Evidence map›Paper›PMID 28344287›Full record

ArticleNanomaterials (Basel, Switzerland)2016

Characterization of Nanoparticle Dispersion in Red Blood Cell Suspension by the Lattice Boltzmann-Immersed Boundary Method.

Jifu Tan, Wesley Keller, Salman Sohrabi, Jie Yang, Yaling Liu

Abstract read
In one paragraph

Article in Nanomaterials (Basel, Switzerland), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 citing papers in PubMed.

  1. Multiscale physics-basedFrontiers in drug delivery · 2024
    Review
  2. Article
  3. Article
  4. Article
  5. Review
  6. Article
  7. Multiscale modeling of hemolysis during microfiltration.Microfluidics and nanofluidics · 2020
    Article
  8. Article
  9. A parallel fluid-solid coupling model usingJournal of computational science · 2018
    Article
  10. Article
  11. Numerical simulation of cell squeezing through a micropore by the immersed boundary method.Proceedings of the Institution of Mechanical Engineers. Part C. Journal of mechanical engineering science · 2018
    Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Nanomaterials for Biosensing Applications.Nanomaterials (Basel, Switzerland) · 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

5 authors.

Jifu TanDepartment of Mechanical Engineering and Mechanics, Lehigh University, Bethlehem, PA 18015, USA. jifutan@seas.upenn.edu.
Wesley KellerDepartment of Civil and Environmental Engineering, Lehigh University, Bethlehem, PA 18015, USA. wjk207@lehigh.edu.
Salman SohrabiDepartment of Mechanical Engineering and Mechanics, Lehigh University, Bethlehem, PA 18015, USA. sas713@lehigh.edu.
Jie YangSchool of Mechanics and Engineering, Southwest Jiaotong University, Chengdu 610031, China. yangchenjie@home.swjtu.edu.cn.
Yaling LiuDepartment of Mechanical Engineering and Mechanics, Lehigh University, Bethlehem, PA 18015, USA. yal310@lehigh.edu.

Funding

An Integrated Biometric Platform for Evaluation of Nanomedicine DeliveryR15EB015105 · NIBIB · LEHIGH UNIVERSITY · PI LIU, YALING · 2013 to 2013
$443k
NIBIB NIH HHS R15 EB015105
6 · The paper itself

Abstract

Nanodrug-carrier delivery in the blood stream is strongly influenced by nanoparticle (NP) dispersion. This paper presents a numerical study on NP transport and dispersion in red blood cell (RBC) suspensions under shear and channel flow conditions, utilizing an immersed boundary fluid-structure interaction model with a lattice Boltzmann fluid solver, an elastic cell membrane model and a particle motion model driven by both hydrodynamic loading and Brownian dynamics. The model can capture the multiphase features of the blood flow. Simulations were performed to obtain an empirical formula to predict NP dispersion rate for a range of shear rates and cell concentrations. NP dispersion rate predictions from the formula were then compared to observations from previous experimental and numerical studies. The proposed formula is shown to accurately predict the NP dispersion rate. The simulation results also confirm previous findings that the NP dispersion rate is strongly influenced by local disturbances in the flow due to RBC motion and deformation. The proposed formula provides an efficient method for estimating the NP dispersion rate in modeling NP transport in large-scale vascular networks without explicit RBC and NP models.

Indexed as

cell suspensiondispersion rateimmersed boundary methodlattice Boltzmann methodnanoparticle delivery

Identifiers

PMID28344287
PMCPMC5302481

What OpenQuestion holds

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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.