Evidence map›Paper›PMID 36771027›Full record

ReviewMolecules (Basel, Switzerland)2023

Porous Framework Materials for Bioimaging and Cancer Therapy.

Meng Jin, Yingying Zhao, Zong-Jie Guan, Yu Fang

Abstract readReview
In one paragraph

Review in Molecules (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Review
  5. Review
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.

Meng JinState Key Laboratory for Chemo/Bio-Sensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, China.
Yingying ZhaoState Key Laboratory for Chemo/Bio-Sensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, China.
Zong-Jie GuanState Key Laboratory for Chemo/Bio-Sensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, China.
Yu FangState Key Laboratory for Chemo/Bio-Sensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, China.

Funding

Hunan Provincial Science & Technology Department 2020RC3020Hunan Provincial Science & Technology Department 2021JJ20021National Natural Science Foundation of China 21501133
6 · The paper itself

Abstract

Cancer remains one of the most pressing diseases in the world. Traditional treatments, including surgery, chemotherapy, and radiotherapy still show certain limitations. Recently, numerous cancer treatments have been proposed in combination with novel materials, such as photothermal therapy, chemodynamic therapy, immunotherapy, and a combination of therapeutic approaches. These new methods have shown significant advantages in reducing side effects and synergistically enhancing anti-cancer efficacy. In addition to the above approaches, early diagnosis and in situ monitoring of lesion areas are also important for reducing side effects and improving the success rate of cancer therapy. This depends on the decent use of bioimaging technology. In this review, we mainly summarize the recent advances in porous framework materials for bioimaging and cancer therapy. In addition, we present future challenges relating to bioimaging and cancer therapy based on porous framework materials.

Indexed as

NeoplasmsPhotochemotherapyCombined Modality TherapyHumansPhototherapyPorositybioimagingcancerchemodynamic therapyphotodynamic therapyphotothermal therapyporous framework materials

Identifiers

PMID36771027
PMCPMC9921779

What OpenQuestion holds

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LicenceCC BY
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.