Surgical treatment of zygomatic bone fracture using two points fixation versus three point fixation-a randomised prospective clinical trial

<p>Abstract</p> <p>Background</p> <p>The zygoma plays an important role in the facial contour for both cosmetic and functional reasons; therefore zygomatic bone injuries should be properly diagnosed and adequately treated. Comparison of various surgical approaches and their complications can only be done objectively using outcome measurements which in turn require protocol management and long-term follow up. The preference for open reduction and internal fixation of zygomatic fractures at three points has continued to grow in response to observations of inadequate results from two point and one point fixation techniques.</p> <p>The objectives of this study were to compare the efficacy of zygomatic bone after treatment with ORIF using 2 point fixation and ORIF using 3 point fixation and compare the outcome of two procedures.</p> <p>Methods</p> <p>100 patients were randomly divided equally into two groups. In group A, 50 patients were treated by ORIF using two point fixation by miniplates and in group B, 50 patients were treated by ORIF using three point fixation by miniplates. They were evaluated for their complications during and after surgery with their advantages and disadvantages and the difference between the two groups was observed.</p> <p>Results</p> <p>A total of 100 fractures were sustained. We found that postoperative complication like decreased malar height and vertical dystopia was more common in those patients who were treated by two point fixation than those who were treated with three point fixation.</p> <p>Conclusions</p> <p>Based on this study open reduction and internal fixation using three point fixation by miniplates is the best available method for the treatment zygomatic bone fractures.</p

Priorities for synthesis research in ecology and environmental science

ACKNOWLEDGMENTS We thank the National Science Foundation grant #1940692 for financial support for this workshop, and the National Center for Ecological Analysis and Synthesis (NCEAS) and its staff for logistical support.Peer reviewedPublisher PD

Priorities for synthesis research in ecology and environmental science

ACKNOWLEDGMENTS We thank the National Science Foundation grant #1940692 for financial support for this workshop, and the National Center for Ecological Analysis and Synthesis (NCEAS) and its staff for logistical support.Peer reviewedPublisher PD

Improving inertial navigation systems with pedestrian locomotion classifiers

Researches on inertial navigation systems (INS) have formulated complex step detection algorithms and stride length estimations. But for current systems to work, INSs have to correctly identify negative pedestrian locomotion. Negative pedestrian locomotion are movements that a user can naturally make without any real position displacement, but has sensor signals that might be misidentified as steps. As the INS\u27s modules have a cascading nature, it is important that these false movements are identified beforehand. This research aims to provide a solution by studying patterns exhibited by positive and negative pedestrian locomotion when sensors are placed on a user\u27s front pocket. A model was then built to classify negative from positive pedestrian locomotion, and to improve the INS\u27s accuracy overall

Using machine learning to detect pedestrian locomotion from sensor-based data

The integration of low cost microelectromagnetic (MEM) sensors into smart phones have made inertial navigation systems (INS) possible for ubiquitous use. Many research studies developed algorithms to detect a user\u27s steps, and to calculate a user\u27s stride to know the position displacement of the user. Subsequent research have already integrated the phone\u27s heading to map out the user\u27s movement across a physical area. These research, however, have not taken into account negative pedestrian locomotion, wherein the user is moving but is not exhibiting any position displacement. Current INSs are not suited to handle negative pedestrian locomotion movements, and this leads them to consider false steps as real steps. As the INS\u27s modules depend heavily on the outputs of the other modules, a cascading error would most likely occur. This research aims to solve this problem by collecting positive and negative pedestrian locomotion with data from phone-embedded sensors positioned in the research subject\u27s front pocket. Using these data, a model will be built to classify negative pedestrian locomotion from positive ones, and to eventually improve the INS\u27s accuracy overall