Evidence map›Paper›PMID 26317654›Full record

ArticlePLoS computational biology2015

Testing Foundations of Biological Scaling Theory Using Automated Measurements of Vascular Networks.

Mitchell G Newberry, Daniel B Ennis, Van M Savage

Abstract read
In one paragraph

Article in PLoS computational biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 1 pooled it
–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

19 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  7. Article
  8. Seeing through noise in power laws.Journal of the Royal Society, Interface · 2023
    Article
  9. Article
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  12. Coevolution of body size and metabolic rate in vertebrates: a life-history perspective.Biological reviews of the Cambridge Philosophical Society · 2020
    Article
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  14. Review
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  19. Energy and time determine scaling in biological and computer designs.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2016
    Article
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

3 authors.

Mitchell G NewberryDepartment of Biomathematics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, United States of America.
Daniel B EnnisDepartment of Radiological Sciences, Biomedical Physics, and Bioengineering, University of California, Los Angeles, Los Angeles, California, United States of America.
Van M SavageDepartment of Biomathematics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, United States of America; Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, California, United States of America; Santa Fe Institute, Santa Fe, New Mexico, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Scientists have long sought to understand how vascular networks supply blood and oxygen to cells throughout the body. Recent work focuses on principles that constrain how vessel size changes through branching generations from the aorta to capillaries and uses scaling exponents to quantify these changes. Prominent scaling theories predict that combinations of these exponents explain how metabolic, growth, and other biological rates vary with body size. Nevertheless, direct measurements of individual vessel segments have been limited because existing techniques for measuring vasculature are invasive, time consuming, and technically difficult. We developed software that extracts the length, radius, and connectivity of in vivo vessels from contrast-enhanced 3D Magnetic Resonance Angiography. Using data from 20 human subjects, we calculated scaling exponents by four methods-two derived from local properties of branching junctions and two from whole-network properties. Although these methods are often used interchangeably in the literature, we do not find general agreement between these methods, particularly for vessel lengths. Measurements for length of vessels also diverge from theoretical values, but those for radius show stronger agreement. Our results demonstrate that vascular network models cannot ignore certain complexities of real vascular systems and indicate the need to discover new principles regarding vessel lengths.

Indexed as

Models, CardiovascularAdultAlgorithmsCarotid Artery DiseasesComputational BiologyHumansMagnetic Resonance AngiographyProspective StudiesRegression Analysis

Identifiers

PMID26317654
PMCPMC4552567

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Registered trials

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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.