Aim: Mucosal fenestrations affecting permanent teeth are clinically challenging because they require a more complex approach. The objective of this case report was to describe a treatment modality used to manage an apical fenestration placed on the left mandibular central incisor root.Case Report: The case report describes the management of a patient with mucosal fenestration of root apex. Mucosal fenestration of root apex was treated by a combination of root canal treatment and surgery. Root-end resection was performed to bring the root apex within the alveolus before root-end filling and packing of the bony defects with platelet rich fibrin. The dehiscence of the buccal labial plate was managed by placement of a barrier membrane. The edges of the soft tissue defect was then deepithelialized, approximated and sutured.Discussion: Various treatment modalities advocated in the literature for the management of mucosal fenestration include-root canal treatment and root-end resection, blind root surface instrumentation and mouth rinsing with chlorhexidine, full thickness mucogingival flap with primary or secondary healing, pedicle flap operations, epithelialized and non-epithelialized grafting procedures for root coverage and full thickness mucogingival flaps with guided tissue regeneration and bone grafting.
Conclusion:The endodontic and periodontal surgical techniques used in the management of alveolar or mucosal fenestrations alone are unremarkable but combining them can give optimum outcome.
Smartphone’s are programmable and embed various sensors; these phones have the potential to change the way how healthcare is delivered. Fall detection is definitely one of the possibilities. Injuries due to falls are dangerous, especially for elderly people, diminishing the quality of life or even resulting in death. This study presents the implementation of a fall detection prototype for the Android-based platform. The proposed system has three components: sensing the accelerometer data from the mobile embedded sensors, learning the relationship between the fall behavior and the collected data, and alerting preconfigured contacts through message while detecting fall. We adopt different fall detection algorithms and conduct various experiments to evaluate performance. The results show that the proposed system can recognize the fall from human activities, such as sitting, walking and standing, with 72.22% sensitivity and 73.78% specificity. The experiment also investigates the impact of different locations where the phone attached. In addition, this study further analyzes the trade-off between sensitivity and specificity and discusses the additional powers consumption of the devices.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.