ArticleMicrofluidics and nanofluidics2017
Predicting different adhesive regimens of circulating particles at blood capillary walls.
Article in Microfluidics and nanofluidics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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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.
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.
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Who cites it
7 citing papers in PubMed, 24 citations in OpenAlex.
- Multiscale physics-basedFrontiers in drug delivery · 2024Review
- Nanoconstructs for theranostic application in cancer: Challenges and strategies to enhance the delivery.Frontiers in pharmacology · 2023Review
- The role of adhesive receptor patterns on cell transport in complex microvessels.Biomechanics and modeling in mechanobiology · 2022Article
- An Updated Review on EPR-Based Solid Tumor Targeting Nanocarriers for Cancer Treatment.Cancers · 2022Review
- Cell Inertia: Predicting Cell Distributions in Lung Vasculature to Optimize Re-endothelialization.Frontiers in bioengineering and biotechnology · 2022Article
- Capsules Rheology in Carreau-Yasuda Fluids.Nanomaterials (Basel, Switzerland) · 2020Article
- Deciphering the relative contribution of vascular inflammation and blood rheology in metastatic spreading.Biomicrofluidics · 2018Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A fundamental step in the rational design of vascular targeted particles is the firm adhesion at the blood vessel walls. Here, a combined lattice Boltzmann-immersed boundary model is presented for predicting the near-wall dynamics of circulating particles. A moving least squares algorithm is used to reconstruct the forcing term accounting for the immersed particle, whereas ligand-receptor binding at the particle-wall interface is described via forward and reverse probability distributions. First, it is demonstrated that the model predicts with good accuracy the rolling velocity of tumor cells over an endothelial layer in a microfluidic channel. Then, particle-wall interactions are systematically analyzed in terms of particle geometries (circular, elliptical with aspect ratios 2 and 3), surface ligand densities (0.3, 0.5, 0.7 and 0.9), ligand-receptor bond strengths (1 and 2) and Reynolds numbers (
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Registered trials
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.