Detection of dental restorations using no-code artificial intelligence.

Objectives: The purpose of this study was to utilize a no-code computer vision platform to develop, train, and evaluate a model specifically designed for segmenting dental restorations on panoramic radiographs.

Methods: One hundred anonymized panoramic radiographs were selected for this study. Accurate labeling of dental restorations was performed by calibrated dental faculty and students, with subsequent final review by an oral radiologist.

Identifying Primary Proximal Caries Lesions in Pediatric Patients From Bitewing Radiographs Using Artificial Intelligence.

To develop a no-code artificial intelligence (AI) model capable of identifying primary proximal surface caries using bitewings among pediatric patients. One hundred bitewing radiographs acquired at pediatric dental clinics were anonymized and reviewed. The inclusion criteria encompassed bitewing radiographs of adequate diagnostic quality of primary and mixed-dentition stages.

Bayesian Rare Variant Analysis Identifies Novel Schizophrenia Putative Risk Genes.

The genetics of schizophrenia is so complex that it involves both common variants and rare variants. Rare variant association studies of schizophrenia are challenging because statistical methods for rare variant analysis are under-powered due to the rarity of rare variants. The recent Schizophrenia Exome meta-analysis (SCHEMA) consortium, the largest consortium in this field to date, has successfully identified 10 schizophrenia risk genes from ultra-rare variants by burden test, while more risk genes remain to be discovered by more powerful rare variant association test methods.

Electrochemical Properties of Nickel-Titanium Rotary Endodontic Instruments.

Introduction: Nickel-titanium rotary endodontic files have been commercially available for decades, but more recent innovations have introduced heat-treated and surface-treated files. This study investigated the corrosion properties of various nickel-titanium files in normal saline and sodium hypochlorite (NaOCl).

Methods: Ten different file brands of size 40 with a 0.

Association between periodontal health status and quality of life: a cross-sectional study.

Introduction: Attachment loss due to periodontal diseases is associated with functional limitations as well as physical pain and psychological discomfort, which may lead to a reduced quality of life. The purpose of this study is to determine if the oral health status, specifically the periodontal status, influences oral health-related quality of life.

Materials And Methods: Survey data were collected in a US dental school clinical setting in a cross-sectional study.

Effect of surface finish and resin cement on the bond strength to CAD-CAM ceramics for interim resin-bonded prostheses.

Statement Of Problem: Resin-bonded prostheses, including interim resin-bonded prostheses, are effective in preserving tooth structure compared with other types of fixed dental prostheses for the replacement of missing teeth. However, loss of retention remains a notable concern with these types of prostheses.

Purpose: The purpose of this in vitro study was to investigate the influence of glass-ceramic type, resin type, and surface finish on the shear bond strength (SBS) to the CAD-CAM ceramics used to fabricate interim resin-bonded prostheses.

An in-vitro analysis of the accuracy of different guided surgery systems - They are not all the same.

Objectives: Different static computer-assisted implant surgery (sCAIS) systems are available that are based on different design concepts. The objective was to assess seven different systems in a controlled environment.

Materials And Methods: Each n = 20 implants were placed in identical mandible replicas (total n = 140).

Gaussian Bayesian network comparisons with graph ordering unknown.

A Bayesian approach is proposed that unifies Gaussian Bayesian network constructions and comparisons between two networks (identical or differential) for data with graph ordering unknown. When sampling graph ordering, to escape from local maximums, an adjusted single queue equi-energy algorithm is applied. The conditional posterior probability mass function for network differentiation is derived and its asymptotic proposition is theoretically assessed.

Jump-seq: Genome-Wide Capture and Amplification of 5-Hydroxymethylcytosine Sites.

5-Hydroxymethylcytosine (5hmC) arises from the oxidation of 5-methylcytosine (5mC) by Fe and 2-oxoglutarate-dependent 10-11 translocation (TET) family proteins. Substantial levels of 5hmC accumulate in many mammalian tissues, especially in neurons and embryonic stem cells, suggesting a potential active role for 5hmC in epigenetic regulation beyond being simply an intermediate of active DNA demethylation. 5mC and 5hmC undergo dynamic changes during embryogenesis, neurogenesis, hematopoietic development, and oncogenesis.

The nested joint clustering via Dirichlet process mixture model.

This article focuses on the clustering problem based on Dirichlet process (DP) mixtures. To model both time invariant and temporal patterns, different from other existing clustering methods, the proposed semi-parametric model is flexible in that both the common and unique patterns are taken into account simultaneously. Furthermore, by jointly clustering subjects and the associated variables, the intrinsic complex shared patterns among subjects and among variables are expected to be captured.

Adjusting background noise in cluster analyses of longitudinal data.

Background noise in cluster analyses can potentially mask the true underlying patterns. To tease out patterns uniquely to certain populations, a Bayesian semi-parametric clustering method is presented. It infers and adjusts background noise.

An efficient Bayesian approach for Gaussian Bayesian network structure learning.

This article proposes a Bayesian computing algorithm to infer Gaussian directed acyclic graphs (DAG's). It has the ability of escaping local modes and maintaining adequate computing speed compared to existing methods. Simulations demonstrated that the proposed algorithm has low false positives and false negatives in comparison to an algorithm applied to DAG's.

A full bayesian approach for boolean genetic network inference.

Boolean networks are a simple but efficient model for describing gene regulatory systems. A number of algorithms have been proposed to infer Boolean networks. However, these methods do not take full consideration of the effects of noise and model uncertainty.