Electromagnetic Biology and Medicine最新文献

Brain tumors can cause difficulties in normal brain function and are capable of developing in various regions of the brain. Malignant tumours can develop quickly, pass through neighboring tissues, and extend to further brain regions or the central nervous system. In contrast, healthy tumors typically develop slowly and do not invade surrounding tissues. Individuals frequently struggle with sensory abnormalities, motor deficiencies affecting coordination, and cognitive impairments affecting memory and focus. In this research, Utilizing Phase-aware Composite Deep Neural Network Optimized with Coati Optimized Algorithm for Brain Tumor Identification Based on Magnetic resonance imaging (PACDNN-COA-BTI-MRI) is proposed. First, input images are taken from the brain tumour Dataset. To execute this, the input image is pre-processed using Multivariate Fast Iterative Filtering (MFIF) and it reduces the occurrence of over-fitting from the collected dataset; then feature extraction using Self-Supervised Nonlinear Transform (SSNT) to extract essential features like model, shape, and intensity. Then, the proposed PACDNN-COA-BTI-MRI is implemented in Matlab and the performance metrics Recall, Accuracy, F1-Score, Precision Specificity and ROC are analysed. Performance of the PACDNN-COA-BTI-MRI approach attains 16.7%, 20.6% and 30.5% higher accuracy; 19.9%, 22.2% and 30.1% higher recall and 16.7%, 21.9% and 30.8% higher precision when analysed through existing techniques brain tumor identification using MRI-Based Deep Learning Approach for Efficient Classification of Brain Tumor (MRI-DLA-ECBT), MRI-Based Brain Tumor Detection using Convolutional Deep Learning Methods and Chosen Machine Learning Techniques (MRI-BTD-CDMLT) and MRI-Based Brain Tumor Image Detection using CNN-Based Deep Learning Method (MRI-BTID-CNN) methods, respectively.

In this study, the genotoxic and histopathological effects of 6 GHz (0.065 W/kg) Radiofrequency-Electromagnetic Radiation (RF-EMR) on rat liver tissue were investigated. Sham (control) and Radiofrequency Radiation (RFR) groups were formed with 10 adult male rats in each group. Rats in the sham group received no treatment. Rats in the RFR group were exposed to 6 GHz RF-EMR for 4 h/day for 42 days. Immediately after the completion of the exposure, the rats in both groups were sacrificed and liver tissues were removed. Comet Test was performed to determine the genotoxic effect in the samples. Masson Trichrome and Hematoxylin Eosin staining methods were applied histopathologically. According to the Comet Analysis results, the genetic damage index (GDI) and damaged cell percentage (DCP) of the RFR group were higher than the sham group, but this difference was not statistically significant (p > 0.05). In histopathologic examinations, portal inflammation, single cell necrosis, vascularity and congestion were more prominent in the RFR group compared to the sham group. In our study, it was shown that 6 GHz RF-EMR can cause histopathologic and DNA level changes in rat liver tissue. As a result of the literature review, no prior studies have specifically examined the genotoxic and histopathological effects of 6 GHz RF-EMR. This makes our study important as it addresses the biological impacts of the 6 GHz frequency band.

The present research concentrates on examining entropy generation during the flow phenomenon of a three-dimensional peristaltic motion of a magnetized tri-hybrid nanofluid within a curved rectangular duct using a machine learning technique called backpropagated Levenberg-Marquardt (BLMT). The Carreau constitutive model is used for base liquid (blood). To obtain the most accurate solutions for the governing equations, an analytical tool called the Homotopy Perturbation Method (HPM) is utilized along with a machine learning methodology ANN-BLMT method on MatLab. The data of HPM and machine learning are also compared to assess how the framework of partial differential equations (PDEs) occurring in the problem can be improved. It shows the highest correlations between output and prediction of ANN-BLMT method. The convergence analysis reveals that for two scenarios, velocity exhibits the best validation performance values around $7.3117\times {10}^{-11}$ and $1.0082\times {10}^{-10}$. A detailed comparison between blood and nanofluid has been presented graphically to enhance the benefits of ternary hybrid nanoparticles in a simple base fluid. It is also found that the velocity of the blood can be slowed by the curvature increase and because of the increment of tri-hybrid nanoparticles in pure blood. It is also noted that the rate of heat transfer for ternary hybrid nanofluids is greater than that of a simple blood. Research findings have obvious implications for comprehending and enhancing peristaltic dynamics in biological processes such as the intestinal tract.

There is sufficient proof that static magnetic fields (SMFs) of different parameters have a significant effect on the cardiovascular system. The sometimes contradictory, opposite-directional nature of SMF's hemodynamic effect generates uncertainty; therefore, an explanation of the underlying mechanisms is required. Following SMF selective carotid baroreceptors or microvascular net exposure, both high and low blood pressure (BP)/vascular tone starting conditions showed a return to normal. Beyond the previous descriptions of SMF's simple hemodynamic results, the current study aims to clarify the physiology of the SMF BP/vascular tone normalizing effects. The examination of available literature and hemodynamic tracings provided strong evidence that mechanoreceptor magnetic activation is concealed behind SMF vascular tone adjustment (increasing or decreasing as needed), filling in the knowledge gap regarding SMF opposite directional vascular tone normalizing outcomes. It has been proposed that cytoskeletal actin filament rearrangement, mechanically-gated Ca²⁺ influx, and nitric oxide (NO) activity may strengthen SMF's vascular mechanoreceptor sensing/regulation ability, modifying BP and vascular tone features in a hemodynamic normalizing pattern. It is suggested that baro/mechanoreceptor magnetic activation physiology is a unique mechanism of the magneto-cardiovascular interaction with substantial potential for cardiovascular protection.

This study investigates the effects of pulsed electromagnetic field (PEMF) therapy on vagus nerve stimulation through non-invasive neck applications. Exploring the efficacy of PEMF across different frequencies (6 hz, 16 hz, and 32 hz), this double-blind placebo-controlled trial included 485 volunteers to assess its impact on autonomic nervous system functions, particularly targeting sleep disturbances and anxiety. Results demonstrated significant improvements in sleep quality and reduction in anxiety levels, especially notable at 16 hz. These findings suggest that PEMF therapy, by modulating autonomic activity, offers a beneficial non-pharmacological treatment option for related disorders.

Hyperthermia is a non-invasive localized heating technique that has proven to be an efficient cancer treatment method. Hyperthermia therapy needs precise temperature control to ensure delivery of the proper thermal dose, causing minimum damage to the neighboring healthy tissues. This work reports the indigenous development of a custom-designed hyperthermia instrument. An advanced RISC machine (ARM)-based embedded closed-loop proportional-integral (PI) controller is developed for controlling the temperature. As per the applied methodology, the DC bias of a Mazzilli oscillator-based half-bridge inverter is controlled through the controller. The PI controller reads the hyperthermia system temperature using an infrared (IR) radiation thermometer and generates an analog output accordingly. This, in turn, changes the amplitude of the alternating magnetic field (AMF), thus controlling the temperature of the magnetic nanoparticles (MNPs). Its potential has been explored for in vitro hyperthermia studies. In vitro experiments have been carried out successfully with the custom-designed heater and controller assembly utilizing commercial non-invasive temperature measurement with a standard deviation of about 0.3°C and overshoot within the hyperthermia temperature range (3°C). The developed system has also obtained a satisfactory value of specific absorption rate (SAR). This paper infers the feasibility of the indigenously developed circuit and the related controller for hyperthermia therapy and preclinical studies. This system can be used for clinical applications with suitable customizations.

Subject-independent emotion detection using EEG (Electroencephalography) using Vibrational Mode Decomposition and deep learning is made possible by the scarcity of labelled EEG datasets encompassing a variety of emotions. Labelled EEG data collection over a wide range of emotional states from a broad and varied population is challenging and resource-intensive. As a result, models trained on small or biased datasets may fail to generalize well to unknown individuals or emotional states, resulting in lower accuracy and robustness in real-world applications. A Node-Level Capsule Graph Neural Network (NCGNN) is then used to correctly recognize emotions like calm, happy, sad, and furious based on the features that have been collected. Generally speaking, the NCGNN classifier does not provide optimization techniques for adjusting parameters to ensure precise emotion recognition. Hence, propose to utilize the Piranha Foraging Optimization Algorithm (PFOA) to enhance Node-Level Capsule Graph Neural Network, accurately categorize the emotion level. Then, the proposed NLCGNN-SIER-EEG is excluded in Python and the performance metrics like Recall, Accuracy, Precision, Specificity, F1 score and RoC. In the end, the performance of NLCGNN-SIER-EEG technique provides 19.57%, 24.37% and 34.15% high accuracy, 22.12%, 26.82% and 28.52% higher Precision and 23.26%, 28.17% and 29.43% higher recall while compared with existing like Subject-independent emotion recognition based on EEG data using VMD and deep learning (SIER-EEG-VMD-DL), Emotion recognition system based on two-level ensemble of deep-convolutional neural network models (ERS-TLE-DCNN), and human emotion recognition based on EEG data using principal component analysis and artificial neural networks (EEH-HER-ANN), respectively.

There is a long-standing debate about the relationship between Radio Frequency Electromagnetic Field (RF-EMF) exposure and fatigue. Past studies primarily rely on self-report scales to assess fatigue, but these methods are often susceptible to personal biases. Notably, the role of psychological factors in the fatigue response induce by RF-EMF exposure remains unclear. Therefore, our study focuses on exploring the impact of 5 G signal exposure on human fatigue, particularly considering the influence of expectancy induced by psychological priming on the outcomes. In this study, we recruited 21 healthy subjects who were tested in three sessions. Each session included two 30-min exposures to either real or sham 5 G signals, with the order randomized. The experiment was conducted under varying informational conditions: subjects were provided with correct, false, or no information about the order of exposure. Additionally, subjects completed a fatigue scoring questionnaire and underwent Electroencephalogram (EEG) measurements during the experiment. The statistical comparison indicates that 5 G RF-EMF exposure at routine levels does not lead to changes in EEG power. The finding reveals that the report of fatigue can be altered by the conveyed information of being exposed by 5 G signals although there is no real exposure and no detectable electrophysiological indicator. Our findings suggest that it is necessary to prevent psychological priming in any kind or to take its possible consequence into consideration, to reveal this effect of RF-EMF exposure.

The rising prevalence of cardiac diseases necessitates advanced IoT-driven health monitoring systems for early detection and diagnosis. This study presents an efficient ECG-based cardiac disease prediction framework leveraging a multi-phase approach to enhance computational efficiency and classification accuracy. The Convolutional Lightweight Deep Auto-encoder Wiener Filter (CLDAWF) is employed for signal preprocessing, while the Quantized Discrete Haar Wavelet Transform (QD-HWT) extracts critical cardiac features, including P-wave fluctuations, QRS complex, and T-wave intervals. These refined features are classified using an optimized Epistemic Neural Network (ENN), whose parameters are fine-tuned via the Boosted Sooty Tern Optimization algorithm, improving accuracy and reducing system loss. The proposed model achieves 99.65% accuracy, demonstrating its effectiveness in real-time cardiac disease monitoring and offering a scalable, high-performance solution for IoT-based healthcare systems.

This article explores the relationship between electromagnetic fields (EMF) and biological systems, focusing on the influence of extremely low-frequency electromagnetic frequencies (ELF), particularly Schumann's resonance (SR) at 7.83 hz. Cells and proteins may have evolved to take advantage of frequencies naturally present in the Earth's EMF, potentially enhancing cellular energy levels and affecting resting membrane potential (RMP). Thus, changes in or absence of SR may have adverse effects on the functioning of the whole organism. Bioelectricity, independent of genes, has been shown to modulate health, suggesting the potential for using controlled application of EMF frequencies in treating certain types of cancer or conditions affecting the RMP. Research indicates that human brainwave activity is highly dependent on the SR, implying a correlation between atmospheric electromagnetic frequencies and brain activity. ELF, including SR, appears to modulate cellular calcium influx/efflux, likely via indirect mechanisms involving field-sensitive molecules or radical pairs that affect ion channel behavior which plays a critical role in cell signaling and regulation of various processes. It can also trigger a cascade of molecular events that ultimately lead to the generation of action potentials, affecting consciousness and behavior. The influence of atmospheric electromagnetic frequencies on human brainwave activity, modulation of cellular calcium influx/efflux, and potential effects on cellular energy levels and RMP highlight the significance of ELF in biological systems. However, further research is required to fully understand these mechanisms and their implications for human health and well-being.