Evidence map›Paper›PMID 27279907›Full record

ArticleTheranostics2016

Photodynamic Therapy Induced Enhancement of Tumor Vasculature Permeability Using an Upconversion Nanoconstruct for Improved Intratumoral Nanoparticle Delivery in Deep Tissues.

Weidong Gao, Zhaohui Wang, Liwei Lv, Deyan Yin, Dan Chen, Zhihao Han, Yi Ma, Min Zhang, Man Yang, Yueqing Gu

Open access · goldAbstract read
In one paragraph

Article in Theranostics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers.

0numbers the graph read from it
0cells of the map it votes in
50citing papers in PubMed
6.3field-weighted citation impact, top 3% of its field
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

50 citing papers in PubMed, 108 citations in OpenAlex.

  1. A Clinical Trial Using Attrition Combined with 5-Aminolevulinic Acids Based Photodynamic Therapy in Treating Squamous Cell Carcinoma.Medical science monitor : international medical journal of experimental and clinical research · 2017
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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

10 authors at 1 institution in 1 country.

Weidong GaoState Key Laboratory of Natural Medicines, Department of Biomedical Engineering, School of Engineering, China Pharmaceutical University, Nanjing, 210009, P. R. China.
Zhaohui WangState Key Laboratory of Natural Medicines, Department of Biomedical Engineering, School of Engineering, China Pharmaceutical University, Nanjing, 210009, P. R. China.
Liwei LvState Key Laboratory of Natural Medicines, Department of Biomedical Engineering, School of Engineering, China Pharmaceutical University, Nanjing, 210009, P. R. China.
Deyan YinState Key Laboratory of Natural Medicines, Department of Biomedical Engineering, School of Engineering, China Pharmaceutical University, Nanjing, 210009, P. R. China.
Dan ChenState Key Laboratory of Natural Medicines, Department of Biomedical Engineering, School of Engineering, China Pharmaceutical University, Nanjing, 210009, P. R. China.
Zhihao HanState Key Laboratory of Natural Medicines, Department of Biomedical Engineering, School of Engineering, China Pharmaceutical University, Nanjing, 210009, P. R. China.
Yi MaState Key Laboratory of Natural Medicines, Department of Biomedical Engineering, School of Engineering, China Pharmaceutical University, Nanjing, 210009, P. R. China.
Min ZhangState Key Laboratory of Natural Medicines, Department of Biomedical Engineering, School of Engineering, China Pharmaceutical University, Nanjing, 210009, P. R. China.
Man YangState Key Laboratory of Natural Medicines, Department of Biomedical Engineering, School of Engineering, China Pharmaceutical University, Nanjing, 210009, P. R. China.
Yueqing GuState Key Laboratory of Natural Medicines, Department of Biomedical Engineering, School of Engineering, China Pharmaceutical University, Nanjing, 210009, P. R. China.
China Pharmaceutical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Photodynamic therapy (PDT) has recently emerged as an approach to enhance intratumoral accumulation of nanoparticles. However, conventional PDT is greatly limited by the inability of the excitation light to sufficiently penetrate tissue, rendering PDT ineffective in the relatively deep tumors. To address this limitation, we developed a novel PDT platform and reported for the first time the effect of deep-tissue PDT on nanoparticle uptake in tumors. This platform employed c(RGDyK)-conjugated upconversion nanoparticles (UCNPs), which facilitate active targeting of the nanoconstruct to tumor vasculature and achieve the deep-tissue photosensitizer activation by NIR light irradiation. Results indicated that our PDT system efficiently enhanced intratumoral uptake of different nanoparticles in a deep-seated tumor model. The optimal light dose for deep-tissue PDT (34 mW/cm(2)) was determined and the most robust permeability enhancement was achieved by administering the nanoparticles within 15 minutes following PDT treatment. Further, a two-step treatment strategy was developed and validated featuring the capability of improving the therapeutic efficacy of Doxil while simultaneously reducing its cardiotoxicity. This two-step treatment resulted in a tumor inhibition rate of 79% compared with 56% after Doxil treatment alone. These findings provide evidence in support of the clinical application of deep-tissue PDT for enhanced nano-drug delivery.

Indexed as

AnimalsAntineoplastic AgentsBlood VesselsDisease Models, AnimalDoxorubicinHeterograftsMaleMice, NudeNanoparticlesPermeabilityPhotochemotherapyPolyethylene GlycolsProstatic NeoplasmsTreatment OutcomeAntineoplastic AgentsDoxorubicinliposomal doxorubicinPolyethylene GlycolsDrug delivery.EPRNanoparticlesPhotodynamic therapyUpconversion

Identifiers

PMID27279907
PMCPMC4893641
OpenAlexW2401465814

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.