Automatic mandibular canal detection using a deep convolutional neural network

The practicability of deep learning techniques has been demonstrated by their successful implementation in varied fields, including diagnostic imaging for clinicians. In accordance with the increasing demands in the healthcare industry, techniques for automatic prediction and detection are being widely researched. Particularly in dentistry, for various reasons, automated mandibular canal detection has become highly desirable. The positioning of the inferior alveolar nerve (IAN), which is one of the major structures in the mandible, is crucial to prevent nerve injury during surgical procedures. However, automatic segmentation using Cone beam computed tomography (CBCT) poses certain difficulties, such as the complex appearance of the human skull, limited number of datasets, unclear edges, and noisy images. Using work-in-progress automation software, experiments were conducted with models based on 2D SegNet, 2D and 3D U-Nets as preliminary research for a dental segmentation automation tool. The 2D U-Net with adjacent images demonstrates higher global accuracy of 0.82 than naïve U-Net variants. The 2D SegNet showed the second highest global accuracy of 0.96, and the 3D U-Net showed the best global accuracy of 0.99. The automated canal detection system through deep learning will contribute significantly to efficient treatment planning and to reducing patients’ discomfort by a dentist. This study will be a preliminary report and an opportunity to explore the application of deep learning to other dental fields.Peer reviewe

Three-Dimensional Blood-Brain Barrier Model for in vitro Studies of Neurovascular Pathology

Blood–brain barrier (BBB) pathology leads to neurovascular disorders and is an important target for therapies. However, the study of BBB pathology is difficult in the absence of models that are simple and relevant. In vivo animal models are highly relevant, however they are hampered by complex, multi-cellular interactions that are difficult to decouple. In vitro models of BBB are simpler, however they have limited functionality and relevance to disease processes. To address these limitations, we developed a 3-dimensional (3D) model of BBB on a microfluidic platform. We verified the tightness of the BBB by showing its ability to reduce the leakage of dyes and to block the transmigration of immune cells towards chemoattractants. Moreover, we verified the localization at endothelial cell boundaries of ZO-1 and VE-Cadherin, two components of tight and adherens junctions. To validate the functionality of the BBB model, we probed its disruption by neuro-inflammation mediators and ischemic conditions and measured the protective function of antioxidant and ROCK-inhibitor treatments. Overall, our 3D BBB model provides a robust platform, adequate for detailed functional studies of BBB and for the screening of BBB-targeting drugs in neurological diseases

Prevalence, Correlates, and Comorbidity of 12-Month Tobacco Dependence among Ever-smokers in South Korea, During 1984-2001

The rate of dependence among ever-users of a drug indicates the risk of developing dependence once an individual has been exposed to the drug. This is the first study to investigate 12-month tobacco dependence (TD) among ever-smokers in a community-based population. Analyses were based on two national studies of representative samples aged 18-64 in 1984 (n=5,025) and in 2001 (n=6,275), conducted with household visits and face-to-face interviews. The rates of 12-month TD among ever-smokers in men showed no significant difference between 51.6% in 1984 and 50.6% in 2001. On the contrary, the rates in women significantly increased from 33.3% in 1984 to 52.8% in 2001. After adjusting for the sociodemographic variables, 'male gender' was significantly associated with 12-month TD among ever-smokers in 1984, but not in 2001. 'Unmarried' was significantly associated in 2001 but not in 1984. 'Alcohol dependence' was the only psychiatric disorder associated with 12-month TD in both study years. In conclusion, 12-month TD was found in about 50% of ever-smokers, and gender differences between the rates of 12-month TD which was observed in 1984 disappeared in 2001. Individuals with 12-month TD showed higher comorbidity with alcohol dependence than ever-smokers without TD

A new strategy for enhancing imputation quality of rare variants from next-generation sequencing data via combining SNP and exome chip data

Background: Rare variants have gathered increasing attention as a possible alternative source of missing heritability. Since next generation sequencing technology is not yet cost-effective for large-scale genomic studies, a widely used alternative approach is imputation. However, the imputation approach may be limited by the low accuracy of the imputed rare variants. To improve imputation accuracy of rare variants, various approaches have been suggested, including increasing the sample size of the reference panel, using sequencing data from study-specific samples (i.e., specific populations), and using local reference panels by genotyping or sequencing a subset of study samples. While these approaches mainly utilize reference panels, imputation accuracy of rare variants can also be increased by using exome chips containing rare variants. The exome chip contains 250 K rare variants selected from the discovered variants of about 12,000 sequenced samples. If exome chip data are available for previously genotyped samples, the combined approach using a genotype panel of merged data, including exome chips and SNP chips, should increase the imputation accuracy of rare variants. Results: In this study, we describe a combined imputation which uses both exome chip and SNP chip data simultaneously as a genotype panel. The effectiveness and performance of the combined approach was demonstrated using a reference panel of 848 samples constructed using exome sequencing data from the T2D-GENES consortium and 5,349 sample genotype panels consisting of an exome chip and SNP chip. As a result, the combined approach increased imputation quality up to 11 %, and genomic coverage for rare variants up to 117.7 % (MAF < 1 %), compared to imputation using the SNP chip alone. Also, we investigated the systematic effect of reference panels on imputation quality using five reference panels and three genotype panels. The best performing approach was the combination of the study specific reference panel and the genotype panel of combined data. Conclusions: Our study demonstrates that combined datasets, including SNP chips and exome chips, enhances both the imputation quality and genomic coverage of rare variants

The genetic architecture of type 2 diabetes

The genetic architecture of common traits, including the number, frequency, and effect sizes of inherited variants that contribute to individual risk, has been long debated. Genome-wide association studies have identified scores of common variants associated with type 2 diabetes, but in aggregate, these explain only a fraction of heritability. To test the hypothesis that lower-frequency variants explain much of the remainder, the GoT2D and T2D-GENES consortia performed whole genome sequencing in 2,657 Europeans with and without diabetes, and exome sequencing in a total of 12,940 subjects from five ancestral groups. To increase statistical power, we expanded sample size via genotyping and imputation in a further 111,548 subjects. Variants associated with type 2 diabetes after sequencing were overwhelmingly common and most fell within regions previously identified by genome-wide association studies. Comprehensive enumeration of sequence variation is necessary to identify functional alleles that provide important clues to disease pathophysiology, but large-scale sequencing does not support a major role for lower-frequency variants in predisposition to type 2 diabetes

Three-Dimensional Analysis of Bone Volume Change at Donor Sites in Mandibular Body Bone Block Grafts by a Computer-Assisted Automatic Registration Method: A Retrospective Study

This study aimed to evaluate the bone volume change at donor sites in patients who received mandibular body bone block grafts using intensity-based automatic image registration. A retrospective study was conducted with 32 patients who received mandibular bone block grafts between 2017 and 2019 at the Pusan National University Dental Hospital. Cone-beam computed tomography (CBCT) images were obtained before surgery (T0), 1 day after surgery (T1), and 4 months after surgery (T2). Scattered artefacts were removed by manual segmentation. The T0 image was used as the reference image for registration of T1 and T2 images using intensity-based registration. A total of 32 donor sites were analyzed three-dimensionally. The volume and pixel value of the bones were measured and analyzed. The mean regenerated bone volume rate on follow-up images (T2) was 34.87% ± 17.11%. However, no statistically significant differences of regenerated bone volume were noted among the four areas of the donor site (upper anterior, upper posterior, lower anterior, and lower posterior). The mean pixel value rate of the follow-up images (T2) was 78.99% ± 16.9% compared with that of T1, which was statistically significant (p < 0.05). Intensity-based registration with histogram matching showed that newly generated bone is generally qualitatively and quantitatively poorer than the original bone, thus revealing the feasibility of pixel value to evaluate bone quality in CBCT images. Considering the bone mass recovered in this study, 4 months may not be sufficient for a second harvesting, and a longer period of follow-up is required