effects, including the development of hypertension and cardiovascular disease. Support or Funding Information Funding: NIH HL130339, Mayo Clinic Center for Biomedical Discovery, Mizzou HES PURE, APS UGSRF. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.009
GPT teacher head0.229
Teacher spread0.220 · 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 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".

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Citations1
Published2019
Admission routes1
Has abstractyes

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