Short-term "in vivo" study on cellular DNA damage induced by acrylic Andresen activator in oral mucosa cells.

OBJECTIVES: To analyse through comet assay and micronucleus test the viability and DNA damage occurred in buccal mucosa epithelial cells after a short-term exposure to Andresen activator resin monomers. SETTING AND SAMPLE POPULATION: Test group consisting of 26 subjects was treated with Andresen activator; 16 subjects who had never undergone orthodontic treatment were enrolled in the control group. MATERIAL & METHODS: Buccal mucosa samples were collected before treatment and after 7, 15, 30, 60 and 90 days. The analyses performed on the cells included the following: cellular viability, comet assay and micronucleus test. Mean ± SD were calculated for cellular viability, tail moment, tail intensity, tail length, micronuclei, binuclear and bud cells. Significance (P < 0.05) was evaluated with Dunnett's test. RESULTS: Cellular viability did not change during observational time, and its trend was similar to the controls. Tail moment and tail intensity significantly increased after 30 and 60 days, respectively, whereas tail length remained unchanged over time in the test group; the same parameters did not change in the control group. In the test group, micronuclei, binuclear and bud cells significantly increased after 30, 60 and 90 days, respectively. CONCLUSION: The resin monomers of the Andresen activator cause genotoxic effects detectable through comet assay and micronucleus test, but they do not produce clear cytotoxic effects after a 90 days exposure.

Syndromes associated with dental agenesis.

INTRODUCTION: Dental agenesis is the congenital absence of a variable number of teeth due to the lack of formation of the corresponding tooth germ. The aim of this work was to investigate the syndromic conditions characterized by dental agenesis. EVIDENCE ACQUISITION: Based on the research conducted through the OMIM® (Online Mendelian Inheritance in Man) and PubMed online databases, more than ninety syndromes associated with severe or moderate agenesis have been found. EVIDENCE SYNTHESIS: The main clinical features of these syndromes are described, especially those concerning the stomatognathic apparatus, referring to the most recent literature. Among these syndromes there are three clinical conditions associated with dental agenesis that are common for the clinician: Down Syndrome, ectodermal dysplasia and labio-palatal cleft. CONCLUSIONS: It must be kept in mind that the success of the treatment of these patients is based on the compliance of the patient as well as on the collaboration among specialists.

Biomechanics-machine learning system for surgical gesture analysis and development of technologies for minimal access surgery.

BACKGROUND: The uptake of minimal access surgery (MAS) has by virtue of its clinical benefits become widespread across the surgical specialties. However, despite its advantages in reducing traumatic insult to the patient, it imposes significant ergonomic restriction on the operating surgeons who require training for the safe execution. Recent progress in manipulator technologies (robotic or mechanical) have certainly reduced the level of difficulty, however it requires information for a complete gesture analysis of surgical performance. This article reports on the development and evaluation of such a system capable of full biomechanical and machine learning. METHODS: The system for gesture analysis comprises 5 principal modules, which permit synchronous acquisition of multimodal surgical gesture signals from different sources and settings. The acquired signals are used to perform a biomechanical analysis for investigation of kinematics, dynamics, and muscle parameters of surgical gestures and a machine learning model for segmentation and recognition of principal phases of surgical gesture. RESULTS: The biomechanical system is able to estimate the level of expertise of subjects and the ergonomics in using different instruments. The machine learning approach is able to ascertain the level of expertise of subjects and has the potential for automatic recognition of surgical gesture for surgeon-robot interactions. CONCLUSIONS: Preliminary tests have confirmed the efficacy of the system for surgical gesture analysis, providing an objective evaluation of progress during training of surgeons in their acquisition of proficiency in MAS approach and highlighting useful information for the design and evaluation of master-slave manipulator systems.

Proficiency assessment of gesture analysis in laparoscopy by means of the surgeon's musculo-skeleton model.

OBJECTIVE: This article presents the implementation of surgeon's musculo-skeletal model for gesture analysis in laparoscopy, thereby providing a complete account of the objective metrics needed to evaluate surgical performance and to improve the design of new surgical instruments including robotic instrumentation for surgical procedures. BACKGROUND: Previous published work has been based exclusively on the kinematics involved whereas, this study is focused on the dynamics and muscle contraction analysis to assess loads on bones and muscle fatigue during simulation of surgical interventions. METHODS: Nine medical students and 2 fully trained surgeons participated in the experimental sessions using a virtual laparoscopic simulator. Movement was acquired by means of an Optical Localization System and processed by means of the biomechanical software platform ADAMS-LifeMOD. RESULTS: The musculo-skeletal analysis allows calculation of how the muscles are used and their respective mean work during the exercises. Results, relative to biceps and trapezius for left and right arm, clearly demonstrate different proficiencies between surgeons and medical students and highlight differences in using different surgical instruments and assumption of different postures. CONCLUSIONS: The model provides data on the evaluation of biomechanical parameters of surgical gesture not only in kinematic terms but also includes analysis of the dynamics of muscle contraction analysis during surgical manipulations.

Modelling and evaluation of surgical performance using hidden Markov models.

Minimally invasive surgery has become very widespread in the last ten years. Since surgeons experience difficulties in learning and mastering minimally invasive techniques, the development of training methods is of great importance. While the introduction of virtual reality-based simulators has introduced a new paradigm in surgical training, skill evaluation methods are far from being objective. This paper proposes a method for defining a model of surgical expertise and an objective metric to evaluate performance in laparoscopic surgery. Our approach is based on the processing of kinematic data describing movements of surgical instruments. We use hidden Markov model theory to define an expert model that describes expert surgical gesture. The model is trained on kinematic data related to exercises performed on a surgical simulator by experienced surgeons. Subsequently, we use this expert model as a reference model in the definition of an objective metric to evaluate performance of surgeons with different abilities. Preliminary results show that, using different topologies for the expert model, the method can be efficiently used both for the discrimination between experienced and novice surgeons, and for the quantitative assessment of surgical ability.

A biomechanical analysis of surgeon's gesture in a laparoscopic virtual scenario.

Minimally invasive surgery (MIS) has become very common in recent years thanks to many advantages that patients can get. However, due to the difficulties surgeons encounter to learn and manage this technique, several training methods and metrics have been proposed in order to, respectively, improve surgeon's abilities and assess his/her surgical skills. In this context, this paper presents a biomechanical analysis method of the surgeon's movements, during exercise involving instrument tip positioning and depth perception in a laparoscopic virtual environment. Estimation of some biomechanical parameters enables us to assess the abilities of surgeons and to distinguish an expert surgeon from a novice. A segmentation algorithm has been defined to deeply investigate the surgeon's movements and to divide them into many sub-movements.