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

Extremely low frequency magnetic fields (ELF-MF) in Switzerland: From exposure monitoring to daily exposure scenarios

Exposure to extremely low frequency magnetic fields (ELF-MF) is ubiquitous in our daily environment. This study aims to provide a comprehensive overview of the ambient ELF-MF exposure in Switzerland and presents a novel environmental exposure matrix for exposure assessment and risk communication. Magnetic flux density levels (µT) were measured using a portable exposimeter carried in a backpack for the main ELF sources: railway power (16.7 Hz), domestic power (50 Hz), and tram ripple current (300 Hz). We collected ELF-MF levels between 2022 and 2024 in various environments representative of the Swiss population: 300 outdoor areas (e.g. city centres, residential areas), 245 public spaces (e.g. train stations, schools), 348 transport journeys (e.g. train, cars), and in 59 homes (e.g. bedrooms, living rooms). Over all environments, the highest ELF-MF exposure levels were measured in train stations (median: 0.48 µT), trains (median: 0.40 µT), and in living rooms near (<200 m) highest voltage lines of 220 kV and 380 kV (median: 0.37 µT). ELF-MF median levels measured two years apart showed high Pearson correlation coefficients in the same 150 outdoor areas (r = 0.88) and 86 public spaces (r = 0.87), without any significant changes. All measurements are well below the Swiss ambient regulatory limit based on the ICNIRP 1998 guidelines (median: 0.2 %). Finally, we derived an environmental exposure matrix and modelled 27 daily time-weighted average ELF-MF exposure scenarios by combining typical time spent at home, work and transport environments. People who do not live near highest voltage lines or work in highly exposed environments are typically exposed to less than 0.3 µT on average, while those who do are likely to exceed this level. This novel environmental exposure matrix is a useful tool for public communication and agent-based exposure modelling for future epidemiological research.

Exposure assessment and cytogenetic biomonitoring study of workers occupationally exposed to extremely low-frequency magnetic fields

Human cytogenetic biomonitoring (HCB) has long been used to evaluate the potential effects of work environments on the DNA integrity of workers. However, HCB studies on the genotoxic effects of occupational exposure to extremely low-frequency electromagnetic fields (ELF-MFs) were limited by the quality of the exposure assessment. More specifically, concerns were raised regarding the method of exposure assessment, the selection of exposure metrics, and the definition of exposure group. In this study, genotoxic effects of occupational exposure to ELF-MFs were assessed on peripheral blood lymphocytes of 88 workers from the electrical sector using the comet and cytokinesis-block micronucleus assay, considering workers' actual exposure over three consecutive days. Different methods were applied to define exposure groups. Overall, the summarized ELF-MF data indicated a low exposure level in the whole study population. It also showed that relying solely on job titles might misclassify 12 workers into exposure groups. We proposed combining hierarchical agglomerative clustering on personal exposure data and job titles to define exposure groups. The final results showed that occupational MF exposure did not significantly induce more genetic damage. Other factors such as age or past smoking rather than ELF-MF exposure could affect the cytogenetic test outcomes.

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

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 is remarkable. Specifically, machine learning (ML) and deep learning (DL) techniques in medicine have been increasingly adopted due to the growing abundance of health-related data, the improved suitability of such techniques for managing large datasets, and more computational power. ML and DL methodologies are fostering the development of new “intelligent” tools and expert systems to process data, to automatize human–machine interactions, and to deliver advanced predictive systems that are changing every aspect of the scientific research, industry, and society. The Italian scientific community was 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. To this end, we selected all the papers published in the last five years with at least one of the authors affiliated to an Italian institution that in the title, in the abstract, or in the keywords present the terms “machine learning” or “deep learning” and reference a medical area. We focused our research on journal papers under the hypothesis that Italian researchers prefer to present novel but well-established research in scientific journals. We then analyzed the selected papers considering different dimensions, including the medical topic, the type of data, the pre-processing methods, the learning methods, and the evaluation methods. As a final outcome, a comprehensive overview of the Italian research landscape is given, highlighting how the community has increasingly worked on a very heterogeneous range of medical problems.

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

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.

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

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.

Alzheimer disease starts in childhood in polluted Metropolitan Mexico City. A major health crisis in progress

Exposures to fine particulate matter (PM_2.5) and ozone (O₃) above USEPA standards are associated with Alzheimer's disease (AD) risk. Metropolitan Mexico City (MMC) youth have life time exposures to PM_2.5 and O₃ above standards. We focused on MMC residents ≤30 years and reviewed 134 consecutive autopsies of subjects age 20.03 ± 6.38 y (range 11 months to 30 y), the staging of Htau and ß amyloid, the lifetime cumulative PM_2.5 (CPM _2.5) and the impact of the Apolipoprotein E (APOE) 4 allele, the most prevalent genetic risk for AD. We also reviewed the results of the Montreal Cognitive Assessment (MoCA) and the brainstem auditory evoked potentials (BAEPs) in clinically healthy young cohorts. Mobile sources, particularly from non-regulated diesel vehicles dominate the MMC pollutant emissions exposing the population to PM_2.5 concentrations above WHO and EPA standards. Iron-rich,magnetic, highly oxidative, combustion and friction-derived nanoparticles (CFDNPs) are measured in the brain of every MMC resident. Progressive development of Alzheimer starts in childhood and in 99.25% of 134 consecutive autopsies ≤30 years we can stage the disease and its progression; 66% of ≤30 years urbanites have cognitive impairment and involvement of the brainstem is reflected by auditory central dysfunction in every subject studied. The average age for dementia using MoCA is 20.6 ± 3.4 y. APOE4 vs 3 carriers have 1.26 higher odds of committing suicide. PM_2.5 and CFDNPs play a key role in the development of neuroinflammation and neurodegeneration in young urbanites. A serious health crisis is in progress with social, educational, judicial, economic and overall negative health impact for 25 million residents. Understanding the neural circuitry associated with the earliest cognitive and behavioral manifestations of AD is needed. Air pollution control should be prioritised-including the regulation of diesel vehicles- and the first two decades of life ought to be targeted for neuroprotective interventions. Defining paediatric environmental, nutritional, metabolic and genetic risk factor interactions is a multidisciplinary task of paramount importance to prevent Alzheimer's disease. Current and future generations are at risk.