Objectives: Leptin, a polypeptide which is related to body fat regulation, is also found to have a role in the inflammatory reaction. The aim of this study is to assess the concentration of leptin in Gingival Crevicular Fluid (GCF) during orthodontic force application and to correlate its concentration to rate of tooth movement.
Methods: Twenty orthodontic patients (10 males and 10 females) were selected for the study. Leptin concentration was measured at T0, before force application; T1, one hour after force application; T2, one day after force application; T3, one week after force application; T4, one month after force application. GCF was collected using filter paper strips from the distal aspect of gingival sulcus of the right maxillary canine distalized by an active lace-backs of tooth movement was measured on dental casts, before and one month after force application. One-way ANOVA with Bonferroni correction and Pearson's correlation test were used to analyze the data.
Results: The mean GCF leptin concentration increased from T0 to T1, rose to a peak at T2, then declined to a minimum value at T3 and then increased to a value at T4, closer to the base line value (T0), and it was statistically significant ( < 0.05). There was positive correlation of the overall mean leptin concentration to rate of tooth movement (correlation coefficient = 0.634).
Conclusion: There was a biphasic change in GCF leptin concentration during one cycle of orthodontic force application. There was a positive correlation between the GCF leptin concentration and rate of tooth movement.
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http://dx.doi.org/10.4103/jos.JOS_58_18 | DOI Listing |
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Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California 90089, United States.
Conventional artificial intelligence (AI) systems are facing bottlenecks due to the fundamental mismatches between AI models, which rely on parallel, in-memory, and dynamic computation, and traditional transistors, which have been designed and optimized for sequential logic operations. This calls for the development of novel computing units beyond transistors. Inspired by the high efficiency and adaptability of biological neural networks, computing systems mimicking the capabilities of biological structures are gaining more attention.
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