Evidence map›Paper›PMID 30464993›Full record

ArticleNano-micro letters2016

Size-Dependent Gold Nanoparticle Interaction at Nano-Micro Interface Using Both Monolayer and Multilayer (Tissue-Like) Cell Models.

Darren Yohan, Charmainne Cruje, Xiaofeng Lu, Devika B Chithrani

Abstract read
In one paragraph

Article in Nano-micro letters, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing 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

24 citing papers in PubMed.

  1. Review
  2. How Key in vivo Models Can Advance Anticancer Nanotherapeutics.International journal of nanomedicine · 2026
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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

4 authors.

Darren Yohan1Department of Physics, Ryerson University, 350 Victoria Street, Toronto, ON M5B 2K3 Canada.
Charmainne Cruje1Department of Physics, Ryerson University, 350 Victoria Street, Toronto, ON M5B 2K3 Canada.
Xiaofeng Lu2Keenan Research Centre, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, ON Canada.
Devika B Chithrani1Department of Physics, Ryerson University, 350 Victoria Street, Toronto, ON M5B 2K3 Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Gold nanoparticles (GNPs) are emerging as a novel tool to improve existing cancer therapeutics. GNPs are being used as radiation dose enhancers in radiation therapy as well as anticancer drugs carriers in chemotherapy. However, the success of GNP-based therapeutics depends on their ability to penetrate tumor tissue. GNPs of 20 and 50 nm diameters were used to elucidate the effects of size on the GNP interaction with tumor cells at monolayer and multilayer level. At monolayer cell level, smaller NPs had a lower uptake compared to larger NPs at monolayer cell level. However, the order was reversed at tissue-like multilayer level. The smaller NPs penetrated better compared to larger NPs in tissue-like materials. Based on our study using tissue-like materials, we can predict that the smaller NPs are better for future therapeutics due to their greater penetration in tumor tissue once leaving the leaky blood vessels. In this study, tissue-like multilayer cellular structures (MLCs) were grown to model the post-vascular tumor environment. The MLCs exhibited a much more extensive extracellular matrix than monolayer cell cultures. The MLC model can be used to optimize the nano-micro interface at tissue level before moving into animal models. This would accelerate the use of NPs in future cancer therapeutics.

Indexed as

Gold nanoparticlesHyper spectral imagingMultilayer cellular structuresSize dependenceTissue penetration

Identifiers

PMID30464993
PMCPMC6223926

What OpenQuestion holds

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Registered trials

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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.