Evidence map›Paper›PMID 33207082›Full record

ArticleCurrent protocols in chemical biology2020

Azide-Terminated RAFT Polymers for Biological Applications.

Ziwen Jiang, Huan He, Hongxu Liu, S Thayumanavan

Abstract read
In one paragraph

Article in Current protocols in chemical biology, 2020. 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. Article
  2. Synthesis and Structure ofMolecules (Basel, Switzerland) · 2021
    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

4 authors.

Ziwen JiangDepartment of Chemistry, University of Massachusetts Amherst, Amherst, Massachusetts.
Huan HeDepartment of Chemistry, University of Massachusetts Amherst, Amherst, Massachusetts.
Hongxu LiuDepartment of Chemistry, University of Massachusetts Amherst, Amherst, Massachusetts.
S ThayumanavanDepartment of Chemistry, University of Massachusetts Amherst, Amherst, Massachusetts.

Funding

Protein-Induced Self-Assembly and Disassembly of Nanostructures Based on OligoR35GM136395 · NIGMS · UNIVERSITY OF MASSACHUSETTS AMHERST · PI Sankaran Thayumanavan · 2020 to 2026
$3.5M
NIGMS NIH HHS R35 GM136395
6 · The paper itself

Abstract

Reversible addition-fragmentation chain-transfer (RAFT) polymerization is a commonly used polymerization methodology to generate synthetic polymers. The products of RAFT polymerization, i.e., RAFT polymers, have been widely employed in several biologically relevant areas, including drug delivery, biomedical imaging, and tissue engineering. In this article, we summarize a synthetic methodology to display an azide group at the chain end of a RAFT polymer, thus presenting a reactive site on the polymer terminus. This platform enables a click reaction between azide-terminated polymers and alkyne-containing molecules, providing a broadly applicable scaffold for chemical and bioconjugation reactions on RAFT polymers. We also highlight applications of these azide-terminated RAFT polymers in fluorophore labeling and for promoting organelle targeting capability. © 2020 Wiley Periodicals LLC. Basic Protocol 1: Synthesis of the azide derivatives of chain transfer agent and radical initiator Basic Protocol 2: Installation of an azide group on the α-end of RAFT polymers Alternate Protocol: Installation of an azide group on the ω-end of RAFT polymers Basic Protocol 3: Click reaction between azide-terminated RAFT polymers and alkyne derivatives.

Indexed as

Diagnostic ImagingDrug Delivery SystemsTissue EngineeringAzidesClick ChemistryPolymerizationPolymersAzidesPolymersclick reactionconjugationend-group modificationlabelingRAFT polymerization

Identifiers

PMID33207082
PMCPMC7685003

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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.