Evidence map›Paper›PMID 29182964›Full record

ArticleComputers in biology and medicine2018

Mass release curves as the constitutive curves for modeling diffusive transport within biological tissue.

M Kojic, M Milosevic, N Kojic, E J Koay, J B Fleming, M Ferrari, A Ziemys

Abstract read
In one paragraph

Article in Computers in biology and medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

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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

7 authors.

M KojicHouston Methodist Research Institute, The Department of Nanomedicine, 6670 Bertner Ave., R7-117, Houston, TX 77030, United States; Bioengineering Research and Development Center BioIRC Kragujevac, Prvoslava Stojanovica 6, 3400 Kragujevac, Serbia; Serbian Academy of Sciences and Arts, Knez Mihailova 35, 11000 Belgrade, Serbia. Electronic address: mkojic@gmail.com.
M MilosevicBioengineering Research and Development Center BioIRC Kragujevac, Prvoslava Stojanovica 6, 3400 Kragujevac, Serbia.
N KojicCenter for Engineering in Medicine and Surgical Services, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, United States.
E J KoayDepartment of Radiation Oncology, MD Anderson Cancer Center, Houston, TX 77030, United States.
J B FlemingDepartment of Surgical Oncology, MD Anderson Cancer Center, Houston, TX 77030, United States.
M FerrariHouston Methodist Research Institute, The Department of Nanomedicine, 6670 Bertner Ave., R7-117, Houston, TX 77030, United States.
A ZiemysHouston Methodist Research Institute, The Department of Nanomedicine, 6670 Bertner Ave., R7-117, Houston, TX 77030, United States.

Funding

Texas Center for Cancer NanomedicineU54CA151668 · NCI · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI FERRARI, MAURO · 2010 to 2014
$12.4M
TRANS-NETWORK PROJECTSU54CA143837 · NCI · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI FERRARI, MAURO · 2009 to 2014
$10.8M
Transport Oncophysics CoreU54CA210181 · NCI · METHODIST HOSPITAL RESEARCH INSTITUTE · PI CHANG, JENNY C-N · 2016 to 2020
$9.3M
NCI NIH HHS U54 CA143837NCI NIH HHS U54 CA151668NCI NIH HHS U54 CA210181
6 · The paper itself

Abstract

In diffusion governed by Fick's law, the diffusion coefficient represents the phenomenological material parameter and is, in general, a constant. In certain cases of diffusion through porous media, the diffusion coefficient can be variable (i.e. non-constant) due to the complex process of solute displacements within microstructure, since these displacements depend on porosity, internal microstructural geometry, size of the transported particles, chemical nature, and physical interactions between the diffusing substance and the microstructural surroundings. In order to provide a simple and general approach of determining the diffusion coefficient for diffusion through porous media, we have introduced mass release curves as the constitutive curves of diffusion. The mass release curve for a selected direction represents cumulative mass (per surface area) passed in that direction through a small reference volume, in terms of time. We have developed a methodology, based on numerical Finite Element (FE) and Molecular Dynamics (MD) methods, to determine simple mass release curves of solutes through complex media from which we calculate the diffusion coefficient. The diffusion models take into account interactions between solute particles and microstructural surfaces, as well as hydrophobicity (partitioning). We illustrate the effectiveness of our approach on several examples of complex composite media, including an imaging-based analysis of diffusion through pancreatic cancer tissue. The presented work offers an insight into the role of mass release curves in describing diffusion through porous media in general, and further in case of complex composite media such as biological tissue.

Indexed as

Biological TransportModels, BiologicalComputer SimulationDiffusionHumansPancreatic NeoplasmsPorosityBiological tissueDiffusionEquivalent diffusion coefficientMass release curveMultiscale modelNumerical homogenizationPorous material

Identifiers

PMID29182964
PMCPMC5927384

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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.