Evidence map›Paper›PMID 27037372›Full record

ArticleAmerican journal of physiology. Heart and circulatory physiology2016

Open-loop (feed-forward) and feedback control of coronary blood flow during exercise, cardiac pacing, and pressure changes.

Ranjan K Pradhan, Eric O Feigl, Mark W Gorman, George L Brengelmann, Daniel A Beard

Open access · bronzeAbstract read
In one paragraph

Article in American journal of physiology. Heart and circulatory physiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
1.8field-weighted citation impact, top 16% of its field
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

10 citing papers in PubMed, 20 citations in OpenAlex.

  1. Article
  2. Red Blood Cell Membrane Mechanics Using Discrete Exterior Calculus (DEC) and Optimization.International journal for numerical methods in biomedical engineering · 2026
    Article
  3. Article
  4. Unraveling the Gordian knot of coronary pressure-flow autoregulation.Journal of molecular and cellular cardiology · 2024
    Review
  5. Article
  6. Overview of mathematical modeling of myocardial blood flow regulation.American journal of physiology. Heart and circulatory physiology · 2020
    Review
  7. Disentangling the Gordian knot of local metabolic control of coronary blood flow.American journal of physiology. Heart and circulatory physiology · 2020
    Review
  8. Coronary arterial vasculature in the pathophysiology of hypertrophic cardiomyopathy.Pflugers Archiv : European journal of physiology · 2019
    Review
  9. Article
  10. Regulation of Coronary Blood Flow.Comprehensive Physiology · 2017
    Review
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 at 2 institutions in 1 country.

Ranjan K PradhanDepartment of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, Michigan; and.
Eric O FeiglDepartment of Physiology and Biophysics, University of Washington, Seattle, Washington.
Mark W GormanDepartment of Physiology and Biophysics, University of Washington, Seattle, Washington.
George L BrengelmannDepartment of Physiology and Biophysics, University of Washington, Seattle, Washington.
Daniel A BeardDepartment of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, Michigan; and beardda@umich.edu.
University of Washington · USUniversity of Michigan–Ann Arbor · US

Funding

Coronary Blood Flow: Integrated Theory and ExperimentsU01HL118738 · NHLBI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI BEARD, DANIEL A, KASSAB, GHASSAN S · 2013 to 2018
$3.4M
NHLBI NIH HHS U01 HL118738
6 · The paper itself

Abstract

A control system model was developed to analyze data on in vivo coronary blood flow regulation and to probe how different mechanisms work together to control coronary flow from rest to exercise, and under a variety of experimental conditions, including cardiac pacing and with changes in coronary arterial pressure (autoregulation). In the model coronary flow is determined by the combined action of a feedback pathway signal that is determined by the level of plasma ATP in coronary venous blood, an adrenergic open-loop (feed-forward) signal that increases with exercise, and a contribution of pressure-mediated myogenic control. The model was identified based on data from exercise experiments where myocardial oxygen extraction, coronary flow, cardiac interstitial norepinephrine concentration, and arterial and coronary venous plasma ATP concentrations were measured during control and during adrenergic and purinergic receptor blockade conditions. The identified model was used to quantify the relative contributions of open-loop and feedback pathways and to illustrate the degree of redundancy in the control of coronary flow. The results indicate that the adrenergic open-loop control component is responsible for most of the increase in coronary blood flow that occurs during high levels of exercise. However, the adenine nucleotide-mediated metabolic feedback control component is essential. The model was evaluated by predicting coronary flow in cardiac pacing and autoregulation experiments with reasonable fits to the data. The analysis shows that a model in which coronary venous plasma adenine nucleotides are a signal in local metabolic feedback control of coronary flow is consistent with the available data.

Indexed as

Models, CardiovascularAnimalsBlood PressureCoronary CirculationCoronary VesselsDogsFeedback, PhysiologicalHeart RateHemodynamicsOxygen ConsumptionPhysical Conditioning, AnimalVasodilationautoregulationcardiac pacingcoronary blood flowfeedback controlmyogenic controlopen-loop (feed-forward) controloxygen mass balance

Identifiers

PMID27037372
PMCPMC4935508
OpenAlexW2327850105

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.