RF-EMF Exposure Assessments in Greek Schools to Support Ubiquitous IoT-Based Monitoring in Smart Cities

Everyday living environments concentrate a growing amount of wireless communications leading to increased public concern for radiofrequency (RF) electromagnetic fields (EMF) exposure. Recent technological advances are turning the focus on Internet of Things (IoT) systems that enable automated and continuous real-time EMF monitoring, facing however several challenges mainly stemming from infrastructural costs. This paper seeks to provide a comprehensive view of RF-EMF levels in Greece and evidence-based decision support for a spatially prioritized deployment of an IoT RF-EMF monitoring system. We applied the stratified sampling method to estimate Electric Field Strength (EFS) in the 27MHz-3GHz range in 661 schools. Three different residential areas were considered, i.e. urban, semi-urban and rural. Results showed that the 95% confidence interval for the EFS is (0.40, 0.44) with central value equal to the sample mean 0.42 V/m. We obtained strong evidence that the mean EFS value for all Greek schools is 0.42, which is 52 times lower than the Greek safety limit and equal to 1% of international limits. Mean EFS values of individual residential areas were also significantly below safety limits. Rural areas displayed the highest EFS peaks comprising the strongest candidate to start the deployment of an IoT RF-EMF monitoring system from

Survivin regulation by HER2 through NF-κB and c-myc in irradiated breast cancer cells

Abstract Radiotherapy is an important treatment modality against cancer resulting in apoptosis and inhibition of cell growth. Survivin is an important cancer biomarker conferring to tumour cells increased survival potential by inhibiting apoptosis. In the present study, we investigated the implication of breast cancer cells features, as hormone receptors and p53 status, in the radio-resistance of breast cancer cells and in the regulation of survivin’s expression by nuclear factor (NF)-κB and c-myc. Six breast cancer cell lines Michigan Cancer Foundation (MCF-7), MCF-7/Human Epidermal Growth Factor Receptor (HER)2, M. D. Anderson – Metastatic Breast (MDA-MB-231), SK-BR-3, BT-474 and Human Breast Lactating (HBL-100) were irradiated and cell viability as well as cell cycle distribution were evaluated by 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay and flow cytometry, respectively. Survivin mRNA and protein levels were evaluated by real time PCR and Western blot analysis. Survivin and HER2 gene knockdown was performed with siRNA technology and investigation of transcription factors binding to survivin and c-myc gene promoters was assessed by chromatin immunoprecipitation. Student’s t-test and F-statistics were used for statistical evaluation. Our results demonstrated that only HER2+ breast cancer cells up-regulated survivin upon irradiation, whereas HER2 knockdown in HER2+ cells led to survivin’s down-regulation. Survivin and especially HER2 knockdown abolished the observed G2/M cell cycle checkpoint and reduced the radio-resistance of HER2 overexpressing breast cancer cells. Additionally, HER2 was found to regulate survivin’s expression through NF-κB and c-myc transcription factors. This study revealed the significance of HER2 in the radio-resistance of HER2+ breast cancer cells through induction of transcription factors NF-κB and c-myc, leading to activation of survivin, a downstream target oncogene preventing apoptosis

Fast spectroscopic multiple analysis (FASMA) for brain tumor classification: a clinical decision support system utilizing multi-parametric 3T MR data

A clinical decision support system (CDSS) for brain tumor classification can be used to assist in the diagnosis and grading of brain tumors. A Fast Spectroscopic Multiple Analysis (FASMA) system that uses combinations of multiparametric MRI data sets was developed as a CDSS for brain tumor classification. MRI metabolic ratios and spectra, from long and short TE, respectively, as well as diffusion and perfusion data were acquired from the intratumoral and peritumoral area of 126 patients with untreated intracranial tumors. These data were categorized based on the pathology, and different machine learning methods were evaluated regarding their classification performance for glioma grading and differentiation of infiltrating versus non-infiltrating lesions. Additional databases were embedded to the system, including updated literature values of the related MR parameters and typical tumor characteristics (imaging and histological), for further comparisons. Custom Graphical User Interface (GUI) layouts were developed to facilitate classification of the unknown cases based on the user's available MR data. The highest classification performance was achieved with a support vector machine (SVM) using the combination of all MR features. FASMA correctly classified 89 and 79 % in the intratumoral and peritumoral area, respectively, for cases from an independent test set. FASMA produced the correct diagnosis, even in the misclassified cases, since discrimination between infiltrative versus non-infiltrative cases was possible. FASMA is a prototype CDSS, which integrates complex quantitative MR data for brain tumor characterization. FASMA was developed as a diagnostic assistant that provides fast analysis, representation and classification for a set of MR parameters. This software may serve as a teaching tool on advanced MRI techniques, as it incorporates additional information regarding typical tumor characteristics derived from the literature