Cluster Analysis of Residential Personal Exposure to ELF Magnetic Field in Children: Effect of Environmental Variables

Tognola, Gabriella; Chiaramello, Emma; Bonato, Marta; Magne, Isabelle; Souques, Martine; Fiocchi, Serena; Parazzini, Marta; Ravazzani, Paolo

doi:10.3390/ijerph16224363

Cited by 11 publications

(4 citation statements)

References 27 publications

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“…The exposure to MF can vary greatly over time and distance, has multiple sources, and is imperceptible and ubiquitous [42][43][44][45]. In exposed schools, children may experience a higher chance of receiving a mean exposure >0.4 µT during school hours [46,47]; whereas those living in big buildings or using electric heating appliances in larger families had a generally higher level of personal indoor exposure [48]. Based on the known location of domestic and service MF sources, apartments can be reliably classified as high and low MF-exposed [49][50][51].…”

Section: Epidemiology Of Residential/domestic Exposure To Mfmentioning

confidence: 99%

Magnetic Fields and Cancer: Epidemiology, Cellular Biology, and Theranostics

Maffei

2022

IJMS

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Humans are exposed to a complex mix of man-made electric and magnetic fields (MFs) at many different frequencies, at home and at work. Epidemiological studies indicate that there is a positive relationship between residential/domestic and occupational exposure to extremely low frequency electromagnetic fields and some types of cancer, although some other studies indicate no relationship. In this review, after an introduction on the MF definition and a description of natural/anthropogenic sources, the epidemiology of residential/domestic and occupational exposure to MFs and cancer is reviewed, with reference to leukemia, brain, and breast cancer. The in vivo and in vitro effects of MFs on cancer are reviewed considering both human and animal cells, with particular reference to the involvement of reactive oxygen species (ROS). MF application on cancer diagnostic and therapy (theranostic) are also reviewed by describing the use of different magnetic resonance imaging (MRI) applications for the detection of several cancers. Finally, the use of magnetic nanoparticles is described in terms of treatment of cancer by nanomedical applications for the precise delivery of anticancer drugs, nanosurgery by magnetomechanic methods, and selective killing of cancer cells by magnetic hyperthermia. The supplementary tables provide quantitative data and methodologies in epidemiological and cell biology studies. Although scientists do not generally agree that there is a cause-effect relationship between exposure to MF and cancer, MFs might not be the direct cause of cancer but may contribute to produce ROS and generate oxidative stress, which could trigger or enhance the expression of oncogenes.

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Section: Epidemiology Of Residential/domestic Exposure To Mfmentioning

confidence: 99%

Magnetic Fields and Cancer: Epidemiology, Cellular Biology, and Theranostics

Maffei

2022

IJMS

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“…This will deliver five different feature groups or distributions for every laboratory experiment. Tognola et al found [65] cluster analysis (unsupervised learning) is a reasonable way to find features that are best at identifying the exposure situations. Supervised learning is better tailored to discover features in occupational and environmental epidemiology and public health studies [54].…”

Section: Discussionmentioning

confidence: 99%

Supervised Machine Learning Algorithms for Bioelectromagnetics: Prediction Models and Feature Selection Techniques Using Data from Weak Radiofrequency Radiation Effect on Human and Animals Cells

Halgamuge

2020

IJERPH

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The emergence of new technologies to incorporate and analyze data with high-performance computing has expanded our capability to accurately predict any incident. Supervised Machine learning (ML) can be utilized for a fast and consistent prediction, and to obtain the underlying pattern of the data better. We develop a prediction strategy, for the first time, using supervised ML to observe the possible impact of weak radiofrequency electromagnetic field (RF-EMF) on human and animal cells without performing in-vitro laboratory experiments. We extracted laboratory experimental data from 300 peer-reviewed scientific publications (1990–2015) describing 1127 experimental case studies of human and animal cells response to RF-EMF. We used domain knowledge, Principal Component Analysis (PCA), and the Chi-squared feature selection techniques to select six optimal features for computation and cost-efficiency. We then develop grouping or clustering strategies to allocate these selected features into five different laboratory experiment scenarios. The dataset has been tested with ten different classifiers, and the outputs are estimated using the k-fold cross-validation method. The assessment of a classifier’s prediction performance is critical for assessing its suitability. Hence, a detailed comparison of the percentage of the model accuracy (PCC), Root Mean Squared Error (RMSE), precision, sensitivity (recall), 1 − specificity, Area under the ROC Curve (AUC), and precision-recall (PRC Area) for each classification method were observed. Our findings suggest that the Random Forest algorithm exceeds in all groups in terms of all performance measures and shows AUC = 0.903 where k-fold = 60. A robust correlation was observed in the specific absorption rate (SAR) with frequency and cumulative effect or exposure time with SAR×time (impact of accumulated SAR within the exposure time) of RF-EMF. In contrast, the relationship between frequency and exposure time was not significant. In future, with more experimental data, the sample size can be increased, leading to more accurate work.

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“…Alzheimer's disease [35][36][37][38][39][40] Autism Spectrum Disorders [41,42] Brain Tumors [43][44][45] Breast Diabetes [73][74][75][76][77][78] Exposure to extremely low frequency waves [79,80] Glioblastoma [81,82] Heart Failure [83][84][85][86][87] Kidney Disease [88,89] Lung Cancer [90][91][92][93][94] Melanoma [95,96] Multiple Sclerosis [97][98][99] Parkinson's Disease [100][101][102][103][104][105][106][107] Prostate Cancer [108,109] Rectal Cancer [110]…”

Section: Topic Referencementioning

confidence: 99%

Exploring the State of Machine Learning and Deep Learning in Medicine: A Survey of Italian Research Community

Bottrighi¹,

Pennisi²

2023

Preprint

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Artificial Intelligence (AI) is becoming increasingly important, especially in the medical field. While AI has been used in medicine for some time, its growth in the last decade has been remarkable. Specifically, Machine Learning (ML) and Deep Learning (DL) techniques in medicine have been increasingly adopted thanks to the growing abundance of health-related data, improved suitability of such techniques for managing large data-sets, and more computational power. The Italian scientific community has been instrumental in advancing this research area. This article aims to conduct a comprehensive investigation of the ML and DL methodologies and applications used in medicine by the Italian research community in the last five years.

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Cluster Analysis of Residential Personal Exposure to ELF Magnetic Field in Children: Effect of Environmental Variables

Cited by 11 publications

References 27 publications

Magnetic Fields and Cancer: Epidemiology, Cellular Biology, and Theranostics

Magnetic Fields and Cancer: Epidemiology, Cellular Biology, and Theranostics

Supervised Machine Learning Algorithms for Bioelectromagnetics: Prediction Models and Feature Selection Techniques Using Data from Weak Radiofrequency Radiation Effect on Human and Animals Cells

Exploring the State of Machine Learning and Deep Learning in Medicine: A Survey of Italian Research Community

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