Evidence map›Paper›PMID 32645887›Full record

ArticleDentistry journal2020

Implant Soft-Tissue Attachment Using 3D Oral Mucosal Models-A Pilot Study.

Emilia Barker, Lina AlQobaly, Zahab Shaikh, Kirsty Franklin, Keyvan Moharamzadeh

Abstract read
In one paragraph

Article in Dentistry journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 citing papers in PubMed.

  1. Review
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  11. PreclinicalFrontiers in bioengineering and biotechnology · 2023
    Review
  12. Article
  13. AnAnnals of African medicine
    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.

Emilia BarkerSchool of Clinical Dentistry, University of Sheffield, Western Bank, Sheffield S10 2TN, UK.
Lina AlQobalySchool of Clinical Dentistry, University of Sheffield, Western Bank, Sheffield S10 2TN, UK.ORCID 0000-0002-3348-7543
Zahab ShaikhSchool of Clinical Dentistry, University of Sheffield, Western Bank, Sheffield S10 2TN, UK.
Kirsty FranklinSchool of Clinical Dentistry, University of Sheffield, Western Bank, Sheffield S10 2TN, UK.
Keyvan MoharamzadehSchool of Clinical Dentistry, University of Sheffield, Western Bank, Sheffield S10 2TN, UK.ORCID 0000-0002-8842-4638

Funding

Institut Straumann AG R/162799
6 · The paper itself

Abstract

purposeThe aim of this study was to investigate soft-tissue attachment to different metal, ceramic, and polymer implant surfaces using an inflamed, three-dimensional (3D), tissue-engineered, human oral mucosal model, as well as multiple-endpoint qualitative and quantitative biological approaches.

methodsNormal human oral fibroblasts, OKF6/TERT-2 keratinocytes and THP-1 monocytes were cultured, and full-thickness, 3D oral mucosal models were engineered inside tissue culture inserts. Sand-blasted and acid-etched (SLA) and machined (M) titanium-zirconium alloy (TiZr; commercially known as Roxolid; Institut Straumann AG, Switzerland), ceramic (ZrO

resultsInflamed, full-thickness, 3D human oral mucosal models with inserted implants were successfully engineered and histologically characterized. The implant pull-test with PrestoBlue assay showed higher viability of the tissue that remained attached to the TiZr-SLA surface compared to the other test groups. This difference was statistically significant (

conclusionsThe inflamed, 3D, oral mucosal model has the potential to be used as a suitable in vitro test system for visualization and quantification of implant soft-tissue attachment. The results of our study indicate greater soft tissue attachment to TiZr-SLA compared to TiZr-M, ceramic, and PEEK surfaces.

Indexed as

implantoral mucosasoft tissue attachmenttissue engineeringtitaniumzirconia

Identifiers

PMID32645887
PMCPMC7558259

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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.