Evidence map›Paper›PMID 35814250›Full record

ReviewFrontiers in pharmacology2022

Recent Advances in Nanotheranostic Agents for Tumor Microenvironment-Responsive Magnetic Resonance Imaging.

Longhai Jin, Chenyi Yang, Jianqiu Wang, Jiannan Li, Nannan Xu

Abstract readReview
In one paragraph

Review in Frontiers in pharmacology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Iron Oxide Nanoparticle-Based TNanomaterials (Basel, Switzerland) · 2024
    Review
  2. 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

5 authors.

Longhai JinDepartment of Radiology, The Second Hospital of Jilin University, Changchun, China.
Chenyi YangDepartment of Blood Transfusion, Central Hospital of Changchun, Changchun, China.
Jianqiu WangDepartment of Radiology, The Second Hospital of Jilin University, Changchun, China.
Jiannan LiDepartment ofGeneral Surgery, The Second Hospital of Jilin University, Changchun, China.
Nannan XuDepartment of Radiology, The Second Hospital of Jilin University, Changchun, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Nanomaterials integrating a variety of excellent properties (such as controllable/suitable size, surface modifier, and multifunctionality) have attracted increasing attention in the biomedical field and have been considered a new generation of magnetic resonance imaging (MRI) contrast agents (CAs). In recent years, stimuli-responsive nanomaterials with specifically responsive ability have been synthesized as MRI CAs, which can significantly improve the diagnostic sensitivity and accuracy depending on their outstanding performance. Furthermore, the inherent tumor microenvironment (TME) of malignant tumor is considered to possess several unique features, such as low extracellular pH, redox condition, hypoxia, and high interstitial pressure, that are significantly different from healthy tissues. Hence, constructing nanomaterials for TME-responsive MRI as an emerging strategy is expected to overcome the current obstacles to precise diagnosis. This review focuses on recent advances of nanomaterials in their application of TME-responsive MRI that trigger the diagnostic function in response to various endogenous stimulations, including pH, redox, enzyme, and hypoxia. Moreover, the future challenges and trends in the development of nanomaterials serving as TME-responsive MRI CAs are discussed.

Indexed as

diagnosis of cancerMRInanomaterialsstimuli-responsivetumor microenvironment

Identifiers

PMID35814250
PMCPMC9257028

What OpenQuestion holds

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LicenceCC BY
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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.