Radiofrequency Exposure Levels in Greece.

Medical Physics Department (Medical School, University of Thessaly) participated in a Greek National EMF research program (EDBM34) with the scope to measure and evaluate radiofrequency (RF) exposure (27-3000 MHz) in areas of sensitive land use. A thousand (1000) measurements were carried out at two "metropolitan locations" (Athens and Thessaloniki: 624 points) and several rest urban/rural locations (376 points). SRM 3006 spectrum analyzer manufactured by Narda Safety Test Solutions was used. The broadband mean electric field in metropolitan areas was 0.41 V/m, while in the rest of Greece was 0.36 V/m. In metropolitan areas, the predominant RF source was the TV and Radio FM signals (36.2% mean contribution to the total RF exposure level). In the rest areas, the predominant source was the systems of the meteorological and military/defensive service (31.1%). The mobile sector contributed 14.9% in metropolitan areas versus 12.2% in the rest of Greece. The predominant mobile source was 900 MHz in both cases (4.5% in metropolitan areas vs. 3.3% in the rest of Greece). The total exposure from all RF sources complied with the International Commission on Non-Ionizing Radiation Protection (ICNIRP) 2020 safety guidelines [ICNIRP, 2020]. The maximum exposure level was 0.129% of the limit for the metropolitan areas vs. 0.110% for the rest of Greece. Nonremarkable differences between metropolitan areas' exposure and the rest of Greece. In most cases, new 5 G antennas will be added to the existing base stations. Thus, the total exposure may be increased, leading to higher safety distances. © 2023 Bioelectromagnetics Society.

A statistical analysis for RF-EMF exposure levels in sensitive land use: A novel study in Greek primary and secondary education schools.

BACKGROUND: The increasing popularity of mobile phones and the expansion of network infrastructure in Greece have given rise to public concerns about potential adverse health effects on sensitive groups, such as children, from long-term radio-frequency (RF) electromagnetic fields (EMFs) exposure. According to Greek law the RF limit values for sensitive land use (schools, hospitals, etc) have been set to 60% of those recommended by EU standard and 70% for the general population. AIMS: The objective of this study is to estimate mean RF-EMF exposure levels of Greek primary and secondary edu-cation schools located in urban environments. METHODS: In selecting the minimum sample size we observed that the variance of the random variable was unknown, as there has been no similar previous study in Greece with schools as the target population. For this reason, a pilot study was conducted in 65 schools in order to estimate the standard deviation of the population and use that value to calculate the minimum sample size. Using a random machine num-ber generator contracted in R based on pseudo-random number algorithms, we obtained a sample of 492 schools in order to estimate the mean value for RF-EMF radiation sources in the 27 MHz-3GHz range in schools within urban environments in Greece. RESULTS: We have performed the appropriate hypothesis test to get that there is sufficient evidence at the α = 0.05 level to conclude that the mean value for RF-EMF radiation sources in the 27 MHz-3GHz range, in schools within urban environments in Greece, is equal to 0.42 V/m, also a 95% confidence interval for the mean value is (0.4024, 0.4395)] with central value equal to the sample mean 0.4209. CONCLUSION: In conclusion, the exposure level in the locations tested are both below 60% of the highest limit set by ICNIRP (International Commision on Non-Ionizing Radiation Protection) regarding sensitive land use.

The Use of Machine Learning to Predict Prevalence of Subclinical Mastitis in Dairy Sheep Farms.

The objective of the study was to develop a computational model with which predictions regarding the level of prevalence of mastitis in dairy sheep farms could be performed. Data for the construction of the model were obtained from a large Greece-wide field study with 111 farms. Unsupervised learning methodology was applied for clustering data into two clusters based on 18 variables (17 independent variables related to health management practices applied in farms, climatological data at the locations of the farms, and the level of prevalence of subclinical mastitis as the target value). The K-means tool showed the highest significance for the classification of farms into two clusters for the construction of the computational model: median (interquartile range) prevalence of subclinical mastitis among farms was 20.0% (interquartile range: 15.8%) and 30.0% (16.0%) (p = 0.002). Supervised learning tools were subsequently used to predict the level of prevalence of the infection: decision trees, k-NN, neural networks, and Support vector machines. For each of these, combinations of hyperparameters were employed; 83 models were produced, and 4150 assessments were made in total. A computational model obtained by means of Support vector machines (kernel: 'linear', regularization parameter C = 3) was selected. Thereafter, the model was assessed through the results of the prevalence of subclinical mastitis in 373 records from sheep flocks unrelated to the ones employed for the selection of the model; the model was used for evaluation of the correct classification of the data in each of 373 sets, each of which included a test (prediction) subset with one record that referred to the farm under assessment. The median prevalence of the infection in farms classified by the model in each of the two categories was 10.4% (5.5%) and 36.3% (9.7%) (p < 0.0001). The overall accuracy of the model for the results presented by the K-means tool was 94.1%; for the estimation of the level of prevalence (<25.0%/≥25.0%) in the farms, it was 96.3%. The findings of this study indicate that machine learning algorithms can be usefully employed in predicting the level of subclinical mastitis in dairy sheep farms. This can facilitate setting up appropriate health management measures for interventions in the farms.

The use of an artificial neural network in the evaluation of the extracorporeal shockwave lithotripsy as a treatment of choice for urinary lithiasis.

Objective: Artificial neural networks (ANNs) are widely applied in medicine, since they substantially increase the sensitivity and specificity of the diagnosis, classification, and the prognosis of a medical condition. In this study, we constructed an ANN to evaluate several parameters of extracorporeal shockwave lithotripsy (ESWL), such as the outcome and safety of the procedure. Methods: Patients with urinary lithiasis suitable for ESWL treatment were enrolled. An ANN was designed using MATLAB. Medical data were collected from all patients and 12 nodes were used as inputs. Conventional statistical analysis was also performed. Results: Finally, 716 patients were included in our study. Univariate analysis revealed that diabetes and hydronephrosis were positively correlated with ESWL complications. Regarding efficacy, univariate analysis revealed that stone location, stone size, the number and density of shockwaves delivered, and the presence of a stent in the ureter were independent factors of the ESWL outcome. This was further confirmed when adjusted for sex and age in a multivariate analysis. The performance of the ANN at the end of the training state reached 98.72%. The four basic ratios (sensitivity, specificity, positive predictive value, and negative predictive value) were calculated for both training and evaluation data sets. The performance of the ANN at the end of the evaluation state was 81.43%. Conclusion: Our ANN achieved high score in predicting the outcome and the side effects of the ESWL treatment for urinary stones.

Quality of Life and Anxiety in Patients with First Diagnosed Non-Muscle Invasive Bladder Cancer Who Receive Adjuvant Bladder Therapy.

BACKGROUND: Bladder cancer (BC) is one of the most common malignancies (4.5%of all newly diagnosed cases worldwide). Most of the new BC cases are diagnosed as non-muscle invasive BC (NMIBC), needing continuous follow up after primary endoscopic therapy. Adjuvant bladder therapy with chemo- or immuno- agents, apart from the initial diagnosis, the strict surveillance program and the risk of recurrence, may have a major impact on the patients' physical and mental health. OBJECTIVE: We evaluated anxiety and quality of life (QoL) in patients who underwent surgery for NMIBC and followed a bladder instillation programme. METHODS: This is a prospective analysis of patients with histopathologically confirmed NMIBCs. Eligible were all adult patients with a single or multiple NMIBCs who underwent a transurethral tumor resection and followed a therapy with either BCG or Epirubicin instillations. The SF-36 questionnaire Physical and Mental health aspects were used for QoL assessment. Similarly, the STAI-Y was introduced for the state (STAI-Y1) and trait anxiety (STAI-Y2) evaluation. RESULTS: 117 eligible patients were screened, with 108 entering finally the study; 9 patients were excluded due to disease recurrence. 17 patients (15.7%) received Epirubicin (Ta-T1, Low Grade tumors), whereas 91 patients (84.3%) received BCG (T1, High Grade). Regarding SF-36 Physical a 6 months decrease was followed by an improvement at 12 months (p = 0.008). Similarly, an increase of the SF-36 Mental health score was identified (p = 0.03). In contrast to STAI-Y2 scores (p = 0.945), a long-term reduction of the state anxiety was identified (p = 0.001). Preoperative SF-36 Physical was inversely correlated with age (p = 0.029), while absence of alcohol was associated with lower mental health (p = 0.003). Overall, patient characteristics, habits and the administered treatment did not affect the postoperative QoL and anxiety. CONCLUSION: Patient QoL and anxiety improved during follow up. Still, further larger scale studies are required to support our findings.