The Effect of EDTA in Attachment Gain and Root Coverage

Root surface biomodification using low pH agents such as citric acid and tetracycline has been proposed to enhance root coverage following connective tissue grafting. The authors hypothesized that root conditioning with neutral pH edetic acid would improve vertical recession depth, root surface coverage, pocket depth, and clinical attachment levels. Twenty teeth in 10 patients with Miller class I and II recession were treated with connective tissue grafting. The experimental sites received 24% edetic acid in sterile distilled water applied to the root surface for 2 minutes before grafting. Controls were pretreated with only sterile distilled water. Measurements were evaluated before surgery and 6 months after surgery. Analysis of variance was used to determine differences between experimental and control groups. We found significant postoperative improvements in vertical recession depth, root surface coverage, and clinical attachment levels in test and control groups, compared to postoperative data. Pocket depth differences were not significant (P\u3c.01)

Decentralised Clinical Guidelines Modelling with Lightweight Coordination Calculus

Background: Clinical protocols and guidelines have been considered as a major means to ensure that cost-effective services are provided at the point of care. Recently, the computerisation of clinical guidelines has attracted extensive research interest. Many languages and frameworks have been developed. Thus far, however,an enactment mechanism to facilitate decentralised guideline execution has been a largely neglected line of research. It is our contention that decentralisation is essential to maintain a high-performance system in pervasive health care scenarios. In this paper, we propose the use of Lightweight Coordination Calculus (LCC) as a feasible solution. LCC is a light-weight and executable process calculus that has been used successfully in multi-agent systems, peer-to-peer (p2p) computer networks, etc. In light of an envisaged pervasive health care scenario, LCC, which represents clinical protocols and guidelines as message-based interaction models, allows information exchange among software agents distributed across different departments and/or hospitals. Results: We outlined the syntax and semantics of LCC; proposed a list of refined criteria against which the appropriateness of candidate clinical guideline modelling languages are evaluated; and presented two LCC interaction models of real life clinical guidelines. Conclusions: We demonstrated that LCC is particularly useful in modelling clinical guidelines. It specifies the exact partition of a workflow of events or tasks that should be observed by multiple "players" as well as the interactions among these "players". LCC presents the strength of both process calculi and Horn clauses pair of which can provide a close resemblance of logic programming and the flexibility of practical implementation

Evaluation of the usefulness of a computer‐based learning program to support student learning in pharmacology

This study aims to evaluate the effectiveness of a computer‐based teaching program in supporting and enhancing traditional teaching methods. The program covers the pharmacology of inflammation and has been evaluated with a group of second‐year medical students at a UK university. The study assessed subject‐specific knowledge using a pre‐ and post‐test and surveyed, by questionnaire, students’ perceptions of the usefulness of the program to support learning before and after use. The use of computers for learning amongst this cohort of students was widespread. The results demonstrated an increase in students ‘ knowledge of the pharmacology of inflammation, coupled with a positive attitude towards the CBL program they had used and the advantages that this mode of study may provide in enabling students to manage their own learning. However, students did not feel that the program could substitute for traditional teaching (lectures)

Modelling Emotion Based Reward Valuation with Computational Reinforcement Learning

We show that computational reinforcement learning can model human decision making in the Iowa Gambling Task (IGT). The IGT is a card game, which tests decision making under uncertainty. In our experiments, we found that modulating learning rate decay in Q-learning, enables the approximation of both the behaviour of normal subjects and those who are emotionally impaired by ventromedial prefrontal lesions. Outcomes observed in impaired subjects are modeled by high learning rate decay, while low learning rate decay replicates healthy subjects under otherwise identical conditions. The ventromedial prefrontal cortex has been associated with emotion based reward valuation, and, the value function in reinforcement learning provides an analogous assessment mechanism. Thus reinforcement learning can provide a good model for the role of emotional reward as a modulator of the learning rate

Effectiveness and Content Analysis of Interventions to Enhance Oral Antidiabetic Drug Adherence in Adults with Type 2 Diabetes : Systematic Review and Meta-Analysis

We thank Frederic Bergeron, information scientist, for assistance in search strategies. We thank American Journal Experts for editing the text. Source of financial support: This study was funded by the Laval University Chair on Adherence to Treatments. This Chair is supported by nonrestricted grants from AstraZeneca Canada, Merck Canada, Sanofi Canada, and Pfizer Canada and from the Prends soin de toi program (a Quebec provincial program for the improvement of public health).Peer reviewedPostprin