Biomedical Engineering: Applications, Basis and Communications最新文献

{"title":"The application of intelligent sensors in medical research: a review","authors":"Yuxiang Zhuang, Yumei Fu, Sheng-Chia Huang, Shuai Gong","doi":"10.53388/bmec2023017","DOIUrl":"https://doi.org/10.53388/bmec2023017","url":null,"abstract":"The rapid advancement of biomedicine in the twenty-first century has been facilitated by the constant innovation in biomedical technology. The most crucial issue in the field of medicine is to use sensor technology to gather information from primitive organisms, particularly the human body. Design, development, and application of biomedical sensors in the study of clinical diseases’ diagnosis and therapy have all been significantly aided by the advancement of medicine. The interest in creating sensors significantly increased in the 1960s. Chemical and biological sensors have been swiftly created in response to an urgent practical necessity, enabling the creation of selective sensors for the direct detection of diverse ions and compounds. The traditional large-size sensors are quickly turning into miniature sensors and are rapidly applied in biological and medical fields. Currently, wearable electronic blood pressure monitors, home blood glucose meters, and quick body surface digital thermometers are commonly used. The advent of a wide variety of medical-grade wearable sensors that will enable real-time biometric data tracking of a large range of physiological characteristics will likely be one of the most revolutionary, exciting, and difficult changes to come to medicine over the next several years. For possible uses in the entertainment, health monitoring, and medical care industries, high-performance flexible strain sensors connected to clothing or human skin are necessary. The use of sensors in the development of biomedical diagnostic tools and medical equipment will enhance human quality of life in the twenty-first century. This article will introduce the current medical sensor field related to sensors for physical quantities, sensors for chemical quantities, sensors for biological quantities such as electronic nose","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"51 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78304126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The fabrication of hydroxyapatite mineralized hydrogels for bone tissue engineering","authors":"Xiumei Zhang, Jin-Qiao Jia, Yu Cao, Yan Wei, Yinchun Hu, X. Lian, Ziwei Liang, Di Huang","doi":"10.53388/bmec2023010","DOIUrl":"https://doi.org/10.53388/bmec2023010","url":null,"abstract":"","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"117 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79058404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Supramolecular polymeric materials based on macrocyclic hosts for cancer therapy","authors":"Shan Wu, Miaomiao Yan, Jiong Zhou","doi":"10.53388/bmec2023015","DOIUrl":"https://doi.org/10.53388/bmec2023015","url":null,"abstract":"All over the world, cancer has become the leading cause of human death and a major obstacle to improving life expectancy [1]. Although the survival rate of cancer has been improved significantly thanks to the continued efforts of researchers, there is still a long way to go before cancer is completely cured [2, 3]. Surgery, radiotherapy, and chemotherapy are still the mainstays of cancer therapy [4, 5]. However, limitations and side effects mean that these methods do not meet the current clinical need for cancer therapy [6–8]. For example, (i) surgery has a high risk, low success rate, and various complications; (ii) as a topical treatment, radiotherapy requires to be highly sensitive to tumors and damages healthy tissue; (iii) chemotherapy leads to a sharp reduction in the number of immune cells and the damage of visceral function [9–11]. Therefore, it is urgent to develop new treatment strategies to solve these problems, improving the therapeutic effect of cancer [12]. Supramolecular chemistry is a frontier science proposed by J.M. Lehn, a Nobel laureate chemist in 1987, which intersected with biology, physics, materials science, information science, and environmental science [13–19]. The main research directions of supramolecular chemistry can be divided into host–guest chemistry and supramolecular assembly chemistry [20–25]. Among them, supramolecular polymers based on macrocyclic hosts (such as cyclodextrins, calixarenes, cucurbiturils, and pillararenes) are a new type of polymers that combines the advantages of supramolecules and polymers (Figure 1) [26–36]. Compared with traditional polymers, macrocyclic hosts-based supramolecular polymers are mainly formed by connecting multiple repeating units through host–guest interactions [37–42]. The reversibility and dynamics of host–guest interactions give supramolecular polymeric materials unparalleled advantages, such as sensitive stimuli-responsiveness, good biocompatibility, and high biodegradability [43–47]. Based on these advantages, macrocyclic hosts-based supramolecular polymeric materials provide a new strategy for cancer therapy [48–52]. At present, macrocyclic hosts-based supramolecular polymeric materials have been widely used in cancer therapy [53–59]. For example, Huang and co-workers developed an amphiphilic supramolecular diblock polymer based on the host–guest interaction between pillar[5]arene and viologen derivative (Figure 2a) [60]. The polymer was further self-assembled into polymeric vesicles in water to deliver doxorubicin (DOX), which improved the therapeutic effect of DOX and reduced the side effects on healthy tissues. Additionally, Zhang and co-workers constructed a cucurbit[7]uril-based supramolecular polymer to load oxaliplatin (OxPt) and mitochondrial targeting peptide (N-Ph-KLAK) effectively (Figure 2b) [61]. In the tumor microenvironment, OxPt and N-Ph-KLAK were competitively released to destroy the nucleus and mitochondria, respectively, successfully inhibiting the ","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"23 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82510220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ultrasound-responsive microbubbles in antibacterial therapy","authors":"Xiao-ye Li, Weijun Xiu, Dong Yang, Heng Dong","doi":"10.53388/bmec2023007","DOIUrl":"https://doi.org/10.53388/bmec2023007","url":null,"abstract":"","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"11 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81649582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computational biology in topical bioactive peptide discovery for cosmeceutical application: a concise review","authors":"Xu-Hui Li, Wenfeng Su, Feifei Wang, Ke Li, Jingjing Zhu, Si-Yu Zhu, Sishun Kang, Cong-Fen He, Jun-Xiang Li, Xiao-Ling Lin","doi":"10.53388/bmec2023014","DOIUrl":"https://doi.org/10.53388/bmec2023014","url":null,"abstract":"Regenerative medicine and anti-aging research have made great strides at the molecular and cellular levels in dermatology and the medical aesthetic field, targeting potential treatments with skin therapeutic and intervention pathways, which make it possible to develop effective skin regeneration and repair ingredients. With the rapid development of computational biology, bioinformatics as well as artificial intelligence (A.I.), the development of new ingredients for regenerative medicine has been greatly accelerated, and the success rate has been improved. Some application cases have appeared in topical skin regeneration and repair scenarios. This review will briefly introduce the application of bioactive peptides in skin repair and anti-aging as emerging ingredients in cosmeceutics and emphasize how A.I. based computational biology technology may accelerate the development of innovative peptide molecules and ultimately translate them into potential skin regenerative and anti-aging scenarios. Typically, two research routines have been summarized and current limitations as well as directions were discussed for border applications in future research.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"1 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83657177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DETECTION OF AUTISM SPECTRUM DISORDER BY FEATURE EXTRACTION OF EEG SIGNALS AND MACHINE LEARNING CLASSIFIERS","authors":"Qaysar Mohi ud Din, A. Jayanthy","doi":"10.4015/s1016237222500466","DOIUrl":"https://doi.org/10.4015/s1016237222500466","url":null,"abstract":"Autism Spectrum Disorder (ASD), a neurodevelopmental disorder, impacts the subject’s social communication and interaction and the subjects exhibit restricted and repetitive behaviors. Subjects with ASD may need assistance throughout their life, depending on the severity. Early diagnosis of ASD is therefore critical for early intervention. ASD is diagnosed clinically based on behavioral assessments of the subjects, which results in delayed diagnosis, since the typical ASD traits due to aberrant brain development take time to develop. Neurological disorders associated with aberrant brain electrical activity have been detected by analyzing Electroencephalogram (EEG) signal patterns. In this study, we used features extracted from EEG brain waves to categorize ASD and normal subjects using Machine Learning (ML) classifiers. Autoregressive (AR) coefficients, Shannon entropy, Multifractal wavelet leader estimates, Multiscale wavelet variance and Discrete Fourier Transform (DFT) coefficients were extracted from EEG brain waves of ASD and normal subjects. Support Vector Machine (SVM), Decision Tree (DT), Logistic Regression (LR), k-Nearest Neighbor (k-NN) and Feed-forward Neural Network (FNN) were utilized as classification algorithms to categorize the ASD subjects and the control subjects. An accuracy of 90% was achieved by k-NN algorithm using AR features, Shannon entropy, Multifractal wavelet leader estimates and Multiscale wavelet variance estimates in ASD categorization. An accuracy of 93% was achieved by k-NN using the DFT features. The findings of this study indicate that features extracted from EEG are sufficient enough for categorization of ASD subjects and the control subjects.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"28 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81015555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"COMPARATIVE ANALYSIS OF TRADITIONAL CLASSIFICATION AND DEEP LEARNING IN LUNG CANCER PREDICTION","authors":"K. Bhavani, Gopalakrishna M T","doi":"10.4015/s101623722250048x","DOIUrl":"https://doi.org/10.4015/s101623722250048x","url":null,"abstract":"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.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"87 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76564229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"THE STUDY ON THE EFFECT OF ARTIFICIAL OSTEOPOROSIS CREATED BY COMBINED OVARIECTOMY AND CALCIUM-RESTRICTED DIETS IN A PORCINE MODEL","authors":"Jui-Yang Hsieh, Yao-Horng Wang, Jyh‐Horng Wang, Po‐Quang Chen, Yi-You Huang","doi":"10.4015/s1016237222500545","DOIUrl":"https://doi.org/10.4015/s1016237222500545","url":null,"abstract":"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 ([Formula: see text]). The initial changes in both the BMD levels (T1/T2) were found to be statistically insignificant ([Formula: see text] and [Formula: see text], respectively). However, upon comparison of later BMD changes (T3/T4, T1/T3 and T1/T4), statistically significant elevations were found ([Formula: see text] 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.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"135 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76723508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ANALYSIS OF CARDIOVASCULAR, CARDIORESPIRATORY, AND VASCULO- RESPIRATORY SIGNALS USING DIFFERENT MACHINE LEARNING TECHNIQUES","authors":"Kirti Singh, I. Saini, Neetu Sood","doi":"10.4015/s1016237222500454","DOIUrl":"https://doi.org/10.4015/s1016237222500454","url":null,"abstract":"Many physiological signals such as heart rate (HR), blood pressure (BP), and respiration (RESP) affect each other, and the inter-relation within and between these signals can be linear or nonlinear. Therefore, this paper’s main aim is to extract the relevant features using the information domain coupling technique based on conditional transfer entropy to detect the nonlinearity and coupling changes between the physiological signals and to classify the database using various machine learning classifiers to study the aging changes in the contribution of HR, BP, and RESP. In the proposed work, the physiological signals, i.e. HR, BP, and RESP, were pre-processed using various filtering methods, then features of physiological signals were extracted using linear and nonlinear techniques. After the pre-processing and extraction of features, the extracted features are classified using machine learning classifiers to classify the physiological signal database to study the aging changes in the contribution of HR, BP, and RESP. The data has been taken from the standard Fantasia database of healthy young and old subjects and self-recorded data of healthy young and old subjects for this study. Naive Bayes (NB), Support vector machine (SVM), K-Nearest Neighbor (KNN), Logistic Regression (LR), and Artificial Neural Network (ANN) were trained using five-fold cross-validation on the physiological dataset. It is concluded from the results that by adding the coupling features, the efficiency of the final prediction of the classifier increased from [Formula: see text]% to [Formula: see text]% obtained by LR, [Formula: see text]% to [Formula: see text]% obtained by SVM, [Formula: see text]% to [Formula: see text]% obtained by KNN, [Formula: see text]% to [Formula: see text]% obtained by NB, and [Formula: see text]% to [Formula: see text]% obtained by ANN. The ANN performs well when provided with the coupling features, gives a maximum accuracy of [Formula: see text]% and very high sensitivity of [Formula: see text]% and specificity of [Formula: see text]%, and takes much less computational time, when compared to other machine learning algorithms on same length of database.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"20 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87815970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FACIAL EMOTION DETECTION OF THERMAL AND DIGITAL IMAGES BASED ON MACHINE LEARNING TECHNIQUES","authors":"B. Sathyamoorthy, U. Snehalatha, T. Rajalakshmi","doi":"10.4015/s1016237222500521","DOIUrl":"https://doi.org/10.4015/s1016237222500521","url":null,"abstract":"The aim of the study is (i) to determine temperature distribution for various emotions from the facial thermal images; (ii) to extract statistical features from the facial region using GLCM feature extraction technique and to classify the emotions using machine learning classifiers such as SVM and Naïve Bayes; (iii) to develop the custom CNN model for the classification of various emotions and compare its performance with machine learning classifiers. Fifty normal subjects were considered for the study to analyze the facial emotions using thermal and digital images. The four different emotions, such as happy, angry, neutral and sad, were obtained with a total image of 200 thermal and 200 digital images. Ten statistical features were extracted using the GLCM method from both thermal and digital images and fed into the machine learning classifiers. After data augmentation, the images are fed into the custom CNN model for the classification of various emotions. The SVM classifier produced an accuracy of 80% in thermal images and 76.5% in digital images compared to the Naive Bayes classifier. The developed CNN model improved the classification accuracy to 94.3% and 90.3% for thermal and digital image, respectively, for the multi-class classification of facial emotions. The CNN model implemented using thermal images provided better classification accuracy than digital images in facial emotion recognition. Hence, it was proved that thermal imaging techniques resulted in better performance in predicting facial emotion than digital images.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"102 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74980774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}