Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 11 3 2024 06 01 302 314 Babak Seddighi Department of Biomedical Engineering, Faculty of Electrical Engineering, K.N. Toosi University of Technology, Tehran, Iran Maryam Mohebbi Department of Biomedical Engineering, Faculty of Electrical Engineering, K.N. Toosi University of Technology, Tehran, Iran 2022 11 02 2023 03 27 Purpose: Interoceptions are a combination of sensation, integration, and interpretation of internal bodily signals. Interoceptions are bidirectionally related to the human being mental and physiological health, and well-being. Sleep and different interoceptive modalities are proven to share common relations. Heartbeat Evoked Potential (HEP) is known as a robust readout to interoceptive processes. In this study, we focused on the relation between HEP modulations and sleep-related disorders. Materials and Methods: We investigated four different sleep-related disorders, including insomnia, rapid eye movement behavior disorder, periodic limb movements and nocturnal frontal lobe epilepsy, and provided HEP signals of multiple Electroencephalogram (EEG) channels over the right hemisphere to compare these disorders with the control group. Here, we investigated and compared the results of 35 subjects, including seven subjects for the control group and seven subjects for each of above-mentioned sleep disorders. Results: By comparing HEP responses of the control group with sleep-related patients’ groups, statistically significant HEP differences were detected over right hemisphere EEG channels, including FP2, F4, C4, P4, and O2 channels. These significant differences were also observed over the grand average HEP amplitude activity of channels over the right hemisphere in the sleep-related disorders as well. Conclusion: Our results between the control group and groups of patients suffering from sleep-related disorders demonstrated that during different stages of sleep, HEPs show significant differences over multiple right hemisphere EEG channels. Interestingly, by comparing different sleep disorders with each other, we observed that each of these HEP differences’ patterns over specific channels and during certain sleep stages bear considerable resemblances to each other. https://fbt.tums.ac.ir/index.php/fbt/article/view/583 https://fbt.tums.ac.ir/index.php/fbt/article/download/583/369

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 11 3 2024 06 01 492 508 Seyed Saeid Masoomkhah Department of Biomedical Engineering, Meybod University, Meybod, Iran Khosro Rezaee Department of Biomedical Engineering, Meybod University, Meybod, Iran Mojtaba Ansari Department of Biomedical Engineering, Meybod University, Meybod, Iran Hossein Eslami Department of Biomedical Engineering, Meybod University, Meybod, Iran 2023 01 22 2023 04 13 Purpose: Artificial Intelligence (AI), which mimics the human brain structure and operation, simulates intelligence. The aim of Machine Learning (ML), which is a branch of artificial intelligence, is to create models by analyzing data. Another type of artificial intelligence, Deep Learning (DL), depicts geometric changes using several layers of model representations. Since DL broke the computational analysis record, AI has advanced in many areas. Materials and Methods: Contrary to the widespread use of conventional ML methodologies, there is still a need to promote the use and popularity of DL for pharmaceutical research and development. Drug discovery and design have been enhanced by ML and DL in major research projects. To fully realize its potential, drug design must overcome many challenges and issues. Various aspects of medication design must be considered to successfully address these concerns and challenges. This review article explains DL's significance both in technological breakthroughs and in effective medications. Results: There are numerous barriers and substantial challenges associated with drug design associated with DL architectures and key application domains. The article discusses several elements of medication development that have been influenced by existing research. Two widely used and efficient Neural Network (NN) designs are discussed in this article: Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). Conclusion: It is described how these tools can be utilized to design and discover small molecules for drug discovery. They are also given an overview of the history of DL approaches, as well as a discussion of some of their drawbacks. https://fbt.tums.ac.ir/index.php/fbt/article/view/637 https://fbt.tums.ac.ir/index.php/fbt/article/download/637/333

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 11 3 2024 06 01 509 529 Sara Chetehouna Department of Microbiology and Biochemistry, Faculty of Sciences, Mohamed Boudiaf-M’sila University, M’sila 28000, Algeria Samir Derouiche Department of Cellular and Molecular Biology, Faculty of the Sciences of Nature and Life, El Oued University, El Oued 39000, El Oued, Algeria. Yassine Reggami Department of Microbiology and Biochemistry, Faculty of Sciences, Mohamed Boudiaf-M’sila University, M’sila 28000, Algeria 2023 06 11 2023 08 15 Cancer is one of the most devastating disorders of the 21st century, creating a major concern among clinicians and researchers. Many different treatment strategies are being tried to fight the war against cancer have been tested. Various inorganic nanoparticles have been investigated to induce cytotoxicity in cancer cells and one of the successfully tried nanoparticles is Selenium Nanoparticles (SeNPs). Green synthesized SeNPs are a promising source of new antioxidant and anti-inflammatory agents, given the multiplicity of its mechanism. SeNPs displayed antiproliferative potential against colon, liver, cervical, breast, melanoma, and prostate cancer cells by several mechanisms, including triggering apoptotic signal transduction pathways or slow down the angiogenic signalling in cancer cells. Metal nano-therapies such as SeNPs are granted research consideration for cancer treatment. The biocompatibility achieved through green synthesis suggests its possible use not only in specific cancer conditions but also in other types of cancer without any risk of toxicity of these molecules. https://fbt.tums.ac.ir/index.php/fbt/article/view/729 https://fbt.tums.ac.ir/index.php/fbt/article/download/729/394

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 11 3 2024 06 01 315 325 EN M. Moein Esfahani Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran Hosein Najafi Department of Computer Engineering, Islamic Azad University, Tehran, Iran Hossein Sadati Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran 2022 11 11 2022 12 22 Purpose: In recent years, the use of Steady-State Visual Evoked Potentials (SSVEPs) in Brain-Computer Interface (BCI) systems has dramatically increased across several fields, such as rehabilitation, cognitive impairment, and brain disease or disorder detection, as well as artificial limbs, wheelchairs, and biomechanical systems. In this study, a novel method is proposed to help scientists develop more efficient BCI systems for Machine Learning Operations (MLOps). This study proposed a state-of-the-art method for detecting SSVEP-based stimulation frequencies with statistical models to design an optimal BCI system. Materials and Methods: In this study, the Canonical Correlation Analysis (CCA) method has been implemented to extract features from the accessible-to-the-public Tsinghua University Benchmark dataset. A limited number of subjects are being studied. After completing feature selection methods and selecting the best subset of features using a specified feature selection method, the classification of the best features using machine learning-based classification methods has been completed. Furthermore, it is assumed that scientists will design and implement a system specifically for subjects. Models work for subjects independently. However, because model training is subject-specific, we must execute the proposed methods on each subject separately. Results: The findings indicate that the novel suggested BCI system achieves an average accuracy of 83±9% in stimulation detection, which is higher than that of the traditional CCA approach with an accuracy of 80±11% (p<0.05). Conclusion: Based on the findings, we demonstrated an increase in accuracy with the novel method. It was also discovered that by using the proposed techniques, it is possible to keep MLOps systems as an advantage. https://fbt.tums.ac.ir/index.php/fbt/article/view/594 https://fbt.tums.ac.ir/index.php/fbt/article/download/594/421

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 11 3 2024 06 01 326 343 B. Manjula Department of Computer Science, University College, Kakatiya University, Telangana, India P Venkateshwarlu Department of Computer Science, Vaageswari College of Engineering, Telangana, India 2022 12 02 2023 04 02 Purpose: Interoceptions are a combination of sensation, integration, and interpretation of internal bodily signals. Interoceptions are bidirectionally related to the human being mental and physiological health, and well-being. Sleep and different interoceptive modalities are proven to share common relations. Heartbeat Evoked Potential (HEP) is known as a robust readout to interoceptive processes. In this study, we focused on the relation between HEP modulations and sleep-related disorders. Materials and Methods: We investigated four different sleep-related disorders, including insomnia, rapid eye movement behavior disorder, periodic limb movements and nocturnal frontal lobe epilepsy, and provided HEP signals of multiple Electroencephalogram (EEG) channels over the right hemisphere to compare these disorders with the control group. Here, we investigated and compared the results of 35 subjects, including seven subjects for the control group and seven subjects for each of above-mentioned sleep disorders. Results: By comparing HEP responses of the control group with sleep-related patients’ groups, statistically significant HEP differences were detected over right hemisphere EEG channels, including FP2, F4, C4, P4, and O2 channels. These significant differences were also observed over the grand average HEP amplitude activity of channels over the right hemisphere in the sleep-related disorders as well. Conclusion: Our results between the control group and groups of patients suffering from sleep-related disorders demonstrated that during different stages of sleep, HEPs show significant differences over multiple right hemisphere EEG channels. Interestingly, by comparing different sleep disorders with each other, we observed that each of these HEP differences’ patterns over specific channels and during certain sleep stages bear considerable resemblances to each other. https://fbt.tums.ac.ir/index.php/fbt/article/view/612 https://fbt.tums.ac.ir/index.php/fbt/article/download/612/351

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 11 3 2024 06 01 344 360 Malarvizhi Ayyadurai Research scholar & Assistant Professor, Department of ECE, Vinayaka Mission’s Kirupananda Variyar Engineering College, Vinayaka Mission’s Research Foundation (Deemed to be University), Salem 636308, Tamilnadu, India. Nagappan Andiappan Director-Innovation, Incubation and Entrepreneurship, Vinayaka Mission’s Research Foundation (Deemed to be University), Salem 636308, Tamilnadu, India. 2022 12 10 2023 04 11 Purpose: In contemporary time, breast cancer has been extensively found among women at a global rate of 24.2% as of 2018. This exposes the significant and imperative need for detecting masses and micro calcifications in the breast to avoid death rates. The existing histopathological images have gained a golden standard considering they would afford reliable results. However, these images have been entangled with various complexities including insufficient image contrast, noise, and misdiagnosis. These pitfalls might negatively impact the detection rate for which automatic recognition has become vital. With the momentous evolvement of Machine Learning (ML) and Deep Learning (DL), various researchers have endeavoured to consider ML and DL for accomplishing this prediction. However, they need to improve in accuracy rate due to ineffective feature extraction, and most studies averted to consider segmentation. Hence, this study regards accomplishing a high classification and segmentation process. Materials and Methods: The study proposes Modified Weight Updated Convolutional Block-U-Net (MWu-Conv-U-Net) to handle the image dimensions optimally. In this case, U-Net based model is regarded by improvising it with the inclusion of an additional convolutional layer in individual encoder-decoder. Further, the study proposes an Optimized Weight Updated eXtreme Gradient Boosting-Support Vector Machine (OWu-XGBoost-SVM) for determining the optimal gradient, which would eventually enhance the prediction rate. Results: The overall performance is assessed through performance metrics to confirm its effectiveness in classifying and segmenting the Breast Cancer Histopathological (BACH) image dataset. Comparison is undertaken with conventional systems in accordance with metrics (recall, F1-score, precision, and accuracy). The results revealed the efficacy of the proposed system with 99% accuracy, 99% F1-score, 99% precision, and 99% recall. Conclusion: High accuracy procured through the analysis reveals its efficacy and hence it could be applicable for real-time execution for assisting medical experts in early breast cancer prognosis. https://fbt.tums.ac.ir/index.php/fbt/article/view/614 https://fbt.tums.ac.ir/index.php/fbt/article/download/614/329

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 11 3 2024 06 01 361 374 Arrigo Palumbo Department of Medical and Surgical Sciences, University Magna Graecia, Viale Europa 88100 Catanzaro, Italy Barbara Calabrese Department of Medical and Surgical Sciences, University Magna Graecia, Viale Europa 88100 Catanzaro, Italy Nicola Ielpo Department of Medical and Surgical Sciences, University Magna Graecia, Viale Europa 88100 Catanzaro, Italy Remo Garropoli Garropoli Computer Science Consulting, 87100 Cosenza, Italy Patrizia Vizza Department of Medical and Surgical Sciences, University Magna Graecia, Viale Europa 88100 Catanzaro, Italy Domenico Corchiola Corchiola Computer Science Consulting, 87100 Cosenza, Italy Vera Gramigna Department of Medical and Surgical Sciences, University Magna Graecia, Viale Europa 88100 Catanzaro, Italy 2022 12 28 2023 03 06 Purpose: In this paper, we propose a secure wearable and portable deiation> Seyed Mohammad Hosseini Department of Medical Physics and Medical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran Ali Mohammad Sharifi Clinical Research Development Center, Shahid Modarres Educational Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran Meysam Tavakoli Department of Radiation Oncology, Winship Cancer Institute, Emory University, Atlanta, GA, USA 2023 05 10 2023 12 14 Gastro-Esophageal (GE) junction cancer has been increasingly prevalent worldwide. This study aims to compare dosimetric and radiobiological parameters for target areas and Organs At Risk (OARs) in men and women patients diagnosed with GE junction cancer. Materials and Methods: Here, thirty patients who underwent radiotherapy using a 6-MV photon beam from a linear accelerator (Shinva Medical, Shandong, China) were selected. Dosimetric and radiobiological parameters within the Planning Target Volume (PTV) and OARs were compared among all patients using a paired-sample t-test. Additionally, a comparative analysis of Field-In-Field (FIF), three-Field (3F), and four-Field Box (4FB) planning techniques was conducted for both men and women patients. Results: In terms of dose distribution in the PTV, a significant difference exists between male and female patients regarding TCP and Monitor Unit (MU). Furthermore, in terms of dose distribution in OARs, there is also a significant difference between males and females in terms of NTCP for the right lung and V20 Gy for the right lung. Conclusion: In general, most dosimetric parameters exhibited similarities between male and female patients. However, notable differences surfaced in TCP, MU, and specific parameters, including NTCP and V20Gy for the right lung. Hence, it is prudent to emphasize meticulous attention in treatment planning for GE junction cancer, considering the anatomical variations between males and females. https://fbt.tums.ac.ir/index.php/fbt/article/view/702 https://fbt.tums.ac.ir/index.php/fbt/article/download/702/465

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 12 2 2025 03 01 265 277 Ali Ekhlasi Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran Ali Motie Nasrabadi Department of Biomedical Engineering, Faculty of Engineering, Shahed University, Tehran, Iran Mohammadreza Mohammadi Psychiatry and Psychology Research Center, Roozbeh Hospital, Tehran University of Medical Sciences, Tehran, Iran 2024 04 22 2024 08 25 Purpose: Investigating brain connectivity using Electroencephalogram (EEG) is a valuable method for studying mental disorders, such as Attention-Deficit/Hyperactivity Disorder (ADHD), and optimizing and developing measures of effective connectivity can provide new insights into differences in brain communication in such disorders. Multivariate Transfer Entropy (MuTE) is a measure of causal connectivity that quantifies the influence of multiple variables on each other in a system. In this study, the MuTE measure was modified by incorporating an interaction delay parameter in connectivity calculations to create a measure with self-prediction optimality, which we named . Materials and Methods: We applied  to investigate EEG effective connectivity in healthy and ADHD children performing an attention task across five frequency bands and to compare brain connectivity differences between the two groups using statistical analysis. Results: Our analysis revealed that children with ADHD exhibited excessive short-distance connections in all frequency bands while healthy children demonstrated stronger long-range connections in the alpha and gamma frequency bands. Moreover, excessive short-distance connectivity was observed in the delta and theta frequency bands in all brain regions, as well as in the alpha, beta, and gamma frequency bands between the central and parietal regions in children with ADHD. These connectivity patterns may contribute to impaired attention functions by impeding effective information transmission and reducing information processing speed in the brains of children with ADHD. Conclusion: Our analysis presents a novel methodology for measuring effective connectivity and elucidates the differences in EEG brain connectivity between children with ADHD and healthy children. https://fbt.tums.ac.ir/index.php/fbt/article/view/998 https://fbt.tums.ac.ir/index.php/fbt/article/download/998/468

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 12 2 2025 03 01 278 291 EN Ali Ekhlasi Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran Hessam Ahmadi Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran Mohammad Saleh Hoseinzadeh Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran 2024 06 27 2024 12 25 Purpose: Brain-Computer Interfaces (BCI) are advanced systems that enable a direct neural pathway between the human brain and external devices. The importance of BCI is underscored by its profound implications for medical therapeutics, particularly in neurorehabilitation. Materials and Methods: This study developed an algorithm to detect 8 motion commands for a robot using individuals' EEG signals (Electroencephalogram). These signals were recorded during imagined and expressed commands. The research aimed to identify optimal features for extracting and classifying EEG signals for robot commands and to pinpoint the best EEG channels for a cost-effective, efficient signal acquisition system. Four categories of features, including temporal, frequency, wavelet, and combined features were extracted from the EEG signals. The Imperialist Competitive Algorithm (ICA) and Cuckoo Optimization Algorithm (COA) were utilized for feature selection. Results: Findings revealed that wavelet features are most effective for analyzing and classifying EEGs. For imagined commands, optimal features from all channels achieved a 96.3% classification accuracy, while expressed commands reached 96.5%. The frontal and parietal lobes were identified as the prime EEG channels for command detection, achieving accuracies of 91.5% and 86.9% for imagined commands, and 92.7% and 86.1% for expressed commands, respectively. The result also indicated that the brain's midline and left hemisphere (containing the Broca area) outperformed the right hemisphere in classification. Conclusion: By focusing on the optimal EEG channels, a more cost-effective hardware system can be designed, surpassing the traditional 21-channel system and requiring only 14 electrodes in the frontal and parietal regions. https://fbt.tums.ac.ir/index.php/fbt/article/view/1050 https://fbt.tums.ac.ir/index.php/fbt/article/download/1050/477

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 12 2 2025 03 01 292 308 Ritika Upadhyay School of Engineering, Ajeenkya DY Patil University, Pune, Maharashtra 412105, India Biswajeet Champaty School of Engineering, Ajeenkya DY Patil University, Pune, Maharashtra 412105, India Suraj Nayak Department of Electrical and Electronics Engineering, School of Engineering and Technology, ADAMAS University, Kolkata, West Bengal, 700126, India 2023 02 06 2023 10 12 Purpose: This study aims to determine whether Himalayan singing bowl vibrations could lead to deeper and faster relaxation than supine silence. Numerous civilizations have used singing bowls, gongs, bells, didgeridoos, and voice sounds and chants as instruments for sound healing for ages in religious rites, festivals, social celebrations, and meditation activities. Materials and Methods: The effect of sound vibrations on physical and mental wellness is supported by scientific research. Although various pieces of research have demonstrated the effect of meditation on humans, very few studies have been done on the beneficial effects of singing bowls on the body and the mind (decrease in unease and temperament, Electroencephalogram, etc.). This study suggests two Machine Learning (ML) models for the automatic classification of the meditative state from the normal state using the Heart Rate Variability (HRV) data. Results: To pick suitable inputs for the ML models a statistics-based t-test and Principal Component Analysis (PCA) was applied. In the statistics-based t-test method, the HRV parameters were subjected to choose appropriate input for the ML model. Conclusion: In this case study there are two models that were considered the most effective models based on their accuracy, that are MLP 31-13-2 and RBF 31-17-2 model having a training accuracy of 83.75% and 68.75% respectively. In the second case study, the PCA approach was applied to the HRV parameters, and as a result MLP 4-6-2 and MLP 4-10-2 were the most effective models, with an accuracy of 69.6% and 71.4% respectively. https://fbt.tums.ac.ir/index.php/fbt/article/view/644 https://fbt.tums.ac.ir/index.php/fbt/article/download/644/489

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 12 2 2025 03 01 309 319 EN Anahita Jafari Medical Imaging Research Centre, Shiraz University of Medical Sciences, Shiraz , Iran Fariba Zarei Medical Imaging Research Centre, Shiraz University of Medical Sciences, Shiraz 7193635899, Iran Hamidreza Masjedi Medical Imaging Research Centre, Shiraz University of Medical Sciences, Shiraz 7193635899, Iran Samira Moshiri Medical Imaging Research Centre, Shiraz University of Medical Sciences, Shiraz 7193635899, Iran Vyas Akondi Department of Physical Sciences, Indian Institute of Science Education and Research (IISER) Berhampur, Berhampur, Odisha 760010, India Sabyasachi Chatterjee Retired Scientist from Indian Institute of Astrophysics, Present Affiliation: Ongile, 79 D3, Sivaya Nagar, Reddiyur Alagapuram, Salem 636004. India Rezvan Ravanfar Haghighi Medical Imaging Research Centre, Shiraz University of Medical Sciences, Shiraz , Iran 2024 02 18 2024 05 30 Purpose: The purpose of this study is to test the linearity of the CT system and ascertain the relationship between Hounsfield Unit ( ) values and weight/weight concentrations of iodine ( ) in mixtures. This aims to determine the iodine concentration thresholds for achieving effective contrasts with minimal iodine usage. Materials and Methods: Aqueous solutions of Iopaque, with 300mgI/mL of iodine, were prepared for different weight/weight ( ) iodine concentrations and filled in a water-pool phantom, and the  observations were taken at different kVps for 10 different CT machines. The variation of  with  was analyzed as, . From this, the  necessary for getting a required  value is estimated. Results: It is found that HU(V) varies linearly with  for low wi values, although the coefficients  and  vary widely between machines. For optimal HU enhancement, it was found that a 0.01% weight/weight concentration of iodine is adequate to produce an  value of 450 at 80 kVp, while the corresponding concentration should be 0.025% weight/weight at 120 kVp. Conclusion: Linear dependence of HU on wi helps to reduce the contrast media volume by estimating the iodine concentration, necessary for obtaining a required HU. It also revealed that lower kVps could yield adequate HU enhancement with a reduced contrast agent, thus potentially minimizing patient exposure to radiation and contrast media. https://fbt.tums.ac.ir/index.php/fbt/article/view/940 https://fbt.tums.ac.ir/index.php/fbt/article/download/940/496

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 12 2 2025 03 01 320 340 EN Shabnam Andalibi Miandoab Department of Electrical Engineering, Tabriz Branch, Islamic Azad university, Tabriz, Iran Nazlar Ghasemzadeh Department of Biomedical Engineering, Tabriz Branch, Islamic Azad university, Tabriz, Iran 2024 10 08 2024 11 26 Purpose: In optogenetics, visible light is usually used, which limits the penetration depth into the tissue, and placing optical fibers to deliver light to deep areas of the brain is necessary. In this paper, to overcome limitations, the use of Near-Infrared light (NRI) and temperature-sensitive opsins has been proposed as a powerful, non-invasive, or minimally invasive tool due to greater penetration depth, with the least damage and most effectiveness in brain tissue. Materials and Methods: Effects of optogenetic stimulation with visible light and NIR on the model of Parkinson's Disease (PD) Basal Ganglia-Thalamic (BG-Th) network to reduce or eliminate pathological effects of Parkinson's disease has been studied. Three and four-state optogenetic Halordopsin (NpHR) and Channelrhodopsin-2 (ChR2) opsins at visible wavelengths and four-state optogenetic with Transient Receptor Potential Vanilloid 1 (TRPV1)  and Transient Receptor Potential Ankyrin 1 (TRPA1) opsins at NIR wavelengths for different frequencies and number of stimulation pulses and light intensity on Error Index (EI) and beta band activity in the BG-TH to introduce optimal values for basic parameters of f, ns, and Alight have been considered. Finally, we obtained Alight effects on the beta band activity for different optogenetic stimulations and opsins (NpHR, ChR2, TRPV1, and TRPA1). Results: Four-state optogenetic stimulation TRPA1 at 808 nm is optimal with the best results, lowest EI, and beta band activity. By increasing Alight, beta band activity for all used opsins has decreased, which is sharp for NpHR, and TRPA1 with 808 nm, with low intensity, has caused less beta band activity. Conclusion: The Near-Infrared light with the best results and the lowest beta band activity (Beta activity=0.2) is more effective. https://fbt.tums.ac.ir/index.php/fbt/article/view/1125 https://fbt.tums.ac.ir/index.php/fbt/article/download/1125/467

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 12 2 2025 03 01 341 354 Mohamed Belmokeddem Biomedical Engineering Department, Faculty of Technology, University Abou-Bekr Belkaid of Tlemcen, Algeria Kamila Khemis Biomedical Engineering Department, Faculty of Technology, University Abou-Bekr Belkaid of Tlemcen, Algeria Salim Loudjedi Surgery B Tlemcen Hospital, Department of Medicine, University Abou-Bekr Belkaid of Tlemcen, Algeria 2023 11 07 2024 03 04 Purpose: Ensuring excellent video quality is crucial for the success of minimally invasive surgical procedures without disrupting the surgical procedure flow. Real-time laparoscopic video frequently encounters issues such as blur and smoke, often stemming from lens contamination. The automatic detection of these distortions is imperative to assist surgeons, ultimately reducing operative time and mitigating risks for the patient. Materials and Methods: In this paper, we leverage the Laparoscopic Video Quality (LVQ) database developed by Khan et al. to train and validate our model. To classify defocus blur, motion blur, and smoke in the laparoscopic video, we adopt a novel approach utilizing a cascade support vector machine (SVM) classifier, which combines decisions from three binary classifiers. The first classifier categorizes videos into two classes: good and distorted. The second classifier focuses on detecting smoke and blur, while the third is dedicated to distinguishing between defocus blur and motion blur. Results: In this study, we calculate performance metrics, including accuracy rate, precision, recall, F1 score, and execution time, which are crucial indicators for evaluating quality detection results. The machine-learning classification demonstrates notable performance, with an accuracy rate of 96.55% for the first classifier, 100% for the second, and 99.67% for the third classifier. Additionally, the classification achieves a high inference speed of 37 frames per second (fps). Conclusion: The experimental results showcased in this paper underscore the efficacy of the proposed approach in automatically detecting distortions in a laparoscopic video. The method exhibits high performance, excelling in both accuracy and processing speed. Notably, the method's advantage lies in its simplicity and the fact that it does not necessitate high-performance computer hardware. https://fbt.tums.ac.ir/index.php/fbt/article/view/873 https://fbt.tums.ac.ir/index.php/fbt/article/download/873/464

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 12 2 2025 03 01 355 364 EN Mahmoud Bagheri Department of Medical Physics, School of Paramedicine, Arak University of Medical Sciences, Arak, Iran Javad Hosseini Nejad Neuroscience research center, Baqiyatallah University of Medical Sciences, Tehran, Iran Hassan Tavakoli Department of Physiology and Biophysics, Baqiyatallah University of Medical Sciences, Tehran, Iran Seyed Abbas Tavallaie Behavioral Sciences Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran Aliakbar Karimi Zarchi Department of Epidemiology and Biostatistics, Baqiyatallah University of Medical Sciences, Tehran, Iran 2023 11 25 2023 12 26 Purpose: This study was conducted to evaluate the comparative effectiveness of repetitive Transcranial Magnetic Stimulation (rTMS) and intermittent Theta Burst Stimulation (iTBS), in Treatment-Resistant Depression (TRD) patients using resting-state Electroencephalography (EEG). iTBS is a novel form of magnetic stimulation with the potential to produce similar anti-depressant effects but in a much shorter time. Materials and Methods: In two stimulation protocols, 78 patients with TRD received 20 sessions. Depression symptoms were assessed based on the changes in the Hamilton Depression Rating Scale (HAM-D) and Beck Depression Inventory (BDI-II) scores at baseline, after the last session, and at 4 weeks after treatment. Resting-state EEG was measured at baseline and after the last session. EEG power spectrum was extracted and power changes were evaluated statistically. Results: There was no significant difference in response and remission rates between the two groups. Following 10 Hz rTMS and iTBS, the clinical indexes improved by 48.5 ± 19.8 % (p-value < 0.05) and 50.4 ± 21.7 % (p-value < 0.05), respectively. There was a significant reduction in the mean depression scores for both treatment groups (p < 0.05). Following treatment, TRD patients showed considerable enhancement in gamma power at the left DLPFC site (F3, F5, and F7 electrode) in the iTBS group and significant increases in delta power at the F3 and F7 electrode sites in the 10 Hz rTMS group. Conclusion: iTBS provides clinical advantages, which showed that the results did not contrast altogether with results from a standard course of rTMS treatment. It might be invaluable from a clinical, benefit, and understanding perspective. Biomarkers of clinical outcomes such as resting-state brain activity measured with EEG may save individuals worthless treatment and moderately limited clinical assets. https://fbt.tums.ac.ir/index.php/fbt/article/view/888 https://fbt.tums.ac.ir/index.php/fbt/article/download/888/390

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 12 2 2025 03 01 365 375 Fatemeh Ziyaei Department of Medical Radiation Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran Somaye Malmir Department of Physics, Payame Noor University, P. O. Box 19395-4697, Tehran, Iran Raheleh Tabari Juybari Department of Radiology Technology, Behbahan Faculty of Medical Sciences, Behbahan, Iran Masoumeh Dorri Giv Nuclear Medicine Research Center, Department of Nuclear Medicine, Ghaem Hospital, Mashhad University of Medical Sciences, Mashhad, Iran Maryam Yaftian Department of Medical Radiation Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran 2023 10 29 2023 12 02 Purpose: The breast is a radiosensitive organ and it is important to prevent the Contralateral Breast (CLB) from irradiation in radiotherapy. In this study, the received dose of CLB was calculated and compared between two breast radiotherapy techniques, including physical stationary and motorized wedged fields. Materials and Methods: Forty female patients undergoing breast radiotherapy with supraclavicular involvement were randomly selected. Twenty were treated with the tangential fields using physical wedges and twenty patients were treated with the tangential fields using motorized wedges. Three thermo-luminescent dosimeters (TLD GR-200) were placed on the CLB skin to estimate the breast dose. Dosimetric parameters for target tissue and organs at risk (OARs) were obtained from the plans of the evaluated techniques and compared to find the differences. CLB doses were compared between the radiotherapy techniques using an independent T-test. Results: There were no significant differences in the target tissue and OARs dosimetric parameters between the evaluated radiotherapy techniques. The results showed that the measured CLB skin doses in patients treated with the motorized wedges were significantly higher than the physical wedge radiotherapy technique, 201.5±20.4 mGy vs. 159.8 ±14.2 mGy (P<0.05).Conclusion: The physical wedged fields technique had lower doses for CLB compared to the fields using motorized wedges. Therefore, it can be proposed to use tangential physical wedged fields for patients with high concern about the CLB. Furthermore, more research considering radiotherapy techniques without using wedges in medial tangent fields and other relevant parameters can be performed to obtain a better evaluation of the CLB dose. https://fbt.tums.ac.ir/index.php/fbt/article/view/858 https://fbt.tums.ac.ir/index.php/fbt/article/download/858/415

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 12 2 2025 03 01 376 386 Mahya Naghipoor-Alamdari Biomedical Engineering Department, Amirkabir University of Technology, Tehran, Iran Jafar Zamani School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran Farzaneh Keyvanfard School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran Abbas Nasiraei-Moghadam Department of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran 2023 11 02 2023 12 09 Purpose: Schizophrenia (SZ), which affects 0.45% of adults worldwide, is a complex mental illness with unknown causes and mechanisms. Neuroimaging techniques have been used to study changes in the brain of patients with SZ. In this study, we aim to construct brain subnetworks, analyze the association of structure with function, and investigate them with graph measures. We hope to identify important subnetworks and graph measures for SZ diagnosis. Materials and Methods: This study investigates the structural and functional brain connectivity of 27 healthy controls (HC) and 27 patients with SZ. Independent component analysis (ICA) and joint ICA (jICA) are used to construct subnetworks based on functional and structural connectivity. An association between structural and functional connectivity is examined. Joint functional and structural subnetworks are also examined and compared with independent analysis of functional and structural subnetworks. Several graph measures are used in the whole brain and its subnetworks. Results: In this study, we investigated brain connectivity in HC and SZ patients using graph measures. The study analyzed both the whole brain and brain subnetworks to better understand the importance of partitioning the brain into subregions. Our results suggest that analyzing whole brain may not be the most effective method for studying brain peculiarities of SZ patients. In addition, multimodal brain analysis has proven to be effective in understanding SZ. There is no one-to-one relationship between structural and functional connectivity in the brain. Certain measures such as maximum modularity, clustering coefficient, network strength, global efficiency and path length were important in distinguishing patients with SZ from HCs in specific subnetworks. This study recommends further investigation of specific subnetworks that overlap with default mode, visual, and somatomotor resting state networks. Conclusion: This study emphasizes importance of subnetwork and multimodal analysis for understanding SZ disease. https://fbt.tums.ac.ir/index.php/fbt/article/view/870 https://fbt.tums.ac.ir/index.php/fbt/article/download/870/386

Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 12 2 2025 03 01 387 398 Bahador Bahadorzadeh Nuclear Engineering Department, School of Mechanical Engineering, Faculty of Engineering, Shiraz University, Shiraz, Iran Reza Faghihi Nuclear Engineering Department, School of Mechanical Engineering, Faculty of Engineering, Shiraz University, Shiraz, Iran Sedigheh Sina Nuclear Engineering Department, School of Mechanical Engineering, Faculty of Engineering, Shiraz University, Shiraz, Iran Ahdiyeh Aghaz Radiation Application Research School, Nuclear Science and Technology Research Institute, Tehran, Iran Arman Rahmim Nuclear Engineering Department, School of Mechanical Engineering, Faculty of Engineering, Shiraz University, Shiraz, Iran Mohammad Reza Ay Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran 2023 09 24 2023 12 02 Purpose: The Xtrim-PET preclinical scanner is specifically designed for positron emission tomography (PET) imaging of small laboratory animals. This study aims to increase the spatial resolution of the scanner by implementing gantry wobbling. Materials and Methods: The gantry wobbling was evaluated using the Gate Monte Carlo code. To prevent image blurring during gantry wobbling, all locations detected in the 3D output were corrected in the sinogram matrix according to the coincidence time of annihilation photons and the gantry motion. In order to evaluate the performance of the scanner using the wobbling motion data acquisition technique, coincidence data from the scanning of NEMA-NU4 and Hot-Rod phantoms were modified, reconstructed and compared to without wobbling mode. Results: The spatial resolution in the center of the scanner with and without implementing