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Authors:Nabil K. Al Shamaa, Rashid Ali Fayadh, Mousa K. Wali Abstract: Biomedical Engineering: Applications, Basis and Communications, Ahead of Print. The detection of sleep is important because it contributes to most road accidents especially high levels of deep sleep while driving. Sleep detection is based on electrooculogram (EoG) signal as sleep causes various changes to this signal. Drivers travelling for long hours, especially those working under transportation field are more likely to sleep in the middle of their journey. In order to avoid this situation, drivers are aided with a system which is capable of monitoring the drivers’ condition depending on communication between the driving simulator and the subject EoG signal as many sleep detection devices are dependent upon eye behavior and movement in addition to pupil size and eye closure for certain periods. Therefore, to solve the problem of detecting sleep while driving, this work extracted different features from the EoG signal precisely from its frequency range (0–25[math]Hz) and (25–37.5[math]Hz) by discrete wavelet transform technique. In this research, 15 subjects have been set in a driving environment for more than 1[math]h for collecting the sleep EoG signal data by low power sensors. The EoG signal is recorded using Cobra3 Data acquisition set and few features (minimum, maximum, mean, standard deviation (SD), mode, energy, median and variance) are extracted using discrete wavelet transform. These features have been used to classify three classes (sleep 0, sleep 0, sleep 1) using support vector machine (SVM). This classifier depends upon the fusion of the above features to get an accuracy of 78% for high-level sleep detection based on db4 wavelet. Citation: Biomedical Engineering: Applications, Basis and Communications PubDate: 2023-05-05T07:00:00Z DOI: 10.4015/S1016237223500102
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Authors:Fatemeh Moayedi, Javad Karimi, Seyed Ebrahim Dashti Abstract: Biomedical Engineering: Applications, Basis and Communications, Ahead of Print. Colon cancer is one of the most common spread cancers in the world, which leads to total death of 10%. Prediction of onset of cancer, and the cause of its development in these patients can be of an enormous help and relief to those affected, as they can get back their “normal” life. Data mining and machine learning are important intelligent tools for classification, prediction and hidden relation extraction between patient information. We collected data from Shahid Faghihi Hospital in Shiraz. Features collected are as follows: Gender, age, duration of cancer before surgery, number of times the patients used bathroom, taking anti-inflammatory drug prednisolone, duration of drug use and dosage, kind of surgery and number of times consulted and retreatment of surgery, incontinence, etc. After pre-processing and data cleaning stages, effective features were extracted, and also occurrence of cancer predicts by using different classification algorithms. Then association rule mining algorithms like Apriori were used for obtaining any internal hidden relation between entries. Approaching them with different algorithms and assessing them with support vector machine was with highest prediction accuracy (84%). Due to unbalanced dataset, we chose cost sensitive support vector machine. In another aspect, after applying Apriori algorithm, the conditions of non-inflammation were extracted based on dataset features. Some significant outcomes are in what follows. If surgery treatment or diagnosed was less than 5 years, the possibility of developing colon cancer is lower. Also, as the duration of disease increases, the possibility of reoperation increases, as confirmed by the interiors. Since this issue with these features was raised for the first time in this paper at the suggestion of internists, early detection of cancer and also the extraction of effective laws can be of help to the medical community. In future, to get higher accuracy, the improvement of the dataset in terms of number of samples and colonoscopy image features is considered. Citation: Biomedical Engineering: Applications, Basis and Communications PubDate: 2023-05-05T07:00:00Z DOI: 10.4015/S1016237223500114
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Authors:Alka Singh, Varun P. Gopi, Anju Thomas, Omkar Singh Abstract: Biomedical Engineering: Applications, Basis and Communications, Ahead of Print. Coronavirus Disease 2019 (COVID-19) is a terrible illness affecting the respiratory systems of animals and humans. By 2020, this sickness had become a pandemic, affecting millions worldwide. Prevention of the spread of the virus by conducting fast tests for many suspects has become difficult. Recently, many deep learning-based methods have been developed to automatically detect COVID-19 infection from lung Computed Tomography (CT) images of the chest. This paper proposes a novel dual-scale Convolutional Neural Network (CNN) architecture to detect COVID-19 from CT images. The network consists of two different convolutional blocks. Each path is similarly constructed with multi-scale feature extraction layers. The primary path consists of six convolutional layers. The extracted features from multipath networks are flattened with the help of dropout, and these relevant features are concatenated. The sigmoid function is used as the classifier to identify whether the input image is diseased. The proposed network obtained an accuracy of 99.19%, with an Area Under the Curve (AUC) value of 0.99. The proposed network has a lower computational cost than the existing methods regarding learnable parameters, the number of FLOPS, and memory requirements. The proposed CNN model inherits the benefits of densely linked paths and residuals by utilizing effective feature reuse methods. According to our experiments, the proposed approach outperforms previous algorithms and achieves state-of-the-art results. Citation: Biomedical Engineering: Applications, Basis and Communications PubDate: 2023-05-05T07:00:00Z DOI: 10.4015/S1016237223500126
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Authors:Farhad Abedinzadeh Torghabeh, Yeganeh Modaresnia, Mohammad Mahdi khalilzadeh Abstract: Biomedical Engineering: Applications, Basis and Communications, Ahead of Print. Alzheimer’s disease (AD) is the leading worldwide cause of dementia. It is a common brain disorder that significantly impacts daily life and slowly progresses from moderate to severe. Due to inaccuracy, lack of sensitivity, and imprecision, existing classification techniques are not yet a standard clinical approach. This paper proposes utilizing the Convolutional Neural Network (CNN) architecture to classify AD based on MRI images. Our primary objective is to use the capabilities of pre-trained CNNs to classify and predict dementia severity and to serve as an effective decision support system for physicians in predicting the severity of AD based on the degree of dementia. The standard Kaggle dataset is used to train and evaluate the classification model of dementia. Synthetic Minority Oversampling Technique (SMOTE) tackles the primary problem with the dataset, which is a disparity across classes. VGGNet16 with ReduceLROnPlateau is fine-tuned and assessed using testing data consisting of four stages of dementia and achieves an overall accuracy of 98.61% and a specificity of 99% for a multiclass classification, which is superior to current approaches. By selecting appropriate Initial Learning Rate (ILR) and scheduling it during the training phase, the proposed method has the benefit of causing the model to converge on local optimums with better performance. Citation: Biomedical Engineering: Applications, Basis and Communications PubDate: 2023-05-04T07:00:00Z DOI: 10.4015/S1016237223500060
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Authors:R. K. Ahalya, U. Snekhalatha, Palani Thanaraj Krishnan Abstract: Biomedical Engineering: Applications, Basis and Communications, Ahead of Print. The study aims to develop a computerized hybrid model using artificial intelligence (AI) for the detection of rheumatoid arthritis (RA) from hand radiographs. The objectives of the study include (i) segmentation of proximal interphalangeal (PIP), and metacarpophalangeal (MCP) joints using the deep learning (DL) method, and features are extracted using handcrafted feature extraction technique (ii) classification of RA and non-RA participants is performed using machine learning (ML) techniques. In the proposed study, the hand radiographs are resized to [math] pixels and pre-processed using the various image processing techniques such as sharpening, median filtering, and adaptive histogram equalization. The segmentation of the finger joints is carried out using the U-Net model, and the segmented binary image is converted to gray scale image using the subtraction method. The features are extracted using the Harris feature extractor, and classification of the proposed work is performed using Random Forest and Adaboost ML classifiers. The study included 50 RA patients and 50 normal subjects for the evaluation of RA. Data augmentation is performed to increase the number of images for U-Net segmentation technique. For the classification of RA and healthy subjects, the Random Forest classifier obtained an accuracy of 91.25% whereas the Adaboost classifier had an accuracy of 90%. Thus, the hybrid model using a Random Forest classifier can be used as an effective system for the diagnosis of RA. Citation: Biomedical Engineering: Applications, Basis and Communications PubDate: 2023-04-28T07:00:00Z DOI: 10.4015/S1016237223500096
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Authors:Thanakorn Phumkuea, Phurich Nilvisut, Thakerng Wongsirichot, Kasikrit Damkliang Abstract: Biomedical Engineering: Applications, Basis and Communications, Ahead of Print. Malaria is a life-threatening mosquito-borne disease. Recently, the number of malaria cases has increased worldwide, threatening vulnerable populations. Malaria is responsible for a high rate of morbidity and mortality in people all around the world. Each year, many people, die from this disease, according to the World Health Organization (WHO). Thick and thin blood smears are used to determine parasite habitation and computer-aided diagnosis (CADx) techniques using machine learning (ML) are being used to assist. CADx reduces traditional diagnosis time, lessens socio-economic impact, and improves quality of life. This study develops a simplified model with selective features to reduce processing power and further shorten diagnostic time, which is important to resource-constrained areas. To improve overall classification results, we use a decision tree (DT)-based approach with image pre-processing called optimal features to identify optimal features. Various feature selection and extraction techniques are used, including information gain (IG). Our proposed model is compared to a benchmark state-of-art classification model. For an unseen dataset, our proposed model achieves accuracy, precision, recall, F-score, and processing time of 0.956, 0.949, 0.964, 0.956, and 9.877 s, respectively. Furthermore, our proposed model’s training time is less than those of the state-of-the-art classification model, while the performance metrics are comparable. Citation: Biomedical Engineering: Applications, Basis and Communications PubDate: 2023-04-27T07:00:00Z DOI: 10.4015/S1016237223500047
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Authors:Shokufeh Akbari, Faraz Edadi Ebrahimi, Mehdi Rajabioun Abstract: Biomedical Engineering: Applications, Basis and Communications, Ahead of Print. Nowadays, the world confronts a highly infectious pandemic called coronavirus (COVID-19) and over 4 million people worldwide have now died from this illness. So, early detection of COVID-19 outbreak and distinguishing it from other diseases with the same physical symptoms can give enough time for treatment with true positive results and prevent coma or death. For early recognition of COVID-19, several methods for each modality are proposed. Although there are some modalities for COVID-19 detection, electrocardiography (ECG) is one of the fastest, the most accessible, the cheapest and the safest one. This paper proposed a new method for classifying COVID-19 patients from other cardiovascular disease by ECG signals. In the proposed method, Resnet50v2 which is a kind of convolutional neural network, is used for classification. In this paper because of image format of data, first data with image format are applied to the network and then for comparison, ECG images are changed to signal format and classification is done. These two strategies are used for COVID-19 classification from other cardiac abnormalities with different filter sizes and the results of strategies are compared with each other and other methods in this field. As it can be concluded from the results, signal-based data give better accuracy than image classification at best performance and it is better to change the image format to signals for classification. The second result can be found by comparing with other methods in this field, the proposed method of this paper gives better performance with high accuracy in COVID-19 classification. Citation: Biomedical Engineering: Applications, Basis and Communications PubDate: 2023-04-27T07:00:00Z DOI: 10.4015/S1016237223500059
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Authors:Kathirvelu Dhandapani, P. Vinupritha, D. Parimala, E. J. Eucharista Abstract: Biomedical Engineering: Applications, Basis and Communications, Ahead of Print. Background: Osteoporosis results in an increased risk of fracture among aging women. A strong connection exists for bone health with tooth loss, menopause, diet, BMI and hysterectomy. Purpose: To study the impact of heel BMD with age, BMI, menopausal status, hysterectomy and tooth loss among people living in Chennai metropolitan neighborhood. Materials and Methods: The study involved ([math], age: [math] years) women, which included women with normal BMD ([math] = 35, age: [math] years), Osteopenia ([math], age: [math] years) and Osteoporosis ([math], age: [math] years). All the participants underwent BMD assessment at their right heel using an Ultrasound densitometer system (Model: CM-200, Manufacturer: FURUNO ELECTRIC CO. LTD., Japan). The subjects were classified into various subgroups based on BMD, age, Menopausal status, hysterectomy and tooth loss. Results: The mean age of women attaining menopause and those undergoing hysterectomy are [math] years and [math] years, respectively. The decrease of heel BMD was very prominent among women having more than two tooth extracted, menopause and hysterectomy. It was found that approximately 90% of the studied population were suffering from either osteopenia or osteoporosis in their post-menopausal period. Conclusion: Women aged above 50 years are at greater risk of osteoporosis due to post-menopausal phase, high probability of undergoing hysterectomy and tooth loss. Therefore, women should ensure sufficient consumption of calcium rich diet in their entire life cycle to ensure a healthy livelihood. Citation: Biomedical Engineering: Applications, Basis and Communications PubDate: 2023-04-01T07:00:00Z DOI: 10.4015/S101623722250051X
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Authors:J. Glory Precious, S. P. Angeline Kirubha, R. Premkumar, I. Keren Evangeline Abstract: Biomedical Engineering: Applications, Basis and Communications, Ahead of Print. The brain tumor is the most common destructive and deadly disease. In general, various imaging modalities such as CT, MRI and PET are used to evaluate the brain tumor. Magnetic resonance imaging (MRI) is a prominent diagnostic method for evaluating these tumors. Gliomas, due to their malignant nature and rapid development, are the most common and aggressive form of brain tumors. In the clinical routine, the method of identifying tumor borders from healthy cells is still a difficult task. Manual segmentation takes time, so we use a deep convolutional neural network to improve efficiency. We present a combined DNN architecture using U-net and MobilenetV2. It exploits both local characteristics and more global contextual characteristics from the 2D MRI FLAIR images. The proposed network has encoder and decoder architecture. The performance metrices such as dice loss, dice coefficient, accuracy and IOU have been calculated. Automated segmentation of 3D MRI is essential for the identification, assessment, and treatment of brain tumors although there is significant interest in machine-learning algorithms for computerized segmentation of brain tumors. The goal of this work is to perform 3D volumetric segmentation using BraTumIA. It is a widely available software application used to separate tumor characteristics on 3D brain MR volumes. BraTumIA has lately been used in a number of clinical trials. In this work, we have segmented 2D slices and 3D volumes of MRI brain tumor images. Citation: Biomedical Engineering: Applications, Basis and Communications PubDate: 2023-03-14T07:00:00Z DOI: 10.4015/S1016237222500557
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Authors:M. Sameera Fathimal, S. P. Angeline Kirubha, A. Jeya Prabha, S. Jothiraj Abstract: Biomedical Engineering: Applications, Basis and Communications, Ahead of Print. Diabetes mellitus (DM) indicates elevated glucose concentration in blood. In type 1 diabetes, the pancreas produces inadequate insulin whereas in type 2 diabetes, the body is incapable to utilize the insulin present. Insulin is required to transport glucose into the cells. The insulin resistance by the cells causes the glucose level in the blood to increase. At present, the clinical methods available to diagnose DM are invasive. The diagnosis of DM is done by either pricking the fingertip or drawing blood from the vein followed by the quantification of blood glucose in terms of [math]. Continuous monitoring is limited as skin is punctured or venous blood is extracted. Spectroscopic analysis of hair, nail, saliva and urine possess the potential to differentiate the hyperglycaemic from the healthy subjects facilitating non-intrusive diagnosis of diabetes. The variation in the incident wavelength following the interaction with the sample is measured by a spectrometer. Based on the energy of the excitation source, the molecular structures present in the sample will either vibrate or absorb and emit photons that produce a spectrum. The samples were collected from both the groups of subjects and pre-processed prior to further examination. The samples were then characterized using the Fourier-transform infrared (FTIR) spectroscopy. The spectral output was pre-processed, filtered and analyzed so as to discriminate between the diabetic and healthy subjects. Although the spectral band of nail and hair samples appears to be identical, a difference in the amplitude was observed between both diabetic and normal subjects at 1450, 1520, 1632, 2925 cm[math]. The area under curve (AUC) in the range of 3600 to 3100 cm-1 is a prominent marker in the discrimination. The peak wavelength and AUC were utilized as a biomarker to discriminate the diabetic and normal individuals. Citation: Biomedical Engineering: Applications, Basis and Communications PubDate: 2023-03-14T07:00:00Z DOI: 10.4015/S1016237223500023
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Authors:Sonalee P. Suryawanshi, Bhaveshkumar C. Dharmani Abstract: Biomedical Engineering: Applications, Basis and Communications, Ahead of Print. Thermography is a noncontact, noninvasive imaging technology that is commonly utilized in the medical profession. As early identification of cancer is critical, the computer-assisted method can enhance the diagnosis rate, curing, and survival of cancer patients. Early diagnosis is one of the major essential steps in decreasing the health and socioeconomic consequences of this condition, given the high cost of therapy and the large prevalence of afflicted people. Mammography is currently the majorly utilized procedure for detecting breast cancer. Yet, owing to the low contrast that occurs from a thick breast, mammography is not advised for young women, and alternate methods must be investigated. This work plans to develop a comparative evaluation of two well-performing heuristic-based expert systems for detecting thermogram breast cancer. The thermogram images are taken from the standard DMR dataset. Then, the given images are transferred to the pre-processing stage. Here, the input thermogram images are accomplished by contrast enhancement and mean filtering. Then the Gradient Vector Flow Snakes (GVFS) model is adopted for breast segmentation, and Optimized Fuzzy [math]-Means Clustering (OFCM) is developed for abnormality segmentation. From the segmented region of interest, the entropy-based features are acquired. In the classification phase, the “Heuristic-based Support Vector Machine” (HSVM) and “Heuristic-based Neural Network” (HNN) are introduced, which diagnose the breast cancer-affected images. The modifications on SVM and NN are extended by the Oppositional Improvement-based Tunicate Swarm Algorithm (OI-TSA). Furthermore, the suggested models are compared to the traditional SVM and NN classifiers, as well as other classifiers, to validate their competitive performance. From the results, the better accuracy and precision of the designed OI-TSA–HNN model are found to be 96% and 98.4%, respectively. Therefore, the findings confirm that the offered approach shows effectiveness in thermogram breast cancer detection. Citation: Biomedical Engineering: Applications, Basis and Communications PubDate: 2023-03-02T08:00:00Z DOI: 10.4015/S1016237222500478
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Authors:S. Sharanya, Sridhar P. Arjunan Abstract: Biomedical Engineering: Applications, Basis and Communications, Ahead of Print. Identifying Cardiac Autonomic Neuropathy (CAN) in the early stages of proliferation demands more prominent techniques with a reliable significance of identification. CAN being a subclinical consequence that is the leading cause of death in individuals with diabetes mellitus (DM), which is common among one in four people above an average age of 45 years, calls for a more dependable technique for analysis. This study investigates the complexity in prominent time segments (RR, QT and ST) of ECG using different entropy measures and four nonlinear fractal dimension (FD) measures including box counting, Petrosian, Higuchi’s and Katz’s methods. Measures of statistical significance were implemented using Wilcoxon, Mann–Whitney and Kruskal–Wallis tests. The results of the study provide an original approach to diagnostics that reveals the fact that, instead of analyzing the signal running for the whole length, complexity measures can be achieved, if the intervals of the signal are studied including a combination of features rather than any one feature considered for diagnosis. A significance level of [math] is achieved in more segments of ECG considered at intervals of time compared to one data recorded at the 20th minute between CAN+ and CAN− groups for both FD and entropy. Neural Network (NN) classification shows the accuracies of 84.61% and 60% in FD and entropy, respectively, computed every fifth minute. The accuracies from the model for the data collected at the 20th minute for FD and entropy are 50.22% and 30.33%, respectively, between the groups. Citation: Biomedical Engineering: Applications, Basis and Communications PubDate: 2023-02-24T08:00:00Z DOI: 10.4015/S1016237223500035
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Authors:Sumit Tripathi, Neeraj Sharma Abstract: Biomedical Engineering: Applications, Basis and Communications, Ahead of Print. The early detection and treatment of COVID-19 infection are necessary to save human life. The study aims to propose a time-efficient and accurate method to classify lung infected images by COVID-19 and viral pneumonia using chest X-ray. The proposed classifier applies end-to-end training approach to classify the images of the set of normal, viral pneumonia and COVID-19-infected images. The features of the two infected classes were precisely captured by the extractor path and transferred to the constructor path for precise classification. The classifier accurately reconstructed the classes using the indices and the feature maps. For firm confirmation of the classification results, we used the Matthews correlation coefficient (MCC) along with accuracy and F1 scores (1 and 0.5). The classification accuracy of the COVID-19 class achieved was about ([math])% with MCC score ([math]). The classifier is distinguished with great precision between the two nearly correlated infectious classes (COVID-19 and viral pneumonia). The statistical test suggests that the obtained results are statistically significant as [math]. The proposed method can save time in the diagnosis of lung infections and can help in reducing the burden on the medical system in the time of the pandemic. Citation: Biomedical Engineering: Applications, Basis and Communications PubDate: 2023-02-08T08:00:00Z DOI: 10.4015/S1016237223500011
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Authors:R. Richa, U. Snekhalatha Abstract: Biomedical Engineering: Applications, Basis and Communications, Ahead of Print. Childhood obesity is a preventable disorder which can reduce the risk of the comorbidities linked with an adult obesity. In order to improve the lifestyle of the obese children, early and accurate detection is required by using some non-invasive technique. Thermal imaging helps in evaluation of childhood obesity without injecting any form of harmful radiation in human body. The goal of this proposed research is to evaluate the body surface temperature in abdominopelvic and cervical regions and to evaluate which region is best for predicting childhood obesity using thermal imaging. Next, to customize the ResNet-18 and VGG-19 architecture using transfer learning approach and to obtain the best modified classifier and to study the classification accuracy between normal and obese children. The two-study region which was selected for this study was abdominopelvic and cervical region where the mean skin surface temperature was recorded. From the two selected body regions, abdominopelvic region has depicted highest temperature difference of 10.98% between normal and obese subjects. The proposed modified ResNet-18 model produced an overall accuracy of 94.2% than the modified VGG-19 model (86.5%) for the classification of obese and normal children. Thus, this study can be considered as a non-invasive and cost-effective way for pre-screening the obesity condition in children. Citation: Biomedical Engineering: Applications, Basis and Communications PubDate: 2023-02-01T08:00:00Z DOI: 10.4015/S1016237222500533
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Authors:K. Bhavani, M. T. Gopalakrishna Abstract: Biomedical Engineering: Applications, Basis and Communications, Ahead of Print. The cancer is an intimidating illness. Extra care is necessary while making a diagnosis. To aid the identification process, medical imaging plays a crucial role by producing images of the internal organs of the body for better diagnosis of cancer. Medical images are typically utilized by radiologists, engineers, and clinicians to spot the inner constitution of either individual patients or group of individuals. Most doctors prefer computed tomography (CT) images for initial screening of cancer — mainly lung cancer. To achieve deeper understanding and categorization of lung cancer, diverse machine learning techniques are employed in image classification. Many research works have been done on the classification of CT images with different algorithms, but they failed to reach 100% accuracy. By applying methods like Support Vector Machine, deep learning system like artificial neural network (ANN) and proposed convolution neural network (CNN), a computerized system can be built for truthful classification. The models are built as a classification system that can identify the nodule, if present in the lungs, as benign, malignant or normal or as benign or normal. Lung cancer datasets at Iraq National Center aimed at Cancer Diseases (IQ-OTHNCCD) and Iran Hospital-based CT images are used in this research. SVM, ANN, and proposed CNN classification techniques are applied to the datasets considered. This research work, proposes a model for classification of CT images with very promising accuracy on the datasets considered. Citation: Biomedical Engineering: Applications, Basis and Communications PubDate: 2022-12-10T08:00:00Z DOI: 10.4015/S101623722250048X
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Authors:Jui-Yang Hsieh, Yao-Horng Wang, Jyh-Horng Wang, Po-Quang Chen, Yi-You Huang Abstract: Biomedical Engineering: Applications, Basis and Communications, Ahead of Print. This study design is to evaluate the mid-term changes in bone mineral density (BMD) with combined calcium-restricted and ovariectomized miniature porcine models as a large animal model in osteoporosis. The combined old practice hangs on for almost 30 years. Four 6-month-old (T0) female miniature pigs were enrolled in this study. The pigs were fed a normal diet prior to the ovariectomy at the age of 1 year and 3 months (T1) but switched to a diet with restricted calcium content afterwards. Each of the pigs received dual-energy X-ray absorptiometry (DXA) once before ovariectomy, and once every three months (T2, T3, T4) after the ovariectomy to evaluate the changes in BMD. The body weight of all four subject pigs increased significantly during this study ([math]). The initial changes in both the BMD levels (T1/T2) were found to be statistically insignificant ([math] and [math], respectively). However, upon comparison of later BMD changes (T3/T4, T1/T3 and T1/T4), statistically significant elevations were found ([math] for all three comparisons). Ovariectomy and calcium-restricted diets are ineffective in achieving an osteoporotic porcine model based on BMD assessments. BMD levels of the subject pigs continued to rise until the point at which body growth had stopped because the ideal pigs for surgical experiments were far from maturity. This finding is not unexpected; after all, the subject pigs are not senile. Without violations of the physiology and Institutional Animal Care and Use Committee (IACUC) regulations, moreover, pigs could be fed by strictly calcium-restricted diets or deprived of soybean component feed. Furthermore, the alternative protocols in osteoporotic porcine model shall perform experiments as soon as possible after ovariectomy. We should take other studies about artificial osteoporotic pigs more into consideration whether it is based on a rational method. Citation: Biomedical Engineering: Applications, Basis and Communications PubDate: 2022-12-10T08:00:00Z DOI: 10.4015/S1016237222500545
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Authors:Nadia, Ekta Gandotra, Narendra Kumar Abstract: Biomedical Engineering: Applications, Basis and Communications, Ahead of Print. The nucleotide-binding domain leucine-rich repeat-containing (NLR) proteins plays significant role in the intestinal tissue repair and innate immunity. It recently added to the members of innate immunity effectors molecules. It also plays an essential role in intestinal microbiota and recently emerged as a crucial hit for developing ulcerative colitis (UC) and colitis-associated cancer (CAC). A machine learning-based approach for predicting NLR proteins has been developed. In this study, we present a comparison of three supervised machine learning algorithms. Using ProtR and POSSUM Packages, the features are extracted for the dataset used in this work. The models are trained with the input compositional features generated using dipeptide composition, amino acid composition, etc., as well as Position Specific Scoring Matrix (PSSM) based compositions. The dataset consists of 390 proteins for the negative and positive datasets. The five-fold cross-validation (CV) is used to optimize Sequential Minimal Optimization (SMO) library of Support Vector Machine (LIBSVM) and Random Forest (RF) parameters, and the best model was selected. The proposed work performs rationally well with an accuracy of 90.91% and 93.94% for RF as the best classifier for the Amino Acid Composition (AAC) and PSE_PSSM-based model. We believe that this method is a reliable, rapid and useful prediction method for NLR Protein. Citation: Biomedical Engineering: Applications, Basis and Communications PubDate: 2022-11-28T08:00:00Z DOI: 10.4015/S1016237222500508
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Authors:Mohammadreza Mehrabani, Mohammadreza Farahvash, Reza Samanipour, Sara Tabatabaee, Adel Marzban, Javad Rahmati, Amirhossein Tavakoli Abstract: Biomedical Engineering: Applications, Basis and Communications, Ahead of Print. The influence of facial appearance on people’s mental confidence is well-known in the modern life. The aim of this study was to evaluate the clinical efficacy of human-derived collagen gel injection on nasolabial folds and reveal a safe and cost-reasonable candidate for aesthetic and therapeutic cases. This assessment was a quasi-experimental interventional study on patients referred to the plastic surgery clinic of Imam Hospital in 2016–2017 who intended to treat nasolabial folds with outpatient methods and rapid recovery time regardless of age and gender restrictions. Allogenic collagen was injected at the site of nasolabial folds and the durability of the fillers was evaluated by the researcher, the neutral examiner and the participants based on the wrinkle severity rating scale (WSRS) and the global aesthetic improvement scale (GAIS). In terms of severity of nasolabial folds before intervention, the mean and severe state comprised 37 (69.81%) and 16 (30.19%) patients, respectively. The majority of subjects (more than 80%) in both assessments (the examiner and the researcher) demonstrated the improvement of the folds. The agreement between the two evaluators was relatively approximate ([math] and [math]). Regardless of the evaluation group, the trend of changes was statistically significant ([math]). Eventually, the duration of the filler efficacy was estimated to be 4–6 months. The allogenic collagen filler is recommended as an almost safe and cost-effective agent for nasolabial fold treatment in short to medium periods in case of low risk of the transmission of contamination and no need for allergic testing. Citation: Biomedical Engineering: Applications, Basis and Communications PubDate: 2022-10-26T07:00:00Z DOI: 10.4015/S1016237222500090
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Authors:M. Hashemi Kamangar, M. R. Karami Mollaei, Reza Ghaderi Abstract: Biomedical Engineering: Applications, Basis and Communications, Ahead of Print. The fiber directions in High Angular Resolution Diffusion Imaging (HARDI) with low fractional anisotropy or low Signal to Noise Ratio (SNR) cannot be estimated accurately. In this paper, the fiber directions are estimated using Particle Swarm Optimization and Spherical Deconvolution (PSO-SD). Fiber orientation is modeled as a Dirac delta function in [math]. The Spherical Harmonic Coefficients (SHC) of the Dirac delta function in the [math] direction are obtained using the rotational harmonic matrix and the SHC of the Dirac delta function in the [math]-axis. The PSO-SD method is used to determine ([math]). We generated noise-free synthetic data for isotropic regions (FA varied from 0.1 to 0.8) and synthetic data with two crossing fibers for anisotropic regions with SNRs of 20, 15, 10 and 5 (FA [math] 0.78). In the noise-free signal (FA [math] 0.3), the Success Ratio (SR) and Mean Difference Angle (MDA) of the PSO-SD method were 1∘ and 9.48∘, respectively. In the noisy signal (FA [math] 0.78, SNR [math] 10, crossing angle [math] 40), the SR and MDA of PSO-SD (with [math]) were 0.46∘ and 12.3∘, respectively. The PSO-SD method can estimate fiber directions in HARDI with low fractional anisotropy and low SNR. Moreover, it has a higher SR and lower MDA in comparison with those of the super-CSD method. Citation: Biomedical Engineering: Applications, Basis and Communications PubDate: 2021-12-02T08:00:00Z DOI: 10.4015/S1016237218500400
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