Introduction: Premolars are the teeth most often extracted to provide space to correct crowding, excessive dental and/or labial protrusion, and to compensate for the sagittal discrepancy. After treatment, the extraction spaces have to remain closed. Nevertheless, several studies have shown a tendency for some relapse even in patients finished with an adequate occlusion. Thus, the objective of this study was to compare the stability of extraction space closure of the first and second premolars.
Methods: Dental casts of 72 patients were digitized using a 3-dimensional scanner (R700; 3Shape, Copenhagen, Denmark) and divided into 2 groups. Group 1 (29 patients; mean age, 13.79 years; 4.57 years after treatment; 116 extraction spaces) was treated with first premolar extractions, and group 2 (43 patients; mean age, 15.20 years; 3.97 years after treatment; 100 extraction spaces) was treated with second premolar extractions. Chi-square tests were used to compare the numbers of open and closed extraction spaces after treatment and at the long-term posttreatment stage. t Tests were used to compare the number of spaces posttreatment and at the long-term posttreatment stages. These tests were also performed in subgroups with completely closed extraction sites posttreatment.
Results: The groups showed similar numbers of extraction sites reopening. The first and second premolar extraction space closure presents a similar tendency for reopening. Considering only the patients that showed completely closed extraction spaces in the final dental models, maxillary extraction space reopening was larger in the first premolar extraction group.
Conclusions: First and second premolar extraction space closure present similar stability.
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http://dx.doi.org/10.1016/j.ajodo.2021.04.027 | DOI Listing |
Cogn Neurodyn
December 2025
Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai, TamilNadu India.
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January 2025
School of Computer Science and Technology, Henan Institute of Science and Technology, Xinxiang, China.
Introduction: With the advent of technologies such as deep learning in agriculture, a novel approach to classifying wheat seed varieties has emerged. However, some existing deep learning models encounter challenges, including long processing times, high computational demands, and low classification accuracy when analyzing wheat seed images, which can hinder their ability to meet real-time requirements.
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MethodsX
June 2025
Department of Networking & Communications, School of Computing, SRM Institute of Science and Technology, Kattankulathur, Chennai, India.
Forecasting student performance with precision in the educational space is paramount for creating tailor-made interventions capable to boost learning effectiveness. It means most of the traditional student performance prediction models have difficulty in dealing with multi-dimensional academic data, can cause sub-optimal classification and generate a simple generalized insight. To address these challenges of the existing system, in this research we propose a new model Multi-dimensional Student Performance Prediction Model (MSPP) that is inspired by advanced data preprocessing and feature engineering techniques using deep learning.
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January 2025
Cancer Prevention, Survivorship and Care Delivery (CPSCD) Research Program, Mayo Clinic Comprehensive Cancer Center, Jacksonville, FL, USA.
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View Article and Find Full Text PDFProtein Sci
February 2025
Department of Physics, University of Washington, Seattle, Washington, USA.
Proteins' flexibility is a feature in communicating changes in cell signaling instigated by binding with secondary messengers, such as calcium ions, associated with the coordination of muscle contraction, neurotransmitter release, and gene expression. When binding with the disordered parts of a protein, calcium ions must balance their charge states with the shape of calcium-binding proteins and their versatile pool of partners depending on the circumstances they transmit. Accurately determining the ionic charges of those ions is essential for understanding their role in such processes.
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