Evidence map›Paper›PMID 24370855›Full record

ArticleMedical & biological engineering & computing2014

Modeling and experimental methods to predict oxygen distribution in bone defects following cell transplantation.

Christopher M Heylman, Sharon Santoso, Melissa D Krebs, Gerald M Saidel, Eben Alsberg, George F Muschler

Abstract read
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In one paragraph

Article in Medical & biological engineering & computing, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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

6 authors.

Christopher M HeylmanDepartment of Biomedical Engineering, Cleveland Clinic Lerner Research Institute, 9500 Euclid Ave ND20, Cleveland, OH, 44195, USA, cheylman@uci.edu.
Sharon Santoso
Melissa D Krebs
Gerald M Saidel
Eben Alsberg
George F Muschler

Funding

Cleveland Clinic Orthopaedic Research Training GrantT32AR050959 · NIAMS · CLEVELAND CLINIC LERNER COM-CWRU · PI IANNOTTI, JOSEPH P. · 2004 to 2008
$1.3M
NIAMS NIH HHS T32 AR050959
6 · The paper itself

Abstract

We have developed a mathematical model that allows simulation of oxygen distribution in a bone defect as a tool to explore the likely effects of local changes in cell concentration, defect size or geometry, local oxygen delivery with oxygen-generating biomaterials (OGBs), and changes in the rate of oxygen consumption by cells within a defect. Experimental data for the oxygen release rate from an OGB and the oxygen consumption rate of a transplanted cell population are incorporated into the model. With these data, model simulations allow prediction of spatiotemporal oxygen concentration within a given defect and the sensitivity of oxygen tension to changes in critical variables. This information may help to minimize the number of experiments in animal models that determine the optimal combinations of cells, scaffolds, and OGBs in the design of current and future bone regeneration strategies. Bone marrow-derived nucleated cell data suggest that oxygen consumption is dependent on oxygen concentration. OGB oxygen release is shown to be a time-dependent function that must be measured for accurate simulation. Simulations quantify the dependency of oxygen gradients in an avascular defect on cell concentration, cell oxygen consumption rate, OGB oxygen generation rate, and OGB geometry.

Indexed as

Models, BiologicalAnimalsBiocompatible MaterialsBone and BonesCell TransplantationComputer SimulationDogsHumansMaleMaterials TestingMiddle AgedOxygenOxygen ConsumptionTissue EngineeringBiocompatible MaterialsOxygen

Identifiers

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.