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Pharmacological Assessment of the Contribution of the Arterial Baroreflex to Sympathetic Discharge Patterns in Healthy Humans

2018· article· en· W3175745368 on OpenAlexaff
Elizabeth P. Ott, Sarah E. Baker, Wayne T. Nicholson, Michael J. Joyner, J. Kevin Shoemaker, Jacqueline K. Limberg

Bibliographic record

VenueThe FASEB Journal · 2018
Typearticle
Languageen
FieldMedicine
TopicHeart Rate Variability and Autonomic Control
Canadian institutionsWestern University
FundersNational Institutes of Health
KeywordsMicroneurographyBaroreflexPhenylephrineHeart rateMedicineAnesthesiaBlood pressureCardiologySodium nitroprussideInternal medicineNitric oxide

Abstract

fetched live from OpenAlex

Objective We examined the contribution of the arterial baroreflex to sympathetic discharge patterns in humans by systematically manipulating the reflex using intravenous nitroprusside and phenylephrine. We hypothesized nitroprusside would increase firing of low‐threshold axons, with no effect on recruitment of latent axons and/or synaptic delays. We further hypothesized phenylephrine would decrease firing of low‐threshold axons, with no effect on synaptic delays. Methods Heart rate (HR, ECG), arterial blood pressure (BP, brachial arterial catheter), and multi‐unit muscle sympathetic nerve activity (MSNA, microneurography of the peroneal nerve) were measured in 13 male subjects (30±2 yrs, 25±1 kg/m 2 ) at baseline and during systemic intravenous infusion of nitroprusside (0.4–1.7 μg/kg/min) and phenylephrine (0.2–1.0 μg/kg/min), administered separately. Action potential (AP) patterns were studied from the filtered raw signal using wavelet‐based methodology. Results Compared to baseline, BP decreased and HR and multi‐unit MSNA (41±5 to 53±4 bursts/100 heart beats) increased with nitroprusside (all P <0.01). Nitroprusside increased AP incidence (326±66 to 579±129 AP/100 heart beats) and AP content per integrated burst (8±1 to 11±2 AP/burst) (all P <0.01). The firing probability of low‐threshold axons increased with nitroprusside, and recruitment of previously latent, high‐threshold axons was observed (22±3 to 24±3 max clusters, 9±1 to 11±1 clusters/burst; all P <0.05). Compared to baseline, BP increased and HR and multi‐unit MSNA (41±3 to 21±3 bursts/100 heart beats) decreased with phenylephrine (all P <0.01). Phenylephrine decreased AP discharge rate (406±128 to 166±42 AP/100 heart beats) and resulted in fewer unique clusters (15±2 to 9±1 max clusters, all P <0.05); however, components of an integrated burst were not altered (9±2 vs 8±1 AP/burst; 5±1 vs 4±1 clusters/burst, P >0.05). AP cluster latency decreased with cluster size under both conditions ( P <0.01), but this relationship was not altered with nitroprusside ( P =0.42) or phenylephrine ( P =0.39). Conclusions Baroreflex unloading with systemic nitroprusside increases sympathetic neuronal discharge rate by increasing the firing probability of low‐threshold axons, in addition to recruitment of previously latent, high‐threshold axons. In contrast, baroreflex loading with systemic phenylephrine decreases neuronal discharge rate, including decreased firing probability of high‐threshold axons; however, the components of an integrated burst (AP and clusters per burst) and synaptic delays are unaffected. These data support a hierarchical pattern of sympathetic neural recruitment/de‐recruitment and suggest acute modification of synaptic delays are not influenced by the baroreflex. Support or Funding Information NIH HL083947, NIH HL130339 This abstract is from the Experimental Biology 2018 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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · 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
Metaresearch0.0000.000
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)0.0010.000

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 head0.026
GPT teacher head0.344
Teacher spread0.318 · 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 designObservational
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
Published2018
Admission routes1
Has abstractyes

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