Journal of Biomedical Physics and Engineering最新文献_第2页

Attentional Control during Visual Modified Flanker Stimuli using fMRI Data. 使用功能磁共振成像数据进行视觉修饰侧侧刺激时的注意控制。

Journal of Biomedical Physics and Engineering Pub Date : 2026-08-01 DOI: 10.31661/jbpe.v0i0.2408-1813

Hamid Sharini, Elahe Rajeyan, Shahab Faraji, Mojtaba Ahmadi, Ayob Faramarzi, Maziar Jalalvandi

{"title":"Attentional Control during Visual Modified Flanker Stimuli using fMRI Data.","authors":"Hamid Sharini, Elahe Rajeyan, Shahab Faraji, Mojtaba Ahmadi, Ayob Faramarzi, Maziar Jalalvandi","doi":"10.31661/jbpe.v0i0.2408-1813","DOIUrl":"10.31661/jbpe.v0i0.2408-1813","url":null,"abstract":"Background: Selective attention is the ability to concentrate on specific sensory inputs while ignoring other stimuli and sensory inputs, and it is related to job performance, especially in military personnel.Objective: This study aimed to evaluate selective attention in military personnel rather than normal individuals.Material and methods: In this cross-sectional study, 40 individuals were divided into two groups: military personnel and normal individuals. Participants were shown a modified flanker task in a military environment, and functional magnetic resonance imaging (fMRI) was used to assess brain activation and functional connectivity through an attention task.Results: Military personnel demonstrated quicker response times than civilians in both high- and low-threat environments, particularly in incongruent trials. In high-threat scenarios, the left Medial Frontal Gyrus (MFG) showed increased voxel counts, while the right MFG was more active in low-threat trials. Additionally, military personnel exhibited stronger functional connectivity in attention regions compared to civilians.Conclusion: Functional connectivity analysis reveals that military personnel show increased connectivity in attention regions during high-threat situations, indicating adaptive neural strategies for managing danger. The study also finds that congruent stimuli demand less neural coordination than incongruent ones, resulting in the understanding improvement of threat perception and attentional processes in military contexts, with significant implications for training and performance.","PeriodicalId":38035,"journal":{"name":"Journal of Biomedical Physics and Engineering","volume":"16 4","pages":"315-332"},"PeriodicalIF":0.0,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13457681/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148713914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Immune Cell Dysregulation in Hindlimb Unloading Mouse Model: Implications for Space Applications. 后肢卸荷小鼠模型免疫细胞失调：空间应用意义

Journal of Biomedical Physics and Engineering Pub Date : 2026-08-01 DOI: 10.31661/jbpe.v0i0.2406-1776

Sadegh Masoudi, Mehdi Kalani, Ali Alavianmehr, Mohammad Amin Mosleh-Shirazi, Seyed Mohammad Javad Mortazavi, Shirin Farjadian

{"title":"Immune Cell Dysregulation in Hindlimb Unloading Mouse Model: Implications for Space Applications.","authors":"Sadegh Masoudi, Mehdi Kalani, Ali Alavianmehr, Mohammad Amin Mosleh-Shirazi, Seyed Mohammad Javad Mortazavi, Shirin Farjadian","doi":"10.31661/jbpe.v0i0.2406-1776","DOIUrl":"10.31661/jbpe.v0i0.2406-1776","url":null,"abstract":"Background: Hindlimb unloading (HU) mice is a ground-based model that simulates the effects of microgravity. Since microgravity significantly affects the immune system, understanding immune cell function under these conditions is crucial for developing strategies to protect astronauts from infections and malignancies during long space missions.Objective: To evaluate how microgravity affects neutrophils and T cells as the key components of innate and adaptive immunity, the activity of these cells in HU mice was compared with untreated control mice.Material and methods: In this experimental study, 10 HU male BALB/c mice and 10 untreated control mice were included. Neutrophil-to-lymphocyte ratio (NLR) was evaluated and neutrophil function was assessed using the DHR assay. T cell proliferation was evaluated using the CFSE-dilution assay. IL-4 and IFN-ɣ production by T cell subsets was determined by intracellular cytokine staining with flow cytometry.Results: The capacity for reactive oxygen species (ROS) production in neutrophils did not differ between HU mice and control mice however, NLR was higher in HU mice. The proliferation of both CD4+ and CD8+ T cells was slightly reduced in HU mice. More notably, IL-4 production by CD4+ T cells and IFN-ɣ production by both CD4+ and CD8+ T cells were significantly decreased in HU mice.Conclusion: Hindlimb unloading, simulating microgravity, impairs immune cell functions by reducing cytokine production and T cell proliferation. The increased NLR in HU mice could indicate a heightened inflammatory response. These insights are essential for advancing space biology and medicine, ensuring astronaut health during prolonged space travel.","PeriodicalId":38035,"journal":{"name":"Journal of Biomedical Physics and Engineering","volume":"16 4","pages":"343-352"},"PeriodicalIF":0.0,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13457494/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148713880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cortical Complexity Alterations in Methamphetamine, Cannabis, and Opioid Users: An EEG-Based Analysis. 甲基苯丙胺、大麻和阿片类药物使用者的皮质复杂性改变：基于脑电图的分析。

Journal of Biomedical Physics and Engineering Pub Date : 2026-08-01 DOI: 10.31661/jbpe.v0i0.2408-1805

Nasimeh Marvi, Javad Haddadnia, Mohammad Reza Fayyazi Bordbar, Fatemeh Davarinia

{"title":"Cortical Complexity Alterations in Methamphetamine, Cannabis, and Opioid Users: An EEG-Based Analysis.","authors":"Nasimeh Marvi, Javad Haddadnia, Mohammad Reza Fayyazi Bordbar, Fatemeh Davarinia","doi":"10.31661/jbpe.v0i0.2408-1805","DOIUrl":"10.31661/jbpe.v0i0.2408-1805","url":null,"abstract":"Background: Drug abuse causes substantial psychological and physical harm to individuals, highlighting the critical need for advanced diagnostic and treatment methodologies.Objective: This study aimed to develop a highly accurate automatic detection system for substance abuse, specifically targeting Methamphetamine (Meth), Cannabis (Can), and Opioid (Op) users.Material and methods: This descriptive study developed a drug abuse detection system based on nonlinear Electroencephalogram (EEG) signal analysis combined with a Support Vector Machine (SVM) classifier. It also examined changes in EEG signal complexity associated with Meth, Can, and Op abuse by extracting determinism and complexity parameters using Recurrence Quantification Analysis (RQA).Results: The observed decrease in EEG complexity in the Op and Meth groups suggests that these substances may reduce cognitive or behavioral complexity. Conversely, increased complexity in the Can group compared to the Healthy Control (HC) group may indicate enhanced complexity associated with cannabis use. The classification system achieved 88.77% accuracy, 87.69% sensitivity, and 96.30% specificity.Conclusion: The designed automatic diagnostic assistance system, leveraging nonlinear brain data analysis, effectively differentiates Meth, Op, and Can users from HC individuals.","PeriodicalId":38035,"journal":{"name":"Journal of Biomedical Physics and Engineering","volume":"16 4","pages":"333-342"},"PeriodicalIF":0.0,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13457673/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148713921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dimensionality Reduction for Offline Alphabet Arabic Sign Language Recognition using Deep Learning. 基于深度学习的离线阿拉伯字母手语识别降维方法。

Journal of Biomedical Physics and Engineering Pub Date : 2026-08-01 DOI: 10.31661/jbpe.v0i0.2503-1904

Sari Awwad, Subhieh M El-Salhi, Bashar Igried

{"title":"Dimensionality Reduction for Offline Alphabet Arabic Sign Language Recognition using Deep Learning.","authors":"Sari Awwad, Subhieh M El-Salhi, Bashar Igried","doi":"10.31661/jbpe.v0i0.2503-1904","DOIUrl":"10.31661/jbpe.v0i0.2503-1904","url":null,"abstract":"Background: Arabic Sign Language (ArSL) recognition remains limited in terms of technological development, compared to American Sign Language (ASL). This disparity restricts communication accessibility for individuals with hearing impairments in Arabic-speaking regions, in offline environments with limited computational resources.Objective: This study aimed to develop a robust offline recognition system for ArSL by integrating Principal Component Analysis (PCA) for dimensionality reduction, Scale-Invariant Feature Transform (SIFT) for feature extraction, and Convolutional Neural Networks (CNNs) for gesture classification.Material and methods: This experimental, quantitative research used a curated dataset of ArSL gestures, obtained from Kaggle. Preprocessing involved normalization, contrast enhancement, and noise reduction. SIFT was used to extract invariant features, while PCA reduced computational complexity. CNN architectures were trained to recognize gestures, assessed using accuracy, precision, recall, F1-score, loss, confusion matrix, and Receiver Operating Characteristic (ROC) curve.Results: The system achieved an accuracy of 86.64%, surpassing conventional models, such as SIFT combined with Support Vector Machines (SIFT+SVM) (84.45%). The integration of PCA and SIFT enhanced recognition efficiency and reduced model complexity. Deep learning methods showed superior adaptability and precision across gesture types.Conclusion: This study presents a robust offline ArSL recognition system that enhances communication, education, and social participation for individuals with hearing impairments in Arabic-speaking regions.","PeriodicalId":38035,"journal":{"name":"Journal of Biomedical Physics and Engineering","volume":"16 4","pages":"353-360"},"PeriodicalIF":0.0,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13457496/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148713955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Optimal Approach in Selecting Embryo for In-Vitro Fertilization (IVF) Based on Deep Learning. 基于深度学习的体外受精胚胎选择优化方法。

Journal of Biomedical Physics and Engineering Pub Date : 2026-08-01 DOI: 10.31661/jbpe.v0i0.2410-1832

Bita Nasiri, Nacer Farajzadeh, Jalil Ghavidel Neycharan

{"title":"An Optimal Approach in Selecting Embryo for In-Vitro Fertilization (IVF) Based on Deep Learning.","authors":"Bita Nasiri, Nacer Farajzadeh, Jalil Ghavidel Neycharan","doi":"10.31661/jbpe.v0i0.2410-1832","DOIUrl":"10.31661/jbpe.v0i0.2410-1832","url":null,"abstract":"Background: About 14% of couples experience infertility, and In Vitro Fertilization (IVF) has become one of the most widely used treatment options. However, the overall success rate of IVF remains relatively low, at around 30%. At present, viable embryos are typically selected on the fifth day based primarily on morphological assessment, a method that is both subjective and limited in accuracy. Considering the substantial financial, physical, and emotional costs associated with failed IVF attempts, there is a pressing need for more reliable and effective embryo selection techniques.Objective: This study aimed to increase the accuracy of embryo selection in IVF based on a deep learning-based transfer with the GoogLeNet architecture.Material and methods: In this experimental study, a retrospective dataset of embryo images was used to develop and evaluate a deep learning-based classification model in the following main phases: data preprocessing, model implementation, and evaluation. Embryo images were standardized through cropping and normalization to ensure consistency across different imaging systems. The GoogLeNet architecture, pre-trained on the ImageNet dataset, was utilized and further modified to adapt to the specific task of embryo viability classification.Results: Evaluation on the test dataset demonstrated that the proposed model achieved strong predictive performance, with accuracy, precision, recall, and F1-score all reaching 97%. This performance surpasses that of existing baseline techniques, highlighting the model's effectiveness.Conclusion: The proposed transfer learning-based approach using GoogLeNet shows significant potential for improving embryo selection in IVF, thereby reducing the emotional and financial strain associated with repeated IVF failures.","PeriodicalId":38035,"journal":{"name":"Journal of Biomedical Physics and Engineering","volume":"16 4","pages":"297-314"},"PeriodicalIF":0.0,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13457672/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148713947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Comparative Analysis of Virtual 2D VMAT-Based SFRT and 3D Lattice Radiotherapy: Evaluation of Dosimetric and Therapeutic Outcomes. 基于虚拟二维vmat的SFRT和三维点阵放疗的比较分析：剂量学和治疗结果的评估。

Journal of Biomedical Physics and Engineering Pub Date : 2026-08-01 DOI: 10.31661/jbpe.v0i0.2410-1835

Elnaz Balvasi, Farshid Mahmoudi, Parastoo Farina, Ali Ameri, Fatemeh Jafari, Ghazale Geraily

{"title":"A Comparative Analysis of Virtual 2D VMAT-Based SFRT and 3D Lattice Radiotherapy: Evaluation of Dosimetric and Therapeutic Outcomes.","authors":"Elnaz Balvasi, Farshid Mahmoudi, Parastoo Farina, Ali Ameri, Fatemeh Jafari, Ghazale Geraily","doi":"10.31661/jbpe.v0i0.2410-1835","DOIUrl":"10.31661/jbpe.v0i0.2410-1835","url":null,"abstract":"Background: Spatially Fractionated Radiotherapy (SFRT) can be implemented using Volumetric-Modulated Arc Therapy (VMAT) in either two-dimensional (2D) or three-dimensional (3D) configurations.Objective: This study aimed to compare the dosimetric and clinical outcomes of two VMAT-based SFRT techniques for large lung tumors.Material and methods: In this experimental study, SFRT plans were designed for each patient using cylindrical and spherical grid targets. Single-fraction prescription doses of 15 and 20 Gy were delivered to the grid target isocenters using 6 MV Flattening-filter-free (FFF) photon beams.Results: The 2D SFRT plan demonstrated higher Gross Tumor Volume (GTV) mean dose, GTV Equivalent Uniform Dose (EUD), and Valley-to-peak Dose ratio (VPDR) compared to the 3D lattice plan. However, the 3D Lattice Radiotherapy (3D-LRT) technique provided a better therapeutic ratio and more uniform valley-peak dose distribution. Both plans demonstrated therapeutic ratios greater than one with minimal Normal Tissue Complication Probability (NTCP).Conclusion: Both 2D and 3D lattice VMAT-based SFRT techniques effectively delivered high radiation doses with steep dose gradients within the GTV, minimizing normal tissue exposure and reducing the risk of complications.","PeriodicalId":38035,"journal":{"name":"Journal of Biomedical Physics and Engineering","volume":"16 4","pages":"285-296"},"PeriodicalIF":0.0,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13457497/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148713902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Occupational Exposure to Blue Light Emitted from Neonatal Phototherapy Devices Alters the Sleep Quality of NICU Nurses. 职业暴露于新生儿光疗设备发出的蓝光会改变新生儿重症监护病房护士的睡眠质量。

Journal of Biomedical Physics and Engineering Pub Date : 2026-06-01 DOI: 10.31661/jbpe.v0i0.2402-1732

Zahra Zahadatpour, Sayed Mostajab Razavinejad, Alireza Eslaminejad, Hassan Vafapour, Seyed Mohammad Javad Mortazavi, Seyed Ali Reza Mortazavi

{"title":"Occupational Exposure to Blue Light Emitted from Neonatal Phototherapy Devices Alters the Sleep Quality of NICU Nurses.","authors":"Zahra Zahadatpour, Sayed Mostajab Razavinejad, Alireza Eslaminejad, Hassan Vafapour, Seyed Mohammad Javad Mortazavi, Seyed Ali Reza Mortazavi","doi":"10.31661/jbpe.v0i0.2402-1732","DOIUrl":"10.31661/jbpe.v0i0.2402-1732","url":null,"abstract":"Exposure to blue light, primarily from light-emitting devices like LEDs, has raised concerns regarding its potential health effects. Neonatal intensive care unit (NICU) nurses, frequently exposed to both digital screens and blue light from phototherapy devices, face particular risks. This study aimed to investigate the influence of blue light exposure from neonatal phototherapy devices on the sleep quality of NICU nurses, presenting the first examination of this issue. NICU nurses and matched controls were studied, comparing their sleep quality using the Pittsburgh Sleep Quality Index (PSQI). General health and reaction time were also assessed using standard questionnaires and benchmark software, respectively. The light intensities of active phototherapy devices were recorded. Statistical analysis included an independent t-test, with a P-value of ≤0.05 considered statistically significant. The study found that exposure to blue light from neonatal phototherapy devices adversely affected sleep disturbances and daytime dysfunction in NICU workers. This aligns with research indicating that reducing ambient blue light can improve cognitive performance, alertness, and sleep quality, especially for night shift workers. It also corresponds with studies linking pre-sleep use of light-emitting devices to higher rates of insomnia in various countries. Amber lenses that block blue light have been proposed as a viable solution for sleep issues. This pioneering research underscores the importance of reducing blue light exposure for NICU nurses. Encouraging the use of blue light-blocking glasses is a practical step that can be taken to mitigate the adverse effects of blue light exposure.","PeriodicalId":38035,"journal":{"name":"Journal of Biomedical Physics and Engineering","volume":"16 3","pages":"259-264"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13263381/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148253686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bacterial Adaptation to Radiofrequency Electromagnetic Fields Based on Experiences from Ionizing Radiation. 基于电离辐射经验的细菌对射频电磁场的适应。

Journal of Biomedical Physics and Engineering Pub Date : 2026-06-01 DOI: 10.31661/jbpe.v0i0.2405-1770

Ilham Said-Salman, Seyed Mohammad Javad Mortazavi, Sami El Khatib, Seyed Alireza Mortazavi, Lembit Sihver

引用次数: 0

Advanced CNN Deep Learning Model for Diabetic Retinopathy Classification. 糖尿病视网膜病变分类的高级CNN深度学习模型。

Journal of Biomedical Physics and Engineering Pub Date : 2026-06-01 DOI: 10.31661/jbpe.v0i0.2406-1774

Noor Ali Sadek, Ziad Tarik Al-Dahan, Suzan Amana Rattan, Ahmed F Hussein, Brendan Geraghty, Ahmed Kazaili

{"title":"Advanced CNN Deep Learning Model for Diabetic Retinopathy Classification.","authors":"Noor Ali Sadek, Ziad Tarik Al-Dahan, Suzan Amana Rattan, Ahmed F Hussein, Brendan Geraghty, Ahmed Kazaili","doi":"10.31661/jbpe.v0i0.2406-1774","DOIUrl":"10.31661/jbpe.v0i0.2406-1774","url":null,"abstract":"Background: Diabetic Retinopathy (DR) is one of several retinal microvascular complications of Diabetes Mellitus (DM), a disease of increasing global prevalence. However, early detection and treatment can reduce or even prevent DR progression. In this work, Deep Learning (DL) techniques are used to grade DR from an early stage using either binary or multiclass classification as a clinical aid to help reduce the risk of patient vision loss.Objective: The primary objective of this research is to develop a low-cost, fast, and accurate automated system using DL for the early detection and classification of DR from retina fundus images.Material and methods: This cross-sectional study employed three DL models, namely Convolutional Neural Networks (CNNs), decision tree, and logistic regression, to categorize three distinct clinically graded datasets, namely the Iraqi dataset, the Indian Diabetic Retinopathy Image Dataset (IDRiD) and the Eyepacs dataset, according to DR severity.Results: Evaluation of the DL model results showed that logistic regression emerged as the most effective, where accuracies of 99%, 99.3%, and 99.4% were achieved for the Iraqi, IDRiD, and Eyepacs datasets, respectively. Conversely, the decision-tree model achieved the lowest accuracy across the three datasets with 95.2%, 95.9%, and 96.0%, respectively.Conclusion: The logistic regression model demonstrated the highest overall accuracy of the three models for the classification of DR, with the Iraqi dataset with the highest accuracy of the three datasets.","PeriodicalId":38035,"journal":{"name":"Journal of Biomedical Physics and Engineering","volume":"16 3","pages":"191-204"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13263389/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148253292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Effect of Extremely Low Frequency (ELF) Electromagnetic Waves on the Prevention of Diabetes Induced by Streptozotocin in Mice: Blood Indicators. 极低频电磁波对链脲佐菌素诱导小鼠糖尿病的预防作用：血液指标。

Journal of Biomedical Physics and Engineering Pub Date : 2026-06-01 DOI: 10.31661/jbpe.v0i0.2403-1733

Ali Dehghan, Tohid Vahdatpour, Mehran Mesgari Abbasi

{"title":"The Effect of Extremely Low Frequency (ELF) Electromagnetic Waves on the Prevention of Diabetes Induced by Streptozotocin in Mice: Blood Indicators.","authors":"Ali Dehghan, Tohid Vahdatpour, Mehran Mesgari Abbasi","doi":"10.31661/jbpe.v0i0.2403-1733","DOIUrl":"10.31661/jbpe.v0i0.2403-1733","url":null,"abstract":"Background: Magnetic fields can affect physiological systems and some diseases, such as diabetes.Objective: The current study aimed to investigate the effect of a low-power magnetic field on the blood parameters of mice, with streptozotocin-induced diabetes.Material and methods: In this experimental study, 48 adult male mice were divided into six groups of eight. Diabetic mice were exposed to magnetic fields of 0.0005 and 0.005 tesla for 5 and 10 days using Helmholtz coils. Blood samples were collected every other day to measure blood factors and assess the magnetic field's effects on diabetes-related parameters.Results: In the diabetes groups, blood protein levels decreased without any effect from the magnetic intervention. However, in three out of the four groups, blood albumin increased under the influence of the magnetic field. The induction of a magnetic field led to a decrease in blood Gamma-Glutamyl Transferase (GGT) activity. Additionally, the magnetic field resulted in increasing blood magnesium levels.Conclusion: The effects of the magnetic field and diabetes on the measured blood parameters, including GGT and magnesium were independent. However, the blood albumin level, which was reduced under the influence of induced diabetes, was improved by the magnetic fields, especially in the magnetic field of the 0.005 tesla.","PeriodicalId":38035,"journal":{"name":"Journal of Biomedical Physics and Engineering","volume":"16 3","pages":"219-230"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13263388/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148253676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0