Evidence map›Paper›PMID 35711646›Full record

ReviewFrontiers in bioengineering and biotechnology2022

Progress of Nanomaterials in Photodynamic Therapy Against Tumor.

Lei Chen, Jiahui Huang, Xiaotong Li, Miaoting Huang, Shaoting Zeng, Jiayi Zheng, Shuyi Peng, Shiying Li

Abstract readReview
In one paragraph

Review in Frontiers in bioengineering and biotechnology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing 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

19 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

8 authors.

Lei ChenDepartment of Anesthesiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
Jiahui HuangDepartment of Anesthesiology, Huizhou Central People's Hospital, Huizhou, China.
Xiaotong LiGuangzhou Medical University, Guangzhou, China.
Miaoting HuangGuangzhou Medical University, Guangzhou, China.
Shaoting ZengGuangzhou Medical University, Guangzhou, China.
Jiayi ZhengGuangzhou Medical University, Guangzhou, China.
Shuyi PengGuangzhou Medical University, Guangzhou, China.
Shiying LiKey Laboratory of Molecular Target and Clinical Pharmacology and The State Key Laboratory of Respiratory Disease, School of Pharmaceutical Sciences and The Fifth Affiliated Hospital, Guangzhou Medical University, Guangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Photodynamic therapy (PDT) is an advanced therapeutic strategy with light-triggered, minimally invasive, high spatiotemporal selective and low systemic toxicity properties, which has been widely used in the clinical treatment of many solid tumors in recent years. Any strategies that improve the three elements of PDT (light, oxygen, and photosensitizers) can improve the efficacy of PDT. However, traditional PDT is confronted some challenges of poor solubility of photosensitizers and tumor suppressive microenvironment. To overcome the related obstacles of PDT, various strategies have been investigated in terms of improving photosensitizers (PSs) delivery, penetration of excitation light sources, and hypoxic tumor microenvironment. In addition, compared with a single treatment mode, the synergistic treatment of multiple treatment modalities such as photothermal therapy, chemotherapy, and radiation therapy can improve the efficacy of PDT. This review summarizes recent advances in nanomaterials, including metal nanoparticles, liposomes, hydrogels and polymers, to enhance the efficiency of PDT against malignant tumor.

Indexed as

nanomaterialsphotodynamic therapyphotosensitizerstumor microenvironmenttumor-targeting

Identifiers

PMID35711646
PMCPMC9194820

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.