Experimental Animal Models in Periodontology: A Review

In periodontal research, animal studies are complementary to in vitro experiments prior to testing new treatments. Animal models should make possible the validation of hypotheses and prove the safety and efficacy of new regenerating approaches using biomaterials, growth factors or stem cells. A review of the literature was carried out by using electronic databases (PubMed, ISI Web of Science). Numerous animal models in different species such as rats, hamsters, rabbits, ferrets, canines and primates have been used for modeling human periodontal diseases and treatments. However, both the anatomy and physiopathology of animals are different from those of humans, making difficult the evaluation of new therapies. Experimental models have been developed in order to reproduce major periodontal diseases (gingivitis, periodontitis), their pathogenesis and to investigate new surgical techniques. The aim of this review is to define the most pertinent animal models for periodontal research depending on the hypothesis and expected results

Guided bone regeneration around dental implants in the atrophic alveolar ridge using a bioresorbable device - an experimental study in the monkey

Guided bone regeneration (GBR) treatment using a bioresorbable foil and an e-polytetrafluoroethylene (e-PTFE) membrane was compared. The bone healing process differed but neither developed soft tissue dehiscences. The poly(D,L-lactid-co-trimethylencarbonate) 70/30 foil used around dental implants in the atrophic alveolar ridge resulted in increased inflammatory reaction adjacent to the degradation and resorption kinetics of the foil. There was less bone fill around the foil-covered implants than when e-PTFE membrane was used. In vitro tests of bioresorbable polymers and devices may not correlate with an accelerated change in material properties, functional characteristics, and biocompatibility in the in vivo situation.</p

Effects of gamma and ethylenoxide sterilization on different bioresorbable polymers

Gas sterilization by means of ethylene oxide leaves residues in harmful quantities to bioresorbable implants. An alternative sterilization method is proposed which uses gamma irradiation. However gamma sterilization does result in a substantial increase of the degradation kinetics. This increase has to be taken in to account before selecting bioresorbable polymers for the development of implants.</p

Bioresorbable implants for the fixation of unloaded fractures in cranial-, maxillofacial-, and plastic surgery

Poly(L-lactides) (PLLA) have sufficient mechanical strength for fracture treatment in the midface, but their degradation and resorption does not allow clinical use. The degradation and resorption process elicits a foreign body reaction due to a high accumulation of degradation and resorption products. This problem can be solved by using smaller implant systems consisting of gamma-sterilized PLLA/PGA 90:10. This implant system, manufactured using the injection molding process and stabilized by welding the screw heads to the plate, shows an improved mechanical properties and uniform degradation and resorption rate with smallest gradient than the non- and ethylene oxide-sterilized specimens.</p

Computational Methods for Complex Stochastic Systems: A Review of Some Alternatives to MCMC.

We consider analysis of complex stochastic models based upon partial information. MCMC and reversible jump MCMC are often the methods of choice for such problems, but in some situations they can be difficult to implement; and suffer from problems such as poor mixing, and the difficulty of diagnosing convergence. Here we review three alternatives to MCMC methods: importance sampling, the forward-backward algorithm, and sequential Monte Carlo (SMC). We discuss how to design good proposal densities for importance sampling, show some of the range of models for which the forward-backward algorithm can be applied, and show how resampling ideas from SMC can be used to improve the efficiency of the other two methods. We demonstrate these methods on a range of examples, including estimating the transition density of a diffusion and of a discrete-state continuous-time Markov chain; inferring structure in population genetics; and segmenting genetic divergence data