Epidermal growth factor receptor regulates β-catenin location, stability, and transcriptional activity in oral cancer

<p>Abstract</p> <p>Background</p> <p>Many cancerous cells accumulate β-catenin in the nucleus. We examined the role of epidermal growth factor receptor (EGFR) signaling in the accumulation of β-catenin in the nuclei of oral cancer cells.</p> <p>Results</p> <p>We used two strains of cultured oral cancer cells, one with reduced EGFR expression (OECM1 cells) and one with elevated EGFR expression (SAS cells), and measured downstream effects, such as phosphorylation of β-catenin and GSK-3β, association of β-catenin with E-cadherin, and target gene regulation. We also studied the expression of EGFR, β-catenin, and cyclin D1 in 112 samples of oral cancer by immunostaining. Activation of EGFR signaling increased the amount of β-catenin in the nucleus and decreased the amount in the membranes. EGF treatment increased phosphorylation of β-catenin (tyrosine) and GSK-3β(Ser-(9), resulting in a loss of β-catenin association with E-cadherin. TOP-FLASH and FOP-FLASH reporter assays demonstrated that the EGFR signal regulates β-catenin transcriptional activity and mediates cyclin D1 expression. Chromatin immunoprecipitation experiments indicated that the EGFR signal affects chromatin architecture at the regulatory element of cyclin D1, and that the CBP, HDAC1, and Suv39h1 histone/chromatin remodeling complex is involved in this process. Immunostaining showed a significant association between EGFR expression and aberrant accumulation of β-catenin in oral cancer.</p> <p>Conclusions</p> <p>EGFR signaling regulates β-catenin localization and stability, target gene expression, and tumor progression in oral cancer. Moreover, our data suggest that aberrant accumulation of β-catenin under EGFR activation is a malignancy marker of oral cancer.</p

Nonlinear and conventional biosignal analyses applied to tilt table test for evaluating autonomic nervous system and autoregulation

Copyright © Tseng et al.; Licensee Bentham Open. This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/ by-nc/3.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.Tilt table test (TTT) is a standard examination for patients with suspected autonomic nervous system (ANS) dysfunction or uncertain causes of syncope. Currently, the analytical method based on blood pressure (BP) or heart rate (HR) changes during the TTT is linear but normal physiological modulations of BP and HR are thought to be predominately nonlinear. Therefore, this study consists of two parts: the first part is analyzing the HR during TTT which is compared to three methods to distinguish normal controls and subjects with ANS dysfunction. The first method is power spectrum density (PSD), while the second method is detrended fluctuation analysis (DFA), and the third method is multiscale entropy (MSE) to calculate the complexity of system. The second part of the study is to analyze BP and cerebral blood flow velocity (CBFV) changes during TTT. Two measures were used to compare the results, namely correlation coefficient analysis (nMxa) and MSE. The first part of this study has concluded that the ratio of the low frequency power to total power of PSD, and MSE methods are better than DFA to distinguish the difference between normal controls and patients groups. While in the second part, the nMxa of the three stages moving average window is better than the nMxa with all three stages together. Furthermore the analysis of BP data using MSE is better than CBFV data.The Stroke Center and Department of Neurology, National Taiwan University, National Science Council in Taiwan, and the Center for Dynamical Biomarkers and Translational Medicine, National Central University, which is sponsored by National Science Council and Min-Sheng General Hospital Taoyuan

A Comparison of Different Algorithms for EEG Signal Analysis for the Purpose of Monitoring Depth of Anesthesia

All rights reserved. Electroencephalography (EEG) signals have been commonly used for assessing the level of anesthesia during surgery. However, the collected EEG signals are usually corrupted with artifacts which can seriously reduce the accuracy of the depth of anesthesia (DOA) monitors. In this paper, the main purpose is to compare five different EEG based anesthesia indices, namely median frequency (MF), 95% spectral edge frequency (SEF), approximate entropy (ApEn), sample entropy (SampEn) and permutation entropy (PeEn), for their artifacts rejection ability in order to measure the DOA accurately. The current analysis is based on synthesized EEG corrupted with four different types of artificial artifacts and real data collected from patients undergoing general anesthesia during surgery. The experimental results demonstrate that all indices could discriminate awake from anesthesia state (p < 0.05), however PeEn is superior to other indices. Furthermore, a combined index is obtained by applying these five indices as inputs to train, validate and test a feed-forward back-propagation artificial neural network (ANN) model with bispectral index (BIS) as target. The combined index via ANN offers more advantages with higher correlation of 0.80 ± 0.01 for real time DOA monitoring in comparison with single indices.Center for Dynamical Biomarkers and Translational Medicine, National Central University, Taiwan which is sponsored by Ministry of Science and Technology (Grant Number: MOST103-2911-I-008-001). National Natural Science Foundation of China (Grant Number: 51475342)

A Comparison of Different Algorithms for EEG Signal Analysis for the Purpose of Monitoring Depth of Anesthesia

All rights reserved. Electroencephalography (EEG) signals have been commonly used for assessing the level of anesthesia during surgery. However, the collected EEG signals are usually corrupted with artifacts which can seriously reduce the accuracy of the depth of anesthesia (DOA) monitors. In this paper, the main purpose is to compare five different EEG based anesthesia indices, namely median frequency (MF), 95% spectral edge frequency (SEF), approximate entropy (ApEn), sample entropy (SampEn) and permutation entropy (PeEn), for their artifacts rejection ability in order to measure the DOA accurately. The current analysis is based on synthesized EEG corrupted with four different types of artificial artifacts and real data collected from patients undergoing general anesthesia during surgery. The experimental results demonstrate that all indices could discriminate awake from anesthesia state (p < 0.05), however PeEn is superior to other indices. Furthermore, a combined index is obtained by applying these five indices as inputs to train, validate and test a feed-forward back-propagation artificial neural network (ANN) model with bispectral index (BIS) as target. The combined index via ANN offers more advantages with higher correlation of 0.80 ± 0.01 for real time DOA monitoring in comparison with single indices.Center for Dynamical Biomarkers and Translational Medicine, National Central University, Taiwan which is sponsored by Ministry of Science and Technology (Grant Number: MOST103-2911-I-008-001). National Natural Science Foundation of China (Grant Number: 51475342)

Healthcare Engineering Defined: A White Paper

Engineering has been playing an important role in serving and advancing healthcare. The term "Healthcare Engineering" has been used by professional societies, universities, scientific authors, and the healthcare industry for decades. However, the definition of "Healthcare Engineering" remains ambiguous. The purpose of this position paper is to present a definition of Healthcare Engineering as an academic discipline, an area of research, a field of specialty, and a profession. Healthcare Engineering is defined in terms of what it is, who performs it, where it is performed, and how it is performed, including its purpose, scope, topics, synergy, education/training, contributions, and prospects

EEG artifacts reduction by multivariate empirical mode decomposition and multiscale entropy for monitoring depth of anaesthesia during surgery

Electroencephalography (EEG) has been widely utilized to measure the depth of anaesthesia (DOA) during operation. However, the EEG signals are usually contaminated by artifacts which have a consequence on the measured DOA accuracy. In this study, an effective and useful filtering algorithm based on multivariate empirical mode decomposition and multiscale entropy (MSE) is proposed to measure DOA. Mean entropy of MSE is used as an index to find artifacts-free intrinsic mode functions. The effect of different levels of artifacts on the performances of the proposed filtering is analysed using simulated data. Furthermore, 21 patients' EEG signals are collected and analysed using sample entropy to calculate the complexity for monitoring DOA. The correlation coefficients of entropy and bispectral index (BIS) results show 0.14 ± 0.30 and 0.63 ± 0.09 before and after filtering, respectively. Artificial neural network (ANN) model is used for range mapping in order to correlate the measurements with BIS. The ANN method results show strong correlation coefficient (0.75 ± 0.08). The results in this paper verify that entropy values and BIS have a strong correlation for the purpose of DOA monitoring and the proposed filtering method can effectively filter artifacts from EEG signals. The proposed method performs better than the commonly used wavelet denoising method. This study provides a fully adaptive and automated filter for EEG to measure DOA more accuracy and thus reduce risk related to maintenance of anaesthetic agents.This research was financially supported by the Center for Dynamical Biomarkers and Translational Medicine, National Central University, Taiwan, which is sponsored by Ministry of Science and Technology (Grant Number: NSC102-2911-I-008-001). Also, it was supported by Chung-Shan Institute of Science and Technology in Taiwan (Grant Numbers: CSIST-095-V301 and CSIST-095-V302) and National Natural Science Foundation of China (Grant Number: 51475342)

A novel model incorporating 3D printing for endodontic training

Aim or Purpose: A 3D printed model includes a tooth body, pulp cavity, and root canal system is useful in simulating clinical condition.The 3D printing method and Cone-Beam Computed Tomography (CBCT) image to create a tooth model that can be applied to various different dental training manikin. Materials and Methods: We used 3D printing to fabricate teeth obtained from CBCT image. Being installed into the model, a conductive wire was placed at the apex of the root tip where filled with solution. On the other side, a connective probe wire extended to the cheek of the human head model and was then connected with the lip hock of the electronic apex locator to form a circuit. When the probe of the electronic apex locator approaches the apex, an audible sound is produced. A bee-bee warning sound was emitted as the probe passes through the apical foramen. Therefore, the devices can simulate the actual conditions during clinical root canal treatment. Results: This device can be applied in preclinical dental student root canal therapy training, objective structured clinical examination (OSCE), endodontic specialist training and standardized training procedures. This may also integrate with dental simulation systems and develop a new training programs in dental simulation system to help our biomedical industry develop distinctive products and enhance international competitiveness. Conclusions: This model can be conveniently attached onto the dental training manikin at preclinical dental laboratory training in dental schools

Long-term maxillary anteroposterior changes following maxillary protraction with or without expansion: A meta-analysis and meta-regression.

BackgroundMaxillary protraction with or without expansion is a widely known orthopedic treatment modality in growing skeletal Class III patients. However, limited data are available regarding the outcomes of long-term changes in the maxilla. Aim of this meta-analysis was to assess the effectiveness of the long-term maxillary anteroposterior changes following a facemask therapy with or without rapid maxillary expansion in growing skeletal Class III patients.MethodsA comprehensive literature search was conducted using the databases of PubMed, Science Direct, Web of Science, and Embase. Randomized controlled trials and cohort studies, published up to Sep. 2020, with maxillary protraction and/or expansion as keywords were included in this meta-analysis. Risk of bias within and across studies were assessed using the Cochrane tools (RoB2.0 and ROBINS-I) and GRADE approach. Overall and subgroup comparisons with the random-effect model were performed in this meta-analysis. Meta-regression models were designed to determine potential heterogeneity.ResultsThere was a statistically significant increase (Mean difference, 2.29°; 95% confidence interval, 1.86-2.73; and p ConclusionThis meta-analysis revealed that maxillary protraction therapy could be effective for a short-term in correcting maxillary hypoplasia and the treatment result was not affected by mean age and sex. However, with increased follow-up duration, the sagittal maxillary changes gradually decreased. Limitations on this review were only the SNA angle was used and clinical heterogeneity was not discussed. The quality of evidence was moderate. Further long-term observational studies are necessary for a comprehensive evaluation of the effects on maxillary skeletal changes