A meta-analysis of in vitro exposures to weak radiofrequency radiation exposure from mobile phones (1990–2015)

Halgamuge, Malka N.; Skafidas, Efstratios; Davis, Devra Lee

doi:10.1016/j.envres.2020.109227

Cited by 27 publications

(28 citation statements)

References 331 publications

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“…The data employed in this analysis have been shown in our recent study (Tables 11-42, Halgamuge et al, 2020 [41]) that extracted high levels of understanding from raw data using different classification algorithms and performance evaluation methods. The collected dataset comprises of five attributes of RF-EMF and 1127 experimental case studies or instances, such as: species (human and animal cells/tissue), frequency of weak RF-EMF, SAR, exposure durations, and cellular response (presence or absence).…”

Section: Data Collectionmentioning

confidence: 99%

“…The impacts of past exposures and conditions can be shown in clinical or epidemiological studies. In contrast, in-vivo and in-vitro studies can be used to predict and eventually limit impacts from arising in the future [41]. Nevertheless, it cannot be expected that humans similarly react to RF-EMF as do cell cultures or animals.…”

Section: Introductionmentioning

confidence: 99%

“…Our recent meta-analysis [41] cross-examined published experiments that considered the non-thermal RF-EMF exposure effects (cytogenetic, gene, and protein expression analysis) on cell types with various doubling times, including lymphocytes, epithelial, endothelial, and spermatozoa from rat, mouse, and humans. Our investigation revealed that 45.3% of experiments concluded that an expansion in such potential has an effect on cells exposed to RF radiation, while 54.7% concluded that no such effects (p = 0.001) are observed.…”

Section: Introductionmentioning

confidence: 99%

“…There is extensive clinical and epidemiological proof [41] to propose that even low degrees of radiofrequency may cause harmful consequences for the functioning of cells. Two such significant epidemiological investigations are: population-based cohorts followed for a longer time, and case-control investigations analyzing precise cases of disease and matched controls that do not have the condition [41].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Data Collectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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 global use of these devices has launched much concern and argument on the possible effects of this kind of daily life radiation on human health and wellbeing through research on biological model systems in vivo, ex vivo or in vitro, as summarized recently by Lloyd Morgan, Lennart Hardell and associates [1]. In vitro studies in general have shown by meta-analysis in 300 peer-reviewed scientific publications describing 1127 experimental observations in cell-based in vitro models that cellular response varies with signal properties [2]. Recent articles elaborate on the parameters of real world PTS exposure [3][4][5] but generally, it has been demonstrated that PTS from various domestic sources affects normal functions [6].…”

Section: Introductionmentioning

confidence: 99%

Pulsed Telecommunication Signals of Non-ionizing Radiation Affect Amyloid Precursor Protein and α-Synuclein Metabolism in Non-neural Human Cells

Stefi¹,

Skouroliakou²,

Margaritis³

et al. 2020

Preprint

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The expanding use of devices emitting Pulsed Telecommunication Signals (PTS) has launched a serious debate over the possible effects of electromagnetic radiation (EMR) on living organisms. Our previous work has indicated that PTS exposure alters Amyloid Precursor Protein (APP) and alpha-synuclein (α-syn) metabolism in human cells of neural origin, providing a possible connection between exposure and neurodegeneration. This investigation aimed to reveal, in vitro in human non-neural cells (HEK293), the aftermath of the same exposure on the processing of APP and α-syn. Data presented here, indicate changes in APP metabolism, acquisition of different cellular topologies of the newly generated APP fragments, changes in monomeric α-syn accumulation and multimerization, indicating that APP and α-syn processing is possibly altered in the periphery by EMR. These effects are accompanied by a substantial increase in the levels of Reactive Oxygen Species (ROS). Further investigation is required in order to provide insights into the interaction of PTS with non-neural cells affecting the peripheral systemic functional stability. This is necessary because nowadays whole body human exposure from various EMR sources is a fact in normal life with the valid estimation that they may be increased in view of the forthcoming 5G telecommunications network implementation.

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Ethical Considerations of Wearable Technologies in Human Research

Gao

2021

Adv Healthcare Materials

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Wearable technologies hold great promise for disease diagnosis and patient care. Despite the flourishing research activities in this field, only a handful of wearable devices are commercialized and cleared for medical usage. The successful translation of current proof‐of‐concept prototypes requires extensive in‐human testing. There is a lag between current standards and operation protocols to guide the responsible and ethical conduct of researchers in such in‐human studies and the rapid development of the field. This essay presents relevant ethical concerns in early‐stage human research from a researcher's perspective.

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A meta-analysis of in vitro exposures to weak radiofrequency radiation exposure from mobile phones (1990–2015)

Cited by 27 publications

References 331 publications

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

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

Pulsed Telecommunication Signals of Non-ionizing Radiation Affect Amyloid Precursor Protein and α-Synuclein Metabolism in Non-neural Human Cells

Ethical Considerations of Wearable Technologies in Human Research

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