Time-dependent microbiome dynamics in orthodontic thermoplastic materials: comparing PETG and TPU.

Objectives: This study compares the effects of thermoplastic polyurethane (TPU) and polyethylene terephthalate glycol (PETG) aligners on the oral microbiome and salivary pH during orthodontic treatment.

Materials And Methods: Ten participants wore TPU and PETG aligners for 24 h. At five time points (1 min, 4, 8, 12, and 24 h), saliva was collected for pH analysis, and microbial samples were taken from both aligner and supragingival plaque surfaces for 16S rRNA sequencing.

A sequential drug delivery system based on silk fibroin scaffold for effective cartilage repair.

Endogenous repair of cartilage defects is a preferential strategy for cartilage repair, but always hindered by insufficient early-stage cells and incomplete cell differentiation at later stages. For cartilage regeneration, it is crucial to develop a sequential drug release system capable of recruiting endogenous bone marrow mesenchymal stem cells (BMSCs) and promoting their chondrogenic differentiation. Herein, based on our long-term and fruitful research on silk fibroin (SF) porous scaffolds, a cell-free sequential drug delivery SF scaffold was developed.

The tooth movement efficiency of different orthodontic thermoplastics for clear aligners: study protocol for a randomized controlled clinical trial.

Introduction: With regard to the esthetics and comfort of orthodontic treatment, the requirement for removable clear aligners (CAs) is increasing. Unlike conventional fixed orthodontic appliances, CAs were made of thermoplastic film by thermoforming on the personalized dental models. The construction of orthodontic thermoplastic is a critical factor for orthodontic tooth movement (OTM).

[Application of Deep Learning in Differential Diagnosis of Ameloblastoma and Odontogenic Keratocyst Based on Panoramic Radiographs].

Objective To evaluate the accuracy of different convolutional neural networks (CNN),representative deep learning models,in the differential diagnosis of ameloblastoma and odontogenic keratocyst,and subsequently compare the diagnosis results between models and oral radiologists. Methods A total of 1000 digital panoramic radiographs were retrospectively collected from the patients with ameloblastoma (500 radiographs) or odontogenic keratocyst (500 radiographs) in the Department of Oral and Maxillofacial Radiology,Peking University School of Stomatology.Eight CNN including ResNet (18,50,101),VGG (16,19),and EfficientNet (b1,b3,b5) were selected to distinguish ameloblastoma from odontogenic keratocyst.

Detection of vertical root fractures by cone-beam computed tomography based on deep learning.

Objectives: This study aims to evaluate the performance of ResNet models in the detection of and vertical root fractures (VRF) in Cone-beam Computed Tomography (CBCT) images.

Methods: A CBCT image dataset consisting of 28 teeth (14 intact and 14 teeth with VRF, 1641 slices) from 14 patients, and another dataset containing 60 teeth (30 intact and 30 teeth with VRF, 3665 slices) from an model were used for the establishment of VRFconvolutional neural network (CNN) models. The most popular CNN architecture ResNet with different layers was fine-tuned for the detection of VRF.

Dehiscence and fenestration of Class I individuals with normality patterns in the anterior region: a CBCT study.

Objectives: The purpose of this study was to investigate the prevalence of alveolar bone dehiscence and fenestration of Class I individuals with normality patterns in the anterior region using cone-beam computed tomography (CBCT).

Materials And Methods: A total of 4715 retrospective cases from January 2018 to December 2020 in the Orthodontic Department of xxx Hospital were screened. Sixty-one cases were Class I individuals with normality patterns in the anterior region.