Evidence map›Paper›PMID 33467028›Full record

ReviewPharmaceuticals (Basel, Switzerland)2021

Activatable Nanoparticles: Recent Advances in Redox-Sensitive Magnetic Resonance Contrast Agent Candidates Capable of Detecting Inflammation.

Chukwuazam Nwasike, Erin Purr, Eunsoo Yoo, Jaspreet Singh Nagi, Amber L Doiron

Abstract readReview
In one paragraph

Review in Pharmaceuticals (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
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.

Chukwuazam NwasikeDepartment of Biomedical Engineering, Binghamton University (SUNY), Binghamton, NY 13902, USA.
Erin PurrDepartment of Biomedical Engineering, Binghamton University (SUNY), Binghamton, NY 13902, USA.
Eunsoo YooDepartment of Otolaryngology-Head & Neck Surgery, University of Pennsylvania, Philadelphia, PA 19104, USA.ORCID 0000-0002-1085-8751
Jaspreet Singh NagiDepartment of Electrical and Biomedical Engineering, University of Vermont, Burlington, VT 05405, USA.
Amber L DoironDepartment of Electrical and Biomedical Engineering, University of Vermont, Burlington, VT 05405, USA.ORCID 0000-0002-6963-0989

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The emergence of activatable magnetic resonance (MR) contrast agents has prompted significant interest in the detection of functional markers of diseases, resulting in the creation of a plethora of nanoprobes capable of detecting these biomarkers. These markers are commonly dysregulated in several chronic diseases, specifically select cancers and inflammatory diseases. Recently, the development of redox-sensitive nanoparticle-based contrast agents has gained momentum given advances in medicine linking several inflammatory diseases to redox imbalance. Researchers have pinpointed redox dysregulation as an opportunity to use activatable MR contrast agents to detect and stage several diseases as well as monitor the treatment of inflammatory diseases or conditions. These new classes of agents represent an advancement in the field of MR imaging as they elicit a response to stimuli, creating contrast while providing evidence of biomarker changes and commensurate disease state. Most redox-sensitive nanoparticle-based contrast agents are sensitive to reductive glutathione or oxidative reactive oxygen species. In this review, we will explore recent investigations into redox-activatable, nanoparticle-based MR contrast agent candidates.

Indexed as

MR contrast agentsredox-activatablerelaxationresponsive

Identifiers

PMID33467028
PMCPMC7829999

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.