Evidence map›Paper›PMID 26927968›Full record

ArticleBMC bioinformatics2016

Computational modeling of phagocyte transmigration for foreign body responses to subcutaneous biomaterial implants in mice.

Mingon Kang, Liping Tang, Jean Gao

Abstract read
In one paragraph

Article in BMC bioinformatics, 2016. 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. 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

3 authors.

Mingon KangDepartment of Computer Science and Engineering, University of Texas at Arlington, 500 UTA Blvd., Arlington, 76019, USA. mingon.kang@mavs.uta.edu.
Liping TangDepartment of Bioengineering, University of Texas at Arlington, 500 UTA Blvd., Arlington, 76019, USA. ltang@uta.edu.
Jean GaoDepartment of Computer Science and Engineering, University of Texas at Arlington, 500 UTA Blvd., Arlington, 76019, USA. gao@uta.edu.

Funding

NIBIB NIH HHS R01EB00727
6 · The paper itself

Abstract

backgroundComputational modeling and simulation play an important role in analyzing the behavior of complex biological systems in response to the implantation of biomedical devices. Quantitative computational modeling discloses the nature of foreign body responses. Such understanding will shed insight on the cause of foreign body responses, which will lead to improved biomaterial design and will reduce foreign body reactions. One of the major obstacles in computational modeling is to build a mathematical model that represents the biological system and to quantitatively define the model parameters.

resultsIn this paper, we considered quantitative inter connections and logical relationships among diverse proteins and cells, which have been reported in biological experiments and literature. Based on the established biological discovery, we have built a mathematical model while unveiling the key components that contribute to biomaterial-mediated inflammatory responses. For the parameter estimation of the mathematical model, we proposed a global optimization algorithm, called Discrete Selection Levenberg-Marquardt (DSLM). This is an extension of Levenberg-Marquardt (LM) algorithm which is a gradient-based local optimization algorithm. The proposed DSLM suggests a new approach for the selection of optimal parameters in the discrete space with fast computational convergence.

conclusionsThe computational modeling not only provides critical clues to recognize current knowledge of fibrosis development but also enables the prediction of yet-to-be observed biological phenomena.

Indexed as

AlgorithmsCell MovementComputer SimulationAnimalsBiocompatible MaterialsForeign BodiesForeign-Body ReactionImplants, ExperimentalMicePhagocytesSubcutaneous TissueBiocompatible Materials

Identifiers

PMID26927968
PMCPMC4772519

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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.