Evidence map›Paper›PMID 32601508›Full record

ArticleMacromolecules2020

Cytoskeleton-inspired artificial protein design to enhance polymer network elasticity.

David S Knoff, Haley Szczublewski, Dallas Altamirano, Kareen A Fajardo Cortes, Minkyu Kim

Open access · greenAbstract read
In one paragraph

Article in Macromolecules, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.6field-weighted citation impact, top 43% of its field
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

4 citing papers in PubMed, 11 citations in OpenAlex.

  1. Article
  2. Review
  3. Control of NanoscaleACS nano · 2021
    Article
  4. 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 at 1 institution in 1 country.

David S KnoffDepartment of Biomedical Engineering, University of Arizona, Tucson, AZ 85721.
Haley SzczublewskiDepartment of Biomedical Engineering, University of Arizona, Tucson, AZ 85721.
Dallas AltamiranoDepartment of Biomedical Engineering, University of Arizona, Tucson, AZ 85721.
Kareen A Fajardo CortesDepartment of Biomedical Engineering, University of Arizona, Tucson, AZ 85721.
Minkyu KimDepartment of Biomedical Engineering, University of Arizona, Tucson, AZ 85721.
University of Arizona · US

Funding

Cardiovascular Biomedical Engineering Training GrantT32HL007955 · NHLBI · UNIVERSITY OF ARIZONA · PI BARTON, JENNIFER KEHLET · 2001 to 2020
$5.1M
NHLBI NIH HHS T32 HL007955
6 · The paper itself

Abstract

Reducing topological network defects to enhance elasticity in polymeric materials remains a grand challenge. Efforts to control network topology, primarily focused on crosslinking junctions, continue to underperform compared to theoretical estimations from idealized networks using affine and phantom network theories. Here, artificial protein technology was adapted for the design of polymer-network hydrogels with precisely defined coil-like and rod-like strands to observe the impact of strand rigidity on the mechanical properties of polymeric materials. Cytoskeleton-inspired polymer-network hydrogels incorporated with rod-like protein strands nearly tripled the gel shear elastic modulus and relaxation time compared to coil-like protein strands, indicating an enhanced effective crosslinking density. Furthermore, asymmetric rod-coil protein designs in network strands with an optimal rod:coil ratio improved the hydrogel relaxation time, enhancing the stability of physical macromolecular associations by modulating crosslinker mobility. The careful design of strand rigidity presents a new direction to reduce topological defects for optimizing polymeric materials.

Identifiers

PMID32601508
PMCPMC7323958
OpenAlexW3021090048

What OpenQuestion holds

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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.