Measurement of hepatitis B virus DNA in fresh versus processed dentin from chronically infected patients

Background Demineralized dentin matrix (DDM) is commonly used as a bone-graft substitute. This study measured and compared human hepatitis B viruses (HBV) DNA in fresh dentin to that of dentin processed into DDM extracted during dental treatment from HBV-infected patients. The hypothesis was that the processing procedure for DDM would inactivate or eliminate HBV in the dentin matrix obtained from infected patients. Methods Dentin from eighteen HBV-infected patients was collected and each dentin specimen was divided into two fragments. One fragment was used before processing as fresh dentin (control group) and the other was processed into DDM (experimental group). DNA was extracted and purified from each fresh and processed dentin specimen and the HBV DNA copy number quantitated by real time polymerase chain reaction. The HBV DNA copy number in the fresh dentin specimens were compared relative to serologic test results. The second parameter was to evaluate the effectiveness of the processing procedure (defatting, demineralization, freeze-drying, and sterilization) to inactivate or eliminate HBV by comparing the DNA copy number in the processed DDM with that in the matched fresh dentin specimens. All results were analyzed using Mann–Whitney U test to compare numerical measurements between groups and differences were considered statistically significant at P-values less than 0.05. Results The presence of HBV DNA was detected in 55.56% (10/18) of the fresh dentin specimens. For the ten HBV DNA-positive fresh dentin specimens, HBV DNA was detected in two (20%) of the matched processed dentin specimens. The copy number of HBV DNA in the two positive processed dentin specimens was 1.79 and 4.03, which were statistically lower than that of the fresh dentin specimens (P = 0.0167). Conclusions The results from this study suggested that fresh dentin may be a carrier of HBV and that the procedure used to generate DDM extensively reduced the levels of HBV DNA. Further studies are needed to evaluate the infectivity of HBV in processed dentin.This research was supported by a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI) funded by the Ministry of Health & Welfare, Republic of Korea (Grant Number: HI15C1535)

An Efficient Human Instance-Guided Framework for Video Action Recognition

In recent years, human action recognition has been studied by many computer vision researchers. Recent studies have attempted to use two-stream networks using appearance and motion features, but most of these approaches focused on clip-level video action recognition. In contrast to traditional methods which generally used entire images, we propose a new human instance-level video action recognition framework. In this framework, we represent the instance-level features using human boxes and keypoints, and our action region features are used as the inputs of the temporal action head network, which makes our framework more discriminative. We also propose novel temporal action head networks consisting of various modules, which reflect various temporal dynamics well. In the experiment, the proposed models achieve comparable performance with the state-of-the-art approaches on two challenging datasets. Furthermore, we evaluate the proposed features and networks to verify the effectiveness of them. Finally, we analyze the confusion matrix and visualize the recognized actions at human instance level when there are several people

Depression in Adolescence and Brain-Derived Neurotrophic Factor

Copyright © 2022 Lee, Shin, Song and Chang.The incidence of depression among adolescents has been rapidly increasing in recent years. Environmental and genetic factors have been identified as important risk factors for adolescent depression. However, the mechanisms underlying the development of adolescent depression that are triggered by these risk factors are not well understood. Clinical and preclinical studies have focused more on adult depression, and differences in depressive symptoms between adolescents and adults make it difficult to adequately diagnose and treat adolescent depression. Brain-derived neurotrophic factor (BDNF) is known to play a critical role in the pathophysiology of many psychiatric disorders, including depression. However, there are still few studies on adolescent depression. Therefore, in this review paper, the causes and treatment of adolescent depression and the function of BDNF are investigated.11Nsciescopu

Tailoring Graphene Nanosheets for Highly Improved Dispersion Stability and Quantitative Assessment in Nonaqueous Solvent

Aggregation is a critical limitation for the practical application of graphene-based materials. Herein, we report that graphene oxide (GO) nanosheets chemically modified with ethanolamine (EA), ethylene glycol (EG), and sulfanilic acid (SA) demonstrate superior dispersion stability in organic solvents, specifically EG, based on the differences in their covalent chemistries. Functionalized GO was successfully dispersed in EG at a concentration of 9.0 mg mL-1 (0.50 vol %), the highest dispersion concentration reported to date. Moreover, our study introduces a unique analytical method for the assessment of dispersion stability and successfully quantifies the instability index based on transmission profiles under centrifugation cycles. Interestingly, GO-EG and GO-EA exhibited highly improved dispersion stabilities approximately 96 and 48 times greater than that of GO in EG solvent, respectively. This finding highlights the critical role of surface functional groups in the enhancement of chemical affinity and miscibility in the surrounding media. We anticipate that the novel structural designs and unique tools presented in this study will further the understanding and application of chemically functionalized carbon materials.ope

Multi-Objective Instance Weighting-Based Deep Transfer Learning Network for Intelligent Fault Diagnosis

Fault diagnosis is a top-priority task for the health management of manufacturing processes. Deep learning-based methods are widely used to secure high fault diagnosis accuracy. Actually, it is difficult and expensive to collect large-scale data in industrial fields. Several prerequisite problems can be solved using transfer learning for fault diagnosis. Data from the source domain that are different but related to the target domain are used to increase the diagnosis performance of the target domain. However, a negative transfer occurs that degrades diagnosis performance due to the transfer when the discrepancy between and within domains is large. A multi-objective instance weighting-based transfer learning network is proposed to solve this problem and successfully applied to fault diagnosis. The proposed method uses a newly devised multi-objective instance weight to deal with practical situations where domain discrepancy is large. It adjusts the influence of the domain data on model training through two theoretically different indicators. Knowledge transfer is performed differentially by sorting instances similar to the target domain in terms of distribution with useful information for the target task. This domain optimization process maximizes the performance of transfer learning. A case study using an industrial robot and spot-welding testbed is conducted to verify the effectiveness of the proposed technique. The performance and applicability of transfer learning in the proposed method are observed in detail through the same case study as the actual industrial field for comparison. The diagnostic accuracy and robustness are high, even when few data are used. Thus, the proposed technique is a promising tool that can be used for successful fault diagnosis