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Bone & Joint Research
Vol. 7, Issue 5 | Pages 362 - 372
1 May 2018
Ueda Y Inui A Mifune Y Sakata R Muto T Harada Y Takase F Kataoka T Kokubu T Kuroda R

Objectives

The aim of this study was to investigate the effect of hyperglycaemia on oxidative stress markers and inflammatory and matrix gene expression within tendons of normal and diabetic rats and to give insights into the processes involved in tendinopathy.

Methods

Using tenocytes from normal Sprague-Dawley rats, cultured both in control and high glucose conditions, reactive oxygen species (ROS) production, cell proliferation, messenger RNA (mRNA) expression of NADPH oxidase (NOX) 1 and 4, interleukin-6 (IL-6), matrix metalloproteinase (MMP)-2, tissue inhibitors of matrix metalloproteinase (TIMP)-1 and -2 and type I and III collagens were determined after 48 and 72 hours in vitro. In an in vivo study, using diabetic rats and controls, NOX1 and 4 expressions in Achilles tendon were also determined.


The Journal of Bone & Joint Surgery British Volume
Vol. 94-B, Issue 7 | Pages 865 - 874
1 Jul 2012
Mills LA Simpson AHRW

This review is aimed at clinicians appraising preclinical trauma studies and researchers investigating compromised bone healing or novel treatments for fractures. It categorises the clinical scenarios of poor healing of fractures and attempts to match them with the appropriate animal models in the literature.

We performed an extensive literature search of animal models of long bone fracture repair/nonunion and grouped the resulting studies according to the clinical scenario they were attempting to reflect; we then scrutinised them for their reliability and accuracy in reproducing that clinical scenario.

Models for normal fracture repair (primary and secondary), delayed union, nonunion (atrophic and hypertrophic), segmental defects and fractures at risk of impaired healing were identified. Their accuracy in reflecting the clinical scenario ranged greatly and the reliability of reproducing the scenario ranged from 100% to 40%.

It is vital to know the limitations and success of each model when considering its application.