ArticleAdvanced healthcare materials2021
Particle Stiffness and Surface Topography Determine Macrophage-Mediated Removal of Surface Adsorbed Particles.
Article in Advanced healthcare materials, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed, 11 citations in OpenAlex.
- Transport of Adoptive Cell Transfers With Magnetic Helical Microrobots.Small (Weinheim an der Bergstrasse, Germany) · 2025Article
- Layer-by-Layer siRNA Particle Assemblies for Localized Delivery of siRNA to Epithelial Cells through Surface-Mediated Particle Uptake.ACS applied bio materials · 2023Article
- Advancements in Macrophage-Targeted Drug Delivery for Effective Disease Management.International journal of nanomedicine · 2023Review
- Substrate stiffness reduces particle uptake by epithelial cells and macrophages in a size-dependent manner through mechanoregulation.Nanoscale · 2022Article
- Size-Dependent Internalization Efficiency of Macrophages from Adsorbed Nanoparticle-Based Monolayers.Nanomaterials (Basel, Switzerland) · 2021Article
- Particle Stiffness and Surface Topography Determine Macrophage-Mediated Removal of Surface Adsorbed Particles.Advanced healthcare materials · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cellular surface recognition and behavior are driven by a host of physical and chemical features which have been exploited to influence particle-cell interactions. Mechanical and topographical cues define the physical milieu which plays an important role in defining a range of cellular activities such as material recognition, adhesion, and migration through cytoskeletal organization and signaling. In order to elucidate the effect of local mechanical and topographical features generated by the adsorption of particles to an underlying surface on primary human monocyte-derived macrophages (MDM), a series of poly(N-isopropylacrylamide) (pNIPAM) particles with differing rigidity are self-assembled to form a defined particle-decorated surface. Assembly of particle-decorated surfaces is facilitated by modification of the underlying glass to possess a positive charge through functionalization using 3-aminopropyltriethoxysilane (APTES) or coating with poly(L-lysine) (PLL). MDMs are noted to preferentially remove particles with higher degrees of crosslinking (stiffer) than those with lower degrees of crosslinking (softer). Alterations to the surface density of particles enabled a greater area of the particle-decorated surface to be cleared. Uniquely, the impact of particle adsorption is evinced to have a direct impact on topographical recognition of the surface, suggesting a novel approach for controllably affecting cell-surface recognition and response.
Indexed as
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.