Evidence map›Paper›PMID 21556956›Full record

ArticleMedical & biological engineering & computing2011

Computational analysis of nanoparticle adhesion to endothelium: effects of kinetic rate constants and wall shear rates.

Moon June Kim, Kyehan Rhee

Abstract read
PubMed Publisher
In one paragraph

Article in Medical & biological engineering & computing, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.5field-weighted citation impact, top 33% 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

5 citing papers in PubMed, 9 citations in OpenAlex.

  1. Multiscale physics-basedFrontiers in drug delivery · 2024
    Review
  2. Article
  3. Article
  4. Organ-on-a-chip platforms for studying drug delivery systems.Journal of controlled release : official journal of the Controlled Release Society · 2014
    Review
  5. 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

2 authors at 1 institution in 1 country.

Moon June KimDepartment of Mechanical Engineering, Myongji University, 38-2, Namdong, Yongin, Kyunggi-do 449-728, South Korea.
Kyehan Rhee
Myongji University · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Various nanoparticles have been developed as imaging probes and drug carriers, and their selectivity in binding to target cells determines the efficacy of these functionalized nanoparticles. Since target cells in different arterial segments experience different hemodynamic environments, we study the effects of wall shear rate waveforms on particle binding. We also explore the effects of the kinetic rate constant, which is determined by particle design parameters, on particle binding. A transport and reaction model is used to evaluate nanoparticle binding to the substrate in a laminar flow chamber. Flow and particle concentration fields are solved by using a computational fluid dynamics. The particle binding rate increases as the mean value of wall shear increases, and the amplitudes of sinusoidal shear waveform do not affect the bound particle density profiles significantly. Particle binding rates increase with the rate constant of attachment (k(A)), and are more sensitively affected by low k(A) values and less by k(A) values higher than 1 × 10⁻⁶ m s⁻¹. Since binding selectivity is affected by k(A) and the wall shear rate, the results of this study can be used for designing functionalized nanoparticles targeting for the specific cells that experience a specific shear rate.

Indexed as

Models, BiologicalCell AdhesionComputational BiologyEndothelium, VascularHumansHydrodynamicsNanoparticlesStress, Mechanical

Identifiers

PMID21556956
OpenAlexW2059344953

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.