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Principal component analysis: A novel analysis to evaluate the characteristics of osseointegration of different implant surfaces

Journal article
Authors Ryo Jimbo
Julia Fernandez-Rodriguez
Young-Taeg Sul
Carina B. Johansson
Published in Implant Dentistry
Volume 20
Issue 5
Pages 364-368
ISSN 1056-6163
Publication year 2011
Published at Institute of Odontology
Institute of Clinical Sciences, Department of Biomaterials
Core Facilities, Centre for Cellular Imaging
Pages 364-368
Language en
Links dx.doi.org/10.1097/ID.0b013e31822b9...
Subject categories Biological Sciences, Biomaterials

Abstract

Objective: To apply a new statistical method (principle component analysis; PCA) to evaluate osseointegration. Materials and Methods: Two different commercially available implants were selected for the study. Twenty implants, 10 of each type, were placed in the rabbit tibiae (n = 10). The fluorochromes (FLCs) alizarin complexone and calcein green were administered after 20 days and 4 days before sacrifice for labeling. On the day of implantation and retrieval (6 weeks), implant stability was measured with a resonance frequency analyzer (RFA). The retrieved samples were ground sectioned for histomorphometric and FLC quantification. The collected data were analyzed by a PCA software program (Qlucore Omics Explorer, Lund, Sweden) to explore and determine the correlation between different study variables and to analyze the differences between different implants. Results: The RFA presented no significant differences at either time point. The bone-to-implant contact was significantly higher for the TiUnite (NobelBiocare, Gothenburg, Sweden); however, the bone area and FLC quantification showed higher values for the Osseotite (3i Implant Innovation, FL). Consistent with these results, the PCA indicated a strong correlation between TiUnite and high bone-to-implant contact values and between Osseotite and high bone area and FLC values. No correlation between RFA and the biological responses were found. Conclusion: The application of the PCA analysis may help interpret and correlate results obtained from numerous evaluations.

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