Development of In Situ Product Recovery (ISPR) System Using Amberlite IRA67 for Enhanced Biosynthesis of Hyaluronic Acid by Streptococcus zooepidemicus.

High broth viscosity due to the accumulation of hyaluronic acid (HA) causes a limited yield of HA. It is a major problem of HA production using Streptococcus zooepidemicus. Extractive fermentation via in situ product recovery (ISPR) was utilized to enhance the HA production. Resins from Amberlite: IRA400 Cl; IRA900 Cl; IRA410 Cl; IRA402 Cl; and IRA67 were tested for the HA adsorption. IRA67 showed high adsorption capacity on HA. The study of the adsorption via a 2 L stirred tank bioreactor of S. zooepidemicus fermentation was investigated to elucidate the adsorption of HA onto IRA67 in dispersed and integrated internal column systems. The application of a dispersed IRA67 improved the HA production compared to the fermentation without resin addition by 1.37-fold. The HA production was further improved by 1.36-fold with an internal column (3.928 g/L) over that obtained with dispersed IRA67. The cultivation with an internal column shows the highest reduction of viscosity value after the addition of IRA67 resin: from 58.8 to 23.7 (mPa·s), suggesting the most effective ISPR of HA. The improved biosynthesis of HA indicated that an extractive fermentation by ISPR adsorption is effective and may streamline the HA purification.

Convolutional neural networks for automated tooth numbering on panoramic radiographs: A scoping review.

Purpose: The objective of this scoping review was to investigate the applicability and performance of various convolutional neural network (CNN) models in tooth numbering on panoramic radiographs, achieved through classification, detection, and segmentation tasks. Material and Methods: An online search was performed of the PubMed, Science Direct, and Scopus databases. Based on the selection process, 12 studies were included in this review. Results: Eleven studies utilized a CNN model for detection tasks, 5 for classification tasks, and 3 for segmentation tasks in the context of tooth numbering on panoramic radiographs. Most of these studies revealed high performance of various CNN models in automating tooth numbering. However, several studies also highlighted limitations of CNNs, such as the presence of false positives and false negatives in identifying decayed teeth, teeth with crown prosthetics, teeth adjacent to edentulous areas, dental implants, root remnants, wisdom teeth, and root canal-treated teeth. These limitations can be overcome by ensuring both the quality and quantity of datasets, as well as optimizing the CNN architecture. Conclusion: CNNs have demonstrated high performance in automated tooth numbering on panoramic radiographs. Future development of CNN-based models for this purpose should also consider different stages of dentition, such as the primary and mixed dentition stages, as well as the presence of various tooth conditions. Ultimately, an optimized CNN architecture can serve as the foundation for an automated tooth numbering system and for further artificial intelligence research on panoramic radiographs for a variety of purposes.

Central Odontogenic Myxoma: A Radiographic Analysis.

OBJECTIVE: This study aimed to determine the radiographic characteristics of odontogenic myxomas (OMs) and their associations. MATERIALS AND METHODS: The study enrolled radiographs of patients taken between 2005 and 2019 with a confirmed histopathological diagnosis of central OM. OM radiographic features were evaluated, including location, border, locularity, involved area, the number of included teeth, root resorption, tooth displacement, bone expansion, bone perforation, and periosteal reaction. Fisher's exact test was used for statistical analysis. RESULTS: Significant associations were found between the OM border and the affected jaw (p=0.036), locularity (p=0.036), involved areas (p=0.009), and bone perforation (p=0.036). OMs with an ill-defined border were associated with maxillary lesions, multilocularity, dentate areas, and cortical bone perforation. The number of included teeth (2 or fewer or 3 or more) was significantly associated with locularity (p=0.010), involved area (p=0.045), and bone expansion (p=0.010). Larger OMs including 3 or more teeth, were associated with a multilocular appearance, dentate areas, and bone expansion. CONCLUSION: The border of OM and the number of included teeth are related to other radiographic appearances. Understanding these relationships could help in treatment decisions and help better understand the nature of OM.