Electromagnetic Biology and Medicine最新文献

This paper presents the findings of a comprehensive study exploring the synergistic effects arising from the combination of microwave ablation and pulsed electromagnetic field (PEMF) therapy on prostate cancer cells. The research encompassed five distinct experimental groups, with continuous electric field measurements conducted during the entire treatment process. Group 1 and Group 2, subjected to microwave power below 350 W, exhibited specific electric field values of 72,800 V/m and 56,600 V/m, respectively. In contrast, Group 3 and Group 4, exposed to 80 W microwave power, displayed electric field levels of approximately 1450 V/m, while remaining free from any observable electrical discharges. The migratory and invasive capacities of PC3 cells were assessed through a scratch test in all groups. Notably, cells in Group 3 and Group 4, subjected to the combined treatment of microwave ablation and PEMF, demonstrated significantly accelerated migration in comparison to those in Groups 1 and 2. Additionally, Group 5 cells, receiving PEMF treatment in isolation, exhibited decreased migratory ability. These results strongly suggest that the combined approach of microwave ablation and PEMF holds promise as a potential therapeutic intervention for prostate cancer, as it effectively reduced cell viability, induced apoptosis, and impeded migration ability in PC3 cells. Moreover, the isolated use of PEMF demonstrated potential in limiting migratory capacity, which could hold critical implications in the fight against cancer metastasis.

This research focuses on improving the detection and classification of brain tumors using a method called Brain Tumor Classification using Dual-Discriminator Conditional Generative Adversarial Network (DDCGAN) for MRI images. The proposed system is implemented in the MATLAB programming language. In this study, images of the brain are taken from a dataset and processed to remove noise and enhance image quality. The brain pictures are taken from Brats MRI image dataset. The images are preprocessed using Structural interval gradient filtering to remove noises and improve the quality of the image. The preprocessing outcomes are given to feature extraction. The features are extracted by Empirical wavelet transform (EWT) and the extracted features are given to the Dual-discriminator conditional generative adversarial network (DDCGAN) for recognizing the brain tumor, which classifies the brain images into glioma, meningioma, pituitary gland, and normal. Then, the weight parameter of DDCGAN is optimized by utilizing Border Collie Optimization (BCO), which is a met a heuristic approach to handle the real world optimization issues. It maximizes the detection accurateness and reduced computational time. Implemented in MATLAB, the experimental results demonstrate that the proposed system achieves a high sensitivity of 99.58%. The BCO-DDCGAN-MRI-BTC method outperforms existing techniques in terms of precision and sensitivity when compared to methods like Kernel Basis SVM (KSVM-HHO-BTC), Joint Training of Two-Channel Deep Neural Network (JT-TCDNN-BTC), and YOLOv2 including Convolutional Neural Network (YOLOv2-CNN-BTC). The research findings indicate that the proposed method enhances the accuracy of brain tumor classification while reducing computational time and errors.

This paper presents data on pain perception in rats exposed to 6 GHz radiofrequency electromagnetic radiation (RF-EMR). Rats were divided into two groups: control (n = 10, 4 replicates per test) and RF-EMR exposed group (n = 10, 4 replicates per test). Nociceptive responses of the groups were measured using rodent analgesiometry. Rats were divided into control and RF-EMR exposed groups. Nociceptive responses were measured using rodent analgesiometry. RF-EMR exposed rats had a 15% delay in responding to hot plate thermal stimulation compared to unexposed rats. The delay in responding to radiant heat thermal stimulation was 21%. We determined that RF-EMR promoted the occurrence of pressure pain as statistical significance by + 42% (p < 0.001). We observed that RF-EMR exposure increased nociceptive pain by + 35% by promoting cold plate stimulation (p < 0.05). RF-EMR exposure did not affect thermal preference as statistical significance but did support the formation of pressure pain perception.

Electromagnetic radiation (EM) pollution has a certain impact on human life and health, and the reconstruction of the EM space field in this paper is of great practical significance for EM analysis and research. The radial basis function (RBF) sufficiently considers the influence of each sampling point and is more suitable for reconstructing the EM space field than other spatial interpolation methods. Currently, when RBF is used to reconstruct the EM space field, the optimal determination of the basis function and shape parameter (SP) is rarely considered. This ultimately leads to low reconstruction accuracy of the EM space field. Therefore, in this paper, the particle swarm optimization (PSO) is used to calculate the optimal SP of the RBF. On this basis, reliable EM space field reconstruction is performed, which helps people understand the EM distribution characteristics in actual situations from a visual perspective. The EM sampling data of a region on the Yunnan Normal University campus are used as the data source, and the RBF under the optimal parameters is used for EM reconstruction. The accuracy of its interpolation results is evaluated and compared and analyzed with inverse distance weighting (IDW) after distance index optimization. The results show that the RBF under optimal parameters reconstructs the EM space field with high accuracy and good effect, which can truly reflect the actual distribution of EM.

To investigate curcumin (CUR) as the protector against the harmful effects of low-frequency electromagnetic field(LF- EMF, 50 Hz) during pregnancy period, 5 males and 15 females of Wistar rat mated and vaginal plaques were observed. Then, the pregnant rats were divided into six groups. During pregnancy(21 days), the EMF group was exposed to EMF for 30 min/day, the CUR group received a single dose of 50 mg/kg/daily CUR intraperitoneal, the EMF+CUR group was injected CUR and exposed to EMF daily. The DMSO(dimethyl sulfoxide) group was injected solvent of CUR (DMSO) intraperitoneal with the same volume of CUR solvent, the sham group was placed through the solenoid in the same conditions as the first group without exposure and the control group was kept in their cage in normal condition. After four weeks, babies born were divided according to the mother groups and sacrificed. Then, the three tissues injuries were investigated. EMF exposure led to an increase in outstanding necrotic areas in hippocampal tissue, an increase in the amount of hyperemia(p = 0.017) and necrotic(p = 0.005) in kidneys, and degeneration in liver tissue(p = 0.007) in the EMF group compared with EMF+CUR groups. A single dose of CUR daily during pregnancy can protect these tissues from injuries caused by LF-EMF exposure in rat fetuses.

Purpose: Venous leg ulcers are chronic wounds that are difficult to cure. The aim of the study was to compare the therapeutic efficacy of two methods of physical medicine - magneto-LED therapy and magnetostimulation, applied as adjuvant treatment in the treatment of venous leg ulcers.

Methods: The study included 81 patients, 37 male (45.6%) and 44 female (54.3%) ones, age range between 45 and 90 years, with venous leg ulcers. The patients were assigned to two study groups: magneto-LED therapy (group 1) or magnetostimulation (group 2). In both groups, a total of 40 daily procedures were performed. Wound healing was evaluated using computerized planimetry and the pain intensity on numeric rating scale.

Results: After treatment, the decrease in healing rate in group 1 was statistically significantly higher in comparison to group 2 (p < 0.001), while a statistically significant reduction in the surface area of ulcers was obtained, amounting on the average from 6.34 ± 1.29 cm² to 2.31 ± 1.25 cm² in group 1 (p < 0.001), and from 6.52 ± 1.20 cm² to 4.79 ± 1.17 cm² in group 2 (p < 0.001). The percentage changes of ulcers area in group 1 (64.21 ± 17.94%) were statistically significantly greater as compared to group 2 (25.87 ± 14.07%) (p < 0.001). After treatment, the decrease in pain relief in group 1 was statistically significantly higher in comparison to group 2 (p = 0.006), while pain intensity after treatment decreased statistically significantly in both compared groups of patients (p < 0.001).

Conclusions: Magneto-LED therapy and magnetostimulation caused significant reduction of surface area of the treated venous leg ulcers and pain intensity, yet magneto-LED therapy was more efficient. Both evaluated methods also significantly reduced pain intensity.

Exposure to blue light at bedtime, suppresses melatonin secretion, postponing the sleep onset and interrupting the sleep process. Some smartphone manufacturers have introduced night-mode functions, which have been claimed to aid in improving sleep quality. In this study, we evaluate the impact of blue light filter application on decreasing blue light emissions and improving sleep quality. Participants in this study recorded the pattern of using their mobile phones through a questionnaire. In order to evaluate sleep quality, we used a PSQI questionnaire. Blue light filters were used by 9.7% of respondents, 9.7% occasionally, and 80% never. The mean score of PSQI was more than 5 in 54.10% of the participants and less than 5 in 45.90%. ANOVA test was performed to assess the relationship between using blue light filter applications and sleep quality (p-value = 0.925). The findings of this study indicate a connection between the use of blue light filter apps and habitual sleep efficiency in the 31-40 age group. However, our results align only to some extent with prior research, as we did not observe sustained positive effects on all parameters of sleep quality from the long-term use of blue light filtering apps. Several studies have found that blue light exposure can suppress melatonin secretion, exacerbating sleep problems. Some studies have reported that physical blue light filters, such as lenses, can affect melatonin secretion and improve sleep quality. However, the impact of blue light filtering applications remains unclear and debatable.

The present study analyzed the microwave ablation of cancerous tumors located in six major cancer-prone organs and estimated the significance of input power and treatment time parameters in the apt positioning of the trocar into the tissue during microwave ablation. The present study has considered a three-dimensional two-compartment tumour-embedded tissue model. FEA based COMSOL Multiphysics software with inbuilt bioheat transfer, electromagnetic waves, heat transfer in solids and fluids, and laminar flow physics has been used to obtain the numerical results. Based on the mortality rates caused by cancer, the present study has considered six major organs affected by cancer, viz. lung, breast, stomach/gastric, liver, liver (with colon metastasis), and kidney for MWA analysis. The input power (100 W) and ablation times (4 minutes) with apt and inapt positioning of the trocar have been considered to compare the ablation volume of various cancerous tissues. The present study addresses one of the major problems clinicians face, i.e. the proper placement of the trocar due to poor imaging techniques and human error, resulting in incomplete tumor ablation and increased surgical procedures. The highest values of the ablation region have been observed for the liver, colon metastatic liver and breast cancerous tissues compared with other organs at the same operating conditions.

This paper proposes a novel approach, BTC-SAGAN-CHA-MRI, for the classification of brain tumors using a SAGAN optimized with a Color Harmony Algorithm. Brain cancer, with its high fatality rate worldwide, especially in the case of brain tumors, necessitates more accurate and efficient classification methods. While existing deep learning approaches for brain tumor classification have been suggested, they often lack precision and require substantial computational time.The proposed method begins by gathering input brain MR images from the BRATS dataset, followed by a pre-processing step using a Mean Curvature Flow-based approach to eliminate noise. The pre-processed images then undergo the Improved Non-Sub sampled Shearlet Transform (INSST) for extracting radiomic features. These features are fed into the SAGAN, which is optimized with a Color Harmony Algorithm to categorize the brain images into different tumor types, including Gliomas, Meningioma, and Pituitary tumors. This innovative approach shows promise in enhancing the precision and efficiency of brain tumor classification, holding potential for improved diagnostic outcomes in the field of medical imaging. The accuracy acquired for the brain tumor identification from the proposed method is 99.29%. The proposed BTC-SAGAN-CHA-MRI technique achieves 18.29%, 14.09% and 7.34% higher accuracy and 67.92%,54.04%, and 59.08% less Computation Time when analyzed to the existing models, like Brain tumor diagnosis utilizing deep learning convolutional neural network with transfer learning approach (BTC-KNN-SVM-MRI); M3BTCNet: multi model brain tumor categorization under metaheuristic deep neural network features optimization (BTC-CNN-DEMFOA-MRI), and efficient method depending upon hierarchical deep learning neural network classifier for brain tumour categorization (BTC-Hie DNN-MRI) respectively.