Bioelectromagnetics最新文献

Short-dipole diode sensors loaded with highly resistive lines are commonly used to measure the time-averaged square of the high-frequency electromagnetic field amplitude directly. Their precision, simplicity, broadband, high dynamic range capability, and minimal scattering make them ideal for application in the near-field of sources, particularly for demonstrating compliance with exposure limits. However, the usage of these sensors to cover multiple orders of magnitude of field amplitude requires signal-specific linearization of the sensor response. Traditionally, linearization had been performed for each signal or modulation by measurement and, more recently, by simulations based on a calibrated sensor model. These approaches have become prohibitively expensive with the launch of the fifth generation of mobile communication (5G), which added thousands of diverse and complex modulation schemes. In response to these challenges, we first developed an innovative approach to accelerate sensor model simulations with an enhancement of accuracy, which allows us to subsequently establish a data set comprising a large number of probe parameters and signal characteristic configurations. Subsequently, a physics-informed neural network (PINN) was trained with readily accessible signal characteristics to obtain on-the-fly linearization parameters with acceptable uncertainties across the relevant dynamic range. In contrast to traditional artificial intelligence (AI) models that predominantly rely on pattern recognition from precomputed data, our approach ensures that the model captures the intrinsic relationships and system dynamics inherent to the physical phenomena under study. Our AI-based approach achieves an error below 0.4 dB at peak specific absorption rate (SAR) values of up to $��>��200��{��\text{W kg}��}^{�-�1�}�$. In addition, AI accelerates the determination of linearization parameters by a factor $��>�$ 34,000$��\times �$

The introduction of 5G networks has raised public concerns about potential changes in environmental electromagnetic field (EMF) exposure. This study analyzes continuous monitoring data collected over 2 years (August 2022–October 2024) from 13 frequency-selective monitoring sensors located in Greece's five largest cities. Focusing on the 3.6 GHz band, we evaluated trends and weekly variations in EMF levels. Results indicated a gradual increase in EMF exposure at 3.6 GHz, driven by the growing penetration of 5G infrastructure and devices. Notably, this band exhibited higher maximum-to-median power density ratios compared to other frequency bands, attributable to active antenna systems' characteristics and traffic variations. Applying the ICNIRP 2020 guidelines, we found that 30-min averaged values significantly reduced these variations. All measured EMF levels, including maximum values, remained well below Greek and international safety limits. These findings, especially the increasing trend identified for the EMF levels, underscore the importance of continuous monitoring networks for assessing EMF exposure to existing and emerging telecommunications networks and ensuring compliance with safety standards.

The rapid deployment of 5G wireless communication has amazingly accelerated global connectivity, marking a significant milestone in how we interact with technology and with each other. This next-generation network promises to revolutionize industries by delivering faster data speeds, drastically reducing latency, and providing the capacity to support a vast and growing ecosystem of interconnected devices. From smart cities and autonomous vehicles to advanced healthcare applications and immersive virtual reality experiences, 5G is poised to be the backbone of a hyper-connected world.

However, the swift and widespread rollout of 5G has not been without controversy. Alongside the excitement over its potential, significant concerns have emerged regarding its potential impact on human health. These concerns stem from the increased exposure to electromagnetic fields (EMFs) associated with 5G technology, particularly as it operates on higher frequency bands, including millimeter waves. Consequently, given the lack of publications concerning the effects of frequencies implemented for 5G (3.5–26 GHz) for the general public, more in-depth studies need to be established due to the increased debates and inconclusive reports about the subject.

Given that 5G is a relatively new technology, short- and long-term studies are still in progress to assess its health implications comprehensively. For this purpose, the European Union Commission via their institutions has launched a call for proposals in the environmental health topic (HORIZON-HL-TH-2021-ENVHLTH-02). This program was implemented to answer to the public concern about the health effect of 5G exposure. The total amount of funding was 30 million euros from Horizon Europe 2021–2027. The results should fill the current knowledge gaps on the effects of wireless technologies on health and the environment. Four projects funded by Horizon Europe have been brought together under the CLUE-H network, involving more than 60 European research organizations across four research consortia: ETAIN, GOLIAT, NextGEM, and SEAWave. Additionally, scientists from outside Europe, including the USA, Korea, and Japan, are also collaborating on these projects.

The rapid deployment of 5G brings unprecedented opportunities for technological innovation but also necessitates thorough and ongoing risk assessment regarding its potential health impacts. While current scientific consensus generally supports the safety of 5G under existing guidelines, the evolving nature of the technology, coupled with the long-term uncertainty, underscores the importance of continued research, transparent communication, and adaptive regulatory frameworks. As 5G becomes more ubiquitous, balancing its benefits with precautionary health measures will be crucial to ensuring public trust and safety.

The authors declare no conflicts of interest.

Idiopathic Environmental Intolerance Attributed to Electromagnetic Fields (IEI-EMF) is a syndrome that defines people who report symptoms that they attribute to their exposure to EMF sources, without any identified underlying medical condition to explain these symptoms. To date, provocation protocols have failed to demonstrate a consistent relationship between EMF exposure and reported symptoms, raising questions among some researchers and individuals with IEI-EMF about the relevance of these protocols for studying the syndrome. To address these criticisms, a provocation protocol was co-designed in collaboration with individuals with IEI-EMF. This study presents the results of the tests, with a focus on exposure perception and symptom reporting among IEI-EMF volunteers. A total of 47 IEI-EMF volunteers were enrolled and participated in an open-field habituation session. Of these, 27 completed the first double-blind controlled exposure session, while 26 and 16 volunteers, respectively, participated in three sessions for collective analyses and 12 sessions for individual-level analyses. At the individual level, no consistent association was found between exposure perception certainty level and exposure status, except for one volunteer whose perception was mostly consistent with exposure status. Similarly, symptom reporting did not align with exposure status, except for the same volunteer, whose symptom reporting showed a borderline significant result with exposure status. However, for half of the volunteers, symptom reporting was significantly correlated with exposure perception certainty level, supporting a nocebo hypothesis. At the collective level, no consistency was observed between exposure perception certainty level, symptom reporting, and exposure status. This study discusses the conditions necessary for future provocation protocols to enhance their relevance, acceptability, and potential utility in a possible care-oriented approach. It also considers criticisms of using exposure perception and symptom reporting as outcomes in provocation protocols, despite their central role in how individuals identify themselves as individuals with IEI-EMF.

Neuromodulation with low-intensity focused ultrasound (LIFUS) holds significant promise for noninvasive treatment of neurological disorders, but its success relies heavily on accurately targeting specific brain regions. Computational model predictions can be used to optimize LIFUS, but uncertain acoustic tissue properties can affect prediction accuracy. The Monte Carlo method is often used to quantify the impact of uncertainties, but many iterations are generally needed for accurate estimates. We studied a surrogate model based on polynomial chaos expansion (PCE) to quantify the uncertainty in the LIFUS acoustic intensity field caused by tissue acoustic property uncertainties. The PCE approach was benchmarked against Monte Carlo method for LIFUS in three different head models. We also investigated the effect of the number of PCE samples on the accuracy of the surrogate model. Our results show that the PCE surrogate model requires only 20 simulation samples to estimate the mean and standard deviation of the acoustic intensity field with high accuracy compared to 100 samples needed for Monte Carlo method. The root mean squared percentage error (RMSPE) in the mean acoustic intensity field was less than 1.5%, with a maximum error of less than 0.5 W/cm² (< 1% of the focus peak intensity in water), while the RMSPE in the standard deviation was less than 9%, with a maximum error of less than 0.3 W/cm². The accuracy of the PCE surrogate model, and the limited number of iterations it requires makes it a promising tool for quantifying the uncertainty in the acoustic intensity field in LIFUS applications.

Temporal interference stimulation (TIS) is a new form of transcranial electrical stimulation (tES) that has been proposed as a method for targeted, non-invasive stimulation of deep brain structures. While TIS holds promise for a variety of clinical and non-clinical applications, little data is yet available regarding its effects in humans and its mechanisms of action. In order to inform the design and safe conduct of experiments involving TIS, researchers require quantitative guidance regarding safe exposure limits and other safety considerations. To this end, we undertook a two-part effort to determine frequency-dependent thresholds for applied currents below which TIS is unlikely to pose risk to humans in terms of heating or unwanted stimulation. Part I of this effort, described here, comprises a summary of the current knowledge pertaining to the safety of TIS and related techniques. Specifically, we provide: i) a broad overview of the electrophysiological impacts neurostimulation, ii) a review of the (bio-)physical principles underlying the mechanisms of action of transcranial alternating/direct stimulation (tACS/tDCS), deep brain stimulation (DBS), and TIS, and iii) a comprehensive survey of the adverse effects (AEs) associated with each technique as reported in the scientific literature and regulatory and clinical databases. In Part II, we perform an in silico study to determine field exposure metrics for tDCS/tACS and DBS under normal (safe) operating conditions and infer frequency-dependent current thresholds for TIS that result in equivalent levels of exposure.