ReviewNanoscale advances2021
Understanding nano-engineered particle-cell interactions: biological insights from mathematical models.
Review in Nanoscale advances, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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.
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.
Who cites it
8 citing papers in PubMed.
- Hybrid Physics-Informed and Bayesian Modeling of Single-Nanoparticle-Cell Adhesion Kinetics under Cytoskeletal Perturbation.Computational and structural biotechnology journal · 2026Article
- Quantifying biological heterogeneity in nano-engineered particle-cell interaction experiments.Journal of the Royal Society, Interface · 2025Article
- Exploring Gluconamide-Modified Silica Nanoparticles of Different Sizes as Effective Carriers for Antimicrobial Photodynamic Therapy.Nanomaterials (Basel, Switzerland) · 2024Article
- Modeling of spatiotemporal dynamics of ligand-coated particle flow in targeted drug delivery processes.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Development of an ultrasound-mediated nano-sized drug-delivery system for cancer treatment: from theory to experiment.Nanomedicine (London, England) · 2024Article
- A Novel Experimental Approach to Understand the Transport of Nanodrugs.Materials (Basel, Switzerland) · 2023Article
- Article
- Identifying cell-to-cell variability in internalization using flow cytometry.Journal of the Royal Society, Interface · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Understanding the interactions between nano-engineered particles and cells is necessary for the rational design of particles for therapeutic, diagnostic and imaging purposes. In particular, the informed design of particles relies on the quantification of the relationship between the physicochemical properties of the particles and the rate at which cells interact with, and subsequently internalise, particles. Quantitative models, both mathematical and computational, provide a powerful tool for elucidating this relationship, as well as for understanding the mechanisms governing the intertwined processes of interaction and internalisation. Here we review the different types of mathematical and computational models that have been used to examine particle-cell interactions and particle internalisation. We detail the mathematical methodology for each type of model, the benefits and limitations associated with the different types of models, and highlight the advances in understanding gleaned from the application of these models to experimental observations of particle internalisation. We discuss the recent proposal and ongoing community adoption of standardised experimental reporting, and how this adoption is an important step toward unlocking the full potential of modelling approaches. Finally, we consider future directions in quantitative models of particle-cell interactions and highlight the need for hybrid experimental and theoretical investigations to address hitherto unanswered questions.
Identifiers
What OpenQuestion holds
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.