Evidence map›Paper›PMID 27629885›Full record

ArticleAmerican journal of physiology. Regulatory, integrative and comparative physiology2016

Nonlinear identification of the total baroreflex arc: higher-order nonlinearity.

Mohsen Moslehpour, Toru Kawada, Kenji Sunagawa, Masaru Sugimachi, Ramakrishna Mukkamala

Abstract read
In one paragraph

Article in American journal of physiology. Regulatory, integrative and comparative physiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Mohsen MoslehpourDepartment of Electrical and Computer Engineering, Michigan State University, East Lansing, Michigan.
Toru KawadaDepartment of Cardiovascular Dynamics, National Cerebral and Cardiovascular Center, Osaka, Japan; and.
Kenji SunagawaDepartment of Cardiovascular Medicine, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.
Masaru SugimachiDepartment of Cardiovascular Dynamics, National Cerebral and Cardiovascular Center, Osaka, Japan; and.
Ramakrishna MukkamalaDepartment of Electrical and Computer Engineering, Michigan State University, East Lansing, Michigan; rama@egr.msu.edu.

Funding

Unobtrusive and Affordable Blood Pressure Monitoring Via Pulse Transit TimeU01EB018818 · NIBIB · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI MUKKAMALA, RAMAKRISHNA · 2014 to 2018
$3.4M
NIBIB NIH HHS U01 EB018818
6 · The paper itself

Abstract

The total baroreflex arc is the open-loop system relating carotid sinus pressure (CSP) to arterial pressure (AP). The nonlinear dynamics of this system were recently characterized. First, Gaussian white noise CSP stimulation was employed in open-loop conditions in normotensive and hypertensive rats with sectioned vagal and aortic depressor nerves. Nonparametric system identification was then applied to measured CSP and AP to establish a second-order nonlinear Uryson model. The aim in this study was to assess the importance of higher-order nonlinear dynamics via development and evaluation of a third-order nonlinear model of the total arc using the same experimental data. Third-order Volterra and Uryson models were developed by employing nonparametric and parametric identification methods. The R

Indexed as

Models, CardiovascularAnimalsBaroreflexBlood PressureCarotid SinusComputer SimulationHumansHypertensionModels, StatisticalNonlinear DynamicsPressoreceptorsRatsarterial baroreflexGaussian white noisehigher-order nonlinear modelhypertensionsystem identification

Identifiers

PMID27629885
PMCPMC5256968

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.