Evidence map›Paper›PMID 32395984›Full record

ArticleBiomacromolecules2020

PCL-Based Shape Memory Polymer Semi-IPNs: The Role of Miscibility in Tuning the Degradation Rate.

Michaela R Pfau, Kelly G McKinzey, Abigail A Roth, Melissa A Grunlan

Abstract read
In one paragraph

Article in Biomacromolecules, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 citing papers in PubMed.

  1. Article
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  4. Article
  5. Article
  6. Review
  7. Shape Memory Polymer Scaffolds-Utility for In Vitro Osteogenesis of Canine Multipotent Stromal Cells.Journal of biomedical materials research. Part B, Applied biomaterials · 2024
    Article
  8. Article
  9. Review
  10. Article
  11. Article
  12. Article
  13. Intrinsic osteoinductivity of PCL-DA/PLLA semi-IPN shape memory polymer scaffolds.Journal of biomedical materials research. Part A · 2021
    Article
  14. Review
  15. Article
  16. 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.

Michaela R PfauDepartment of Biomedical Engineering, Texas A&M University, College Station Texas 77843, United States.
Kelly G McKinzeyDepartment of Biomedical Engineering, Texas A&M University, College Station Texas 77843, United States.
Abigail A RothDepartment of Biomedical Engineering, Texas A&M University, College Station Texas 77843, United States.
Melissa A GrunlanDepartment of Biomedical Engineering, Texas A&M University, College Station Texas 77843, United States.ORCID 0000-0002-5428-0461

Funding

Bioactive, "Self-fitting" Shape Memory Polymer (SMP) Scaffolds to Treat Cranial Bone DefectsR01DE025886 · NIDCR · TEXAS ENGINEERING EXPERIMENT STATION · PI GRUNLAN, MELISSA · 2017 to 2020
$1.8M
A Self-Cleaning Membrane to Extend the Lifetime of an Implanted Glucose BiosensorR01DK095101 · NIDDK · TEXAS ENGINEERING EXPERIMENT STATION · PI GRUNLAN, MELISSA · 2012 to 2016
$1.6M
NIDCR NIH HHS R01 DE025886NIDDK NIH HHS R01 DK095101
6 · The paper itself

Abstract

The utility of poly(ε-caprolactone) (PCL) as a shape memory polymer (SMP) may be improved by accelerating its degradation. Recently, we have reported novel semi-interpenetrating networks (semi-IPNs) composed of cross-linked PCL diacrylate (PCL-DA) and thermoplastic poly(l-lactic acid) (PLLA) that exhibited SMP behavior, accelerated degradation, and enhanced moduli versus the PCL-DA control. Herein, we systematically varied the thermoplastic component of the PCL-based semi-IPNs, incorporating homo- and copolymers based on lactic acid of different

Indexed as

Smart MaterialsHydrophobic and Hydrophilic InteractionsPolyestersPolymersPolyestersPolymersSmart Materials

Identifiers

PMID32395984
PMCPMC8742658

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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.