Evidence map›Paper›PMID 23650478›Full record

ArticleTheranostics2013

Optimization of optical excitation of upconversion nanoparticles for rapid microscopy and deeper tissue imaging with higher quantum yield.

Qiuqiang Zhan, Sailing He, Jun Qian, Hao Cheng, Fuhong Cai

Abstract read
In one paragraph

Article in Theranostics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

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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

5 authors.

Qiuqiang ZhanZJU-SCNU Joint Research Center of Photonics, Centre for Optical and Electromagnetic Research, South China Academy of Advanced Optoelectronics, South China Normal University-SCNU, 510006 Guangzhou, P. R. China.
Sailing He
Jun Qian
Hao Cheng
Fuhong Cai

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Relatively low quantum yield (QY), time-consuming scanning and strong absorption of light in tissue are some of the issues present in the development of upconversion nanoparticles (UCNPs) for biomedical applications. In this paper we systematically optimize several aspects of optical excitation of UCNPs to improve their applicability in bioimaging and biotherapy. A novel multi-photon evanescent wave (EW) excitation modality is proposed for UCNP-based microscopy. The scanning-free, ultrahigh contrast and high spatiotemporal resolution method could simultaneously track a few particles in a large area with a speed of ³⁺up to 350 frames per second. The HeLa cancer cell membrane imaging was successfully performed using NaYF₄: 20% Yb³⁺/2Er³⁺ targeting nanoparticles. Studies with different tissues were made to illustrate the impact of optical property parameters on the deep imaging ability of 920-nm band excitation. In the experiments a semiconductor laser with a 920 nm wavelength was used to excite UCNPs in tissue phantom at five depths. Our experimental and computational results have shown that in UCNP-based diffusion optical imaging with 920-nm laser excitation could lead to larger imaging depth range compared to traditional 974-nm excitation in a wide dynamic range of tissue species. As the QY is power density dependent, a pulsed laser is proposed to improve the QY of UCNPs. This proposal is promising in drastically increasing the imaging depth and efficiency of photodynamic therapy.

Indexed as

Phantoms, ImagingQuantum TheoryCell MembraneDiagnostic ImagingHeLa CellsHumansLuminescenceMicroscopyNanoparticlesOptical ImagingSolutionsSolutionsdeep imagingpulsed excitationquantum yield.total internal reflectionupconverting

Identifiers

PMID23650478
PMCPMC3645057

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.