Electromagnetic Biology and Medicine最新文献

In cardiovascular research, electromagnetic fields generated by Riga plates are utilized to study or manipulate blood flow dynamics, which is particularly crucial in developing treatments for conditions such as arterial plaque deposition and understanding blood behavior under varied flow conditions. This research predicts the flow patterns of blood enhanced with gold and maghemite nanoparticles (gold-maghemite/blood) in an electromagnetic microchannel influenced by Riga plates with a temperature gradient that decays exponentially, under sudden changes in pressure gradient. The flow modeling includes key physical influences like radiation heat emission and Darcy drag forces in porous media, with the flow mathematically represented through unsteady partial differential equations solved using the Laplace transform (LT) method. Results, including shear stress (SS) and rate of heat transfer (RHT), are graphically detailed, demonstrating changes in blood velocity profile with modifications in the Hartmann number and the width of electrodes, and differences in temperature and RHT between hybrid nano-blood (HNB) and nano-blood (NB). The key results indicate an increase in blood velocity distribution with higher modified Hartmann number, and a decrease with wider electrodes. Temperature is elevated in both hybrid nano-blood (HNB) and nano-blood (NB). Notably, HNB with gold and maghemite enhances heat transmission in the flow. Furthermore, an artificial intelligence-driven methodology employing an artificial neural network (ANN) has been incorporated to facilitate rapid and precise evaluations of SS and RHT, demonstrating remarkable predictive accuracy. The proposed algorithm exhibits outstanding accuracy, achieving 99.998% on the testing dataset and 96.843% during cross-validation for predicting SS, and 100% on the testing dataset, and 95.008% during cross-validation for predicting RHT. The implementation of nanotechnology with artificial intelligence promises new tools for doctors and surgeons, potentially transforming patient care in fields such as oncology, cardiology, and radiology. This model also facilitates the generation of precise electromagnetic fields to guide drug-loaded magnetic nanoparticles for applications in targeted drug delivery, hyperthermia treatment, MRI contrast enhancement, blood flow monitoring, cancer treatment, and controlled drug release.

Nuclear electromagnetic pulse (NEMP) as a public hygiene issue has aroused increasing attention. Recent research has demonstrated that NEMP can disrupt the male reproductive system. Molecular hydrogen, a selective hydroxyl radical scavenger, has been shown to have the protective effects against many diseases closely associated with oxidative damage. We sought to characterize the beneficial effects of molecular hydrogen on the male reproductive cells. GC-1 spermatogonial cells and TM-3 Leydig cells, two well-established male reproductive cell lines, were exposed to NEMP. Finally, we employed transcriptomic sequencing to explore the transcriptional changes in male reproductive cells induced by NEMP exposure. For the intervention, cells were incubated in hydrogen gas (H₂ 75%, O₂ 20%, and CO₂ 5%) for 1 h. NEMP exposure induced damage to both GC-1 and TM-3 cells, resulting in decreased cell viability and increased apoptosis rates. However, intervention with molecular hydrogen significantly mitigated this damage. Specifically, molecular hydrogen reduced the production of ROS and restored mitochondrial function, thereby alleviating oxidative stress and apoptosis. Transcriptomic sequencing analysis showed that NEMP affected the molecular function term antioxidant activity, particularly Gstp2, a gene predicted to be located in the mitochondria to promote glutathione transferase activity. Hence, molecular hydrogen is a promising protective agent against NEMP in the mechanism that other antioxidants cannot be available.

The global expansion of 5 G communication networks has heightened concerns about the biological effects of high-frequency radiofrequency (RF) radiation, particularly on endocrine organs such as the thyroid gland. This study investigated the effects of 3.5 GHz RF radiation on thyroid hormone levels and oxidative stress markers in male Wistar rats and assessed the potential protective role of quercetin, a natural antioxidant. Twenty-eight rats were randomly assigned to four groups: Sham, RF, Quercetin, and RF + Quercetin. RF exposure was administered at 3.5 GHz (2 W) for 2 hours/day, 5 days/week, for 30 days. Quercetin (20 mg/kg) was administered intraperitoneally. Serum levels of T3, T4, and TSH, as well as thyroid tissue levels of TAS, TOS, GSH, and MDA, were analyzed using ELISA. RF exposure significantly decreased T3 and T4, increased TSH, elevated MDA and TOS, and reduced TAS and GSH levels. Quercetin treatment showed trends toward reversing some of these effects, although not all changes reached statistical significance. SAR simulations confirmed higher energy absorption in the thyroid region (average SAR: 1.128 W/kg). These findings suggest that 3.5 GHz RF radiation may impair thyroid function and redox homeostasis, and that quercetin may exert limited biochemical protection, though further studies are needed to confirm its efficacy. Further long-term molecular studies are warranted to elucidate the mechanisms involved.

Previous studies indicated that magnetic fields (MF) could alter the conformation of membrane proteins such as some drug influx transporters and affect their function. In this study, we focused on two mutants of npr-1 receptor gene related to feeding behavior in Caenorhabditis elegans (C. elegans) to investigate the effect of 60 Hz, 50 mT MF on a receptor in neurotransmission. The wild-type laboratory strain Bristol N2 in C. elegans forages solitary on the E. coli lawn, whereas npr-1 mutant of N2 aggregate on food and take on social feeding behavior because of its altered function. Under MF exposure, the number of aggregates exhibiting social feeding behavior on two mutants, IM222 and DA609, was decreased by 40% and 25%, respectively. Meanwhile, npr-1 is not involved in the behavior to attractant diacetyl in C. elegans, and the moving speed and reach rate to attractant in two mutants was not affected by MF exposure for up to 90 min. The results indicate that MF can affect the altered function of NPR-1 in mutant strains and partially change the feeding behaviour of the npr-1 mutants from social to solitary. Furthermore, considering the position of these amino acid substitutions in NPR-1 mutant receptors, the extracellular parts of membrane protein may be more affected by MF than the transmembrane part.

Anxiety is an adaptive condition characterized by heightened uneasiness, which in the long term can cause complications such as reducing the quality of life and problems related to the mental and physical health. Concerns have been raised regarding the potential dangers of extremely low frequency electromagnetic fields (ELF-EMF) ranging from 3 to 3000 Hz, which are omnipresent in our daily lives and there have been studies about the anxiogenic effects of these fields. Studies conducted in this specific area has revealed that ELF-EMF can have an impact on various brain regions, such as the hippocampus. In conclusion, studies have shown that ELF-EMF can interfere with hippocampus-prefrontal cortex pathway, inducing anxiety behavior. Also, ELF-EMF may initiate anxiety behavior by generating oxidative stress in hypothalamus and hippocampus. Moreover, ELF-EMF may induce anxiety behavior by reducing hippocampus neuroplasticity and increasing the NMDA2_A receptor expression in the hippocampus. Furthermore, supplementation with antioxidants could serve as an effective protective measure against the adverse effects of FLF-FMF in relation to anxiety behavior.

The electromagnetic waves of 2.45 GHz microwave frequency have become abundant in environments worldwide. This study assessed the short-term impact of low-intensity 2.45 GHz exposure on young Arabidopsis thaliana plants. The plants underwent a 48-hour exposure to continuous wave 2.45 GHz microwaves at a power density of 1.0 ± 0.1 W m^-2. Experiments were conducted inside anechoic chambers. After the microwave exposure samples were subjected to morphological, genotoxicity, pigmentation, and physiochemical analysis. Microwave exposure elevated the levels of photosynthetic pigments, oxidative stress, guaiacol peroxidase activity, and ascorbic peroxidase activity in plants. Conversely, catalase activity decreased. Photosystem efficiency remained unchanged, while non-photochemical quenching increased. Leaf morphological parameters exhibited no significant alterations during this brief exposure period. Notably, despite shifts in physiological parameters and pigmentations, genomic template stability remained unaffected. The findings suggest that the non-thermal effects of microwave exposure influence the photosystem and plant physiology. Research confirmed the existence of non-thermal effects of microwave exposure; however, these effects are within tolerable limits for Arabidopsis thaliana plants.

Human being's biological processes and psychological activities are jointly connected to the brain. So, the examination of human activity is more significant for the well-being of humans. There are various models for brain activity detection considering neuroimaging for attaining decreased time requirement, increased control commands, and enhanced accuracy. Motor Imagery (MI)-based Brain-Computer Interface (BCI) systems create a way in which the brain can interact with the environment by processing Electroencephalogram (EEG) signals. Human Activity Recognition (HAR) deals with identifying the physiological activities of human beings based on sensory signals. This survey reviews the different methods available for HAR based on MI-EEG signals. A total of 50 research articles based on HAR from EEG signals are considered in this survey. This survey discusses the challenges faced by various techniques for HAR. Moreover, the papers are assessed considering various parameters, techniques, publication year, performance metrics, utilized tools, employed databases, etc. There were many techniques developed to solve the problem of HAR and they are classified as Machine Learning (ML) and Deep Learning (DL)models. At last, the research gaps and limitations of the techniques were discussed that contribute to developing an effective HAR.

In this study, we investigated the inhibitory effects of radiofrequency exposure on RANKL-induced osteoclast differentiation in RAW264.7 cells, along with the underlying mechanisms. RAW264.7 cells were subjected to radiofrequency exposure at three distinct power densities: 50 µW/cm², 150 µW/cm², and 450 µW/cm². The results showed that, among the three dosage levels, exposure to 150 µW/cm² of radiofrequency radiation significantly reduced the proliferation capacity of RAW264.7 cells. RF exposure at three power densities resulted in significant increases in the level of osteoclast apoptosis and notable decreases in osteoclast differentiation. Notably, the most pronounced effects on apoptosis, differentiation in RAW 264.7 cells were observed at the 150 µW/cm² power density. These effects were accompanied by concurrent decreases in mRNA and protein levels of osteoclast-specific genes, including RANK, NFATc1, and TRACP. Furthermore, radiofrequency exposure at power density of 150 µW/cm² induced a significant decrease in cytoplasmic NF-κB protein levels while increasing its nuclear fraction, thereby counteracting the effects of RANKL-induced NF-κB activation. These data suggest that radiofrequency exerts inhibitory properties on RANKL-induced NF-κB transcriptional activity, subsequently indirectly suppressing the expression of downstream NF-κB target genes, such as NFATc1 and TRACP. In conclusion, our study demonstrates that radiofrequency radiation effectively inhibits osteoclast differentiation by modulating the NF-κB signaling pathway. These findings have important implications for potential therapeutic interventions in osteoporosis.

The brain is a crucial organ that controls the body's neural system. The tumor develops and spreads across the brain as a result of irregular cell generation. The provision of substantial treatment to patients requires the early diagnosis of malignancies. However, timely diagnosis and accurate classification were difficult in the conventional models. Thus, the Taylor Fire Hawk optimization (TFHO) is implemented here for effective segmentation and classification. The TFHO is the merging of the Taylor series and Fire Hawk Optimizer (FHO). The de-noising is accomplished by the adaptive median filter, and the segmentation is carried out using M-Net, which has been trained by TFHO. Subsequently, image augmentation is performed to increase the image dimension, followed by the extraction of effective features. Finally, DenseNet is used for the classification, and the training is done by TFHO. The introduced method obtained 94.86% accuracy, 92.83% Negative Predictive Values, 89.33% Positive Predictive Values (PPV), 95.91% True Positive Rate (TPR), 4.37% False Negative Rate (FNR), and 90.98% F1-score.

Brain tumors present a formidable diagnostic challenge due to their aberrant cell growth. Accurate determination of tumor location and size is paramount for effective diagnosis. Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) are pivotal tools in clinical diagnosis, yet tumor segmentation within their images remains challenging, particularly at boundary pixels, owing to limited sensitivity. Recent endeavors have introduced fusion-based strategies to refine segmentation accuracy, yet these methods often prove inadequate. In response, we introduce the Parallel-Way framework to surmount these obstacles. Our approach integrates MRI and PET data for a holistic analysis. Initially, we enhance image quality by employing noise reduction, bias field correction, and adaptive thresholding, leveraging Improved Kalman Filter (IKF), Expectation Maximization (EM), and Improved Vibe Algorithm (IVib), respectively. Subsequently, we conduct multi-modality image fusion through the Dual-Tree Complex Wavelet Transform (DTWCT) to amalgamate data from both modalities. Following fusion, we extract pertinent features using the Advanced Capsule Network (ACN) and reduce feature dimensionality via Multi-objective Diverse Evolution-based selection. Tumor segmentation is then executed utilizing the Twin Vision Transformer with dual attention mechanism. Implemented our Parallel-Way framework which exhibits heightened model performance. Evaluation across multiple metrics, including accuracy, sensitivity, specificity, F1-Score, and AUC, underscores its superiority over existing methodologies.