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Record W2965716697 · doi:10.1113/jp278376

Arterial baroreflex regulation of muscle sympathetic nerve activity at rest and during stress

2019· article· en· W2965716697 on OpenAlexafffund
Anthony V. Incognito, Sergiu‐Gabriel Duplea, Jordan B. Lee, Jess Sussman, Andrew D. Shepherd, Connor J. Doherty, Joseph Cacoilo, Karambir Notay, Philip J. Millar

Bibliographic record

VenueThe Journal of Physiology · 2019
Typearticle
Languageen
FieldMedicine
TopicHeart Rate Variability and Autonomic Control
Canadian institutionsUniversity of Guelph-HumberToronto General HospitalUniversity of Guelph
FundersCanada Foundation for InnovationNatural Sciences and Engineering Research Council of CanadaOntario Ministry of Research, Innovation and ScienceGovernment of Canada
KeywordsBlood pressureBaroreflexHeart rateCardiologyBeat (acoustics)MedicineInternal medicineHemodynamicsAnesthesia

Abstract

fetched live from OpenAlex

Key points The arterial baroreflex controls vasoconstrictor muscle sympathetic nerve activity (MSNA) in a negative feedback manner by increasing or decreasing activity during spontaneous blood pressure falls or elevations, respectively. Spontaneous sympathetic baroreflex sensitivity is commonly quantified as the slope of the relationship between MSNA burst incidence or strength and beat‐to‐beat variations in absolute diastolic blood pressure. We assessed the relationships between blood pressure inputs related to beat‐to‐beat blood pressure change or blood pressure rate‐of‐change (variables largely independent of absolute pressure) and MSNA at rest and during exercise and mental stress. The number of participants with strong linear relationships between MSNA and beat‐to‐beat diastolic blood pressure change variables or absolute diastolic blood pressure were similar at rest, although during stress the beat‐to‐beat diastolic blood pressure change variables were superior. Current methods may not fully characterize the capacity of the arterial baroreflex to regulate MSNA. Abstract Spontaneous sympathetic baroreflex sensitivity (sBRS) is commonly quantified as the slope of the relationship between variations in absolute diastolic blood pressure (DBP) and muscle sympathetic nerve activity (MSNA) burst incidence or strength. This relationship is well maintained at rest but not during stress. We assessed whether sBRS could be calculated at rest and during stress (static handgrip, rhythmic handgrip, mental stress) using blood pressure variables that quantify relative change: beat‐to‐beat DBP change (ΔDBP), ΔDBP rate‐of‐change (ΔDBP rate), pulse pressure (PP) and PP rate‐of‐change (PP rate). Sixty‐six healthy participants underwent continuous measures of blood pressure (finger photoplethysmography) and multi‐unit MSNA (microneurography). At rest, absolute DBP (91%), ΔDBP (97%) and ΔDBP rate (97%) each yielded higher proportions of participants with strong linear relationships ( r ≥ 0.6) with MSNA burst incidence compared to PP (57%) and PP rate (56%) and produced similar sBRS slopes (DBP: −4.5 ± 2.0 bursts 100 heartbeats –1 /mmHg; ΔDBP: −5.0 ± 2.1 bursts 100 heartbeats –1 /ΔmmHg; ΔDBP rate: −4.9 ± 2.2 bursts 100 heartbeats –1 /ΔmmHg s –1 ; P > 0.05). During stress, ΔDBP (74%) and ΔDBP rate (74%) yielded higher proportions of strong linear relationships with MSNA burst incidence than absolute DBP (43%), PP (46%) and PP rate (49%) (all P < 0.05). The absolute DBP associated with a 50% chance of a MSNA burst ( T 50 ) was shifted rightward during static handgrip (Δ+15 ± 11 mmHg, P < 0.001) and mental stress (Δ+11 ± 7 mmHg, P < 0.001); however, the ΔDBP T 50 was shifted rightward during static handgrip (Δ+2.5 ± 3.7 mmHg, P = 0.009) but not mental stress (Δ0.0 ± 4.4 mmHg, P = 0.99). These findings suggest that calculating sBRS using absolute DBP alone may not adequately characterize arterial baroreflex regulation of MSNA, particularly during stress.

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 score0.804
Threshold uncertainty score0.158

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 head0.011
GPT teacher head0.246
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

Citations27
Published2019
Admission routes2
Has abstractyes

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