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Anbuselvam B, Gunasekaran BM, Srinivasan S, Ezhilan M, Rajagopal V, Nesakumar N. Wearable biosensors in cardiovascular disease. Clin Chim Acta 2024; 561:119766. [PMID: 38857672 DOI: 10.1016/j.cca.2024.119766] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2024] [Revised: 06/05/2024] [Accepted: 06/05/2024] [Indexed: 06/12/2024]
Abstract
This review provides a comprehensive overview of the latest advancements in wearable biosensors, emphasizing their applications in cardiovascular disease monitoring. Initially, the key sensing signals and biomarkers crucial for cardiovascular health, such as electrocardiogram, phonocardiography, pulse wave velocity, blood pressure, and specific biomarkers, are highlighted. Following this, advanced sensing techniques for cardiovascular disease monitoring are examined, including wearable electrophysiology devices, optical fibers, electrochemical sensors, and implantable cardiac devices. The review also delves into hydrogel-based wearable electrochemical biosensors, which detect biomarkers in sweat, interstitial fluids, saliva, and tears. Further attention is given to flexible electronics-based biosensors, including resistive, capacitive, and piezoelectric force sensors, as well as resistive and pyroelectric temperature sensors, flexible biochemical sensors, and sensor arrays. Moreover, the discussion extends to polymer-based wearable sensors, focusing on innovations in contact lens, textile-type, patch-type, and tattoo-type sensors. Finally, the review addresses the challenges associated with recent wearable biosensing technologies and explores future perspectives, highlighting potential groundbreaking avenues for transforming wearable sensing devices into advanced diagnostic tools with multifunctional capabilities for cardiovascular disease monitoring and other healthcare applications.
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Affiliation(s)
- Bhavadharani Anbuselvam
- School of Chemical & Biotechnology (SCBT), SASTRA Deemed University, Thanjavur 613 401, Tamil Nadu, India
| | - Balu Mahendran Gunasekaran
- School of Chemical & Biotechnology (SCBT), SASTRA Deemed University, Thanjavur 613 401, Tamil Nadu, India; Center for Nanotechnology & Advanced Biomaterials (CENTAB), SASTRA Deemed University, Thanjavur 613 401, Tamil Nadu, India
| | - Soorya Srinivasan
- Department of Mechanical Engineering, IIT Madras, Chennai 600036, Tamil Nadu, India
| | - Madeshwari Ezhilan
- Department of Biomedical Engineering, Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology, Vel Nagar, Avadi, Chennai 600062, Tamil Nadu, India.
| | - Venkatachalam Rajagopal
- Centre for Advanced Materials and Industrial Chemistry (CAMIC), School of Science, STEM College, RMIT University, GPO Box 2476, Melbourne, Victoria 3001, Australia
| | - Noel Nesakumar
- School of Chemical & Biotechnology (SCBT), SASTRA Deemed University, Thanjavur 613 401, Tamil Nadu, India; Center for Nanotechnology & Advanced Biomaterials (CENTAB), SASTRA Deemed University, Thanjavur 613 401, Tamil Nadu, India.
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Fang K, Wang J, Chen Q, Feng X, Qu Y, Shi J, Xu Z. Swin-cryoEM: Multi-class cryo-electron micrographs single particle mixed detection method. PLoS One 2024; 19:e0298287. [PMID: 38593135 PMCID: PMC11003668 DOI: 10.1371/journal.pone.0298287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 01/18/2024] [Indexed: 04/11/2024] Open
Abstract
Cryo-electron micrograph images have various characteristics such as varying sizes, shapes, and distribution densities of individual particles, severe background noise, high levels of impurities, irregular shapes, blurred edges, and similar color to the background. How to demonstrate good adaptability in the field of image vision by picking up single particles from multiple types of cryo-electron micrographs is currently a challenge in the field of cryo-electron micrographs. This paper combines the characteristics of the MixUp hybrid enhancement algorithm, enhances the image feature information in the pre-processing stage, builds a feature perception network based on the channel self-attention mechanism in the forward network of the Swin Transformer model network, achieving adaptive adjustment of self-attention mechanism between different single particles, increasing the network's tolerance to noise, Incorporating PReLU activation function to enhance information exchange between pixel blocks of different single particles, and combining the Cross-Entropy function with the softmax function to construct a classification network based on Swin Transformer suitable for cryo-electron micrograph single particle detection model (Swin-cryoEM), achieving mixed detection of multiple types of single particles. Swin-cryoEM algorithm can better solve the problem of good adaptability in picking single particles of many types of cryo-electron micrographs, improve the accuracy and generalization ability of the single particle picking method, and provide high-quality data support for the three-dimensional reconstruction of a single particle. In this paper, ablation experiments and comparison experiments were designed to evaluate and compare Swin-cryoEM algorithms in detail and comprehensively on multiple datasets. The Average Precision is an important evaluation index of the evaluation model, and the optimal Average Precision reached 95.5% in the training stage Swin-cryoEM, and the single particle picking performance was also superior in the prediction stage. This model inherits the advantages of the Swin Transformer detection model and is superior to mainstream models such as Faster R-CNN and YOLOv5 in terms of the single particle detection capability of cryo-electron micrographs.
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Affiliation(s)
- Kun Fang
- Hunan Meteorological Information Center, Hunan Meteorological Bureau, Changsha, Hunan, China
- Hunan Key Laboratory of Meteorological Disaster Prevention and Reduction, Hunan Meteorological Bureau, Changsha, Hunan, China
| | - JinLing Wang
- Xiangtan University& China Unicom (Hunan) Industrial Internet Co., Ltd, China Unicom (Hunan), Changsha, Hunan, China
| | - QingFeng Chen
- Hunan Meteorological Information Center, Hunan Meteorological Bureau, Changsha, Hunan, China
- Hunan Key Laboratory of Meteorological Disaster Prevention and Reduction, Hunan Meteorological Bureau, Changsha, Hunan, China
| | - Xian Feng
- Hunan Meteorological Information Center, Hunan Meteorological Bureau, Changsha, Hunan, China
- Hunan Key Laboratory of Meteorological Disaster Prevention and Reduction, Hunan Meteorological Bureau, Changsha, Hunan, China
| | - YouMing Qu
- Hunan Meteorological Information Center, Hunan Meteorological Bureau, Changsha, Hunan, China
- Hunan Key Laboratory of Meteorological Disaster Prevention and Reduction, Hunan Meteorological Bureau, Changsha, Hunan, China
| | - Jiachi Shi
- Hunan Meteorological Information Center, Hunan Meteorological Bureau, Changsha, Hunan, China
- Hunan Key Laboratory of Meteorological Disaster Prevention and Reduction, Hunan Meteorological Bureau, Changsha, Hunan, China
| | - Zhuomin Xu
- School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan, Hubei, China
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Zhou B, Ran B, Chen L. A GraphSAGE-based model with fingerprints only to predict drug-drug interactions. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2024; 21:2922-2942. [PMID: 38454713 DOI: 10.3934/mbe.2024130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/09/2024]
Abstract
Drugs are an effective way to treat various diseases. Some diseases are so complicated that the effect of a single drug for such diseases is limited, which has led to the emergence of combination drug therapy. The use multiple drugs to treat these diseases can improve the drug efficacy, but it can also bring adverse effects. Thus, it is essential to determine drug-drug interactions (DDIs). Recently, deep learning algorithms have become popular to design DDI prediction models. However, most deep learning-based models need several types of drug properties, inducing the application problems for drugs without these properties. In this study, a new deep learning-based model was designed to predict DDIs. For wide applications, drugs were first represented by commonly used properties, referred to as fingerprint features. Then, these features were perfectly fused with the drug interaction network by a type of graph convolutional network method, GraphSAGE, yielding high-level drug features. The inner product was adopted to score the strength of drug pairs. The model was evaluated by 10-fold cross-validation, resulting in an AUROC of 0.9704 and AUPR of 0.9727. Such performance was better than the previous model which directly used drug fingerprint features and was competitive compared with some other previous models that used more drug properties. Furthermore, the ablation tests indicated the importance of the main parts of the model, and we analyzed the strengths and limitations of a model for drugs with different degrees in the network. This model identified some novel DDIs that may bring expected benefits, such as the combination of PEA and cannabinol that may produce better effects. DDIs that may cause unexpected side effects have also been discovered, such as the combined use of WIN 55,212-2 and cannabinol. These DDIs can provide novel insights for treating complex diseases or avoiding adverse drug events.
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Affiliation(s)
- Bo Zhou
- Institute of Wound Prevention and Treatment, Shanghai University of Medicine and Health Sciences, Shanghai 201318, China
- School of Basic Medical Sciences, Shanghai University of Medicine and Health Sciences, Shanghai 201318, China
| | - Bing Ran
- College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
| | - Lei Chen
- College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
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Rahman AU, Alsenani Y, Zafar A, Ullah K, Rabie K, Shongwe T. Enhancing heart disease prediction using a self-attention-based transformer model. Sci Rep 2024; 14:514. [PMID: 38177293 PMCID: PMC10767116 DOI: 10.1038/s41598-024-51184-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Accepted: 01/01/2024] [Indexed: 01/06/2024] Open
Abstract
Cardiovascular diseases (CVDs) continue to be the leading cause of more than 17 million mortalities worldwide. The early detection of heart failure with high accuracy is crucial for clinical trials and therapy. Patients will be categorized into various types of heart disease based on characteristics like blood pressure, cholesterol levels, heart rate, and other characteristics. With the use of an automatic system, we can provide early diagnoses for those who are prone to heart failure by analyzing their characteristics. In this work, we deploy a novel self-attention-based transformer model, that combines self-attention mechanisms and transformer networks to predict CVD risk. The self-attention layers capture contextual information and generate representations that effectively model complex patterns in the data. Self-attention mechanisms provide interpretability by giving each component of the input sequence a certain amount of attention weight. This includes adjusting the input and output layers, incorporating more layers, and modifying the attention processes to collect relevant information. This also makes it possible for physicians to comprehend which features of the data contributed to the model's predictions. The proposed model is tested on the Cleveland dataset, a benchmark dataset of the University of California Irvine (UCI) machine learning (ML) repository. Comparing the proposed model to several baseline approaches, we achieved the highest accuracy of 96.51%. Furthermore, the outcomes of our experiments demonstrate that the prediction rate of our model is higher than that of other cutting-edge approaches used for heart disease prediction.
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Affiliation(s)
- Atta Ur Rahman
- Riphah Institute of System Engineering, Riphah International University Islamabad, Islamabad, 46000, Pakistan.
- Research and Development Department, Lun Startup Studio, 11543, Riyadh, Saudi Arabia.
| | - Yousef Alsenani
- Department of Information Systems, FCIT, King Abdulaziz University, 21443, Jeddah, Saudi Arabia
- Research and Development Department, Lun Startup Studio, 11543, Riyadh, Saudi Arabia
| | - Adeel Zafar
- Riphah Institute of System Engineering, Riphah International University Islamabad, Islamabad, 46000, Pakistan
| | - Kalim Ullah
- Department of Zoology, Kohat University of Science and Technology, Kohat, 26000, Pakistan
| | - Khaled Rabie
- Department of Engineering, Manchester Metropolitan University, Manchester, M15 6BH, UK
- Department of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg, 2006, South Africa
| | - Thokozani Shongwe
- Department of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg, 2006, South Africa
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Li S, Liu Z, Yan Y, Wang R, Dong E, Cheng Z. Research on Diesel Engine Fault Status Identification Method Based on Synchro Squeezing S-Transform and Vision Transformer. SENSORS (BASEL, SWITZERLAND) 2023; 23:6447. [PMID: 37514741 PMCID: PMC10385223 DOI: 10.3390/s23146447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 07/12/2023] [Accepted: 07/13/2023] [Indexed: 07/30/2023]
Abstract
The reliability and safety of diesel engines gradually decrease with the increase in running time, leading to frequent failures. To address the problem that it is difficult for the traditional fault status identification methods to identify diesel engine faults accurately, a diesel engine fault status identification method based on synchro squeezing S-transform (SSST) and vision transformer (ViT) is proposed. This method can effectively combine the advantages of the SSST method in processing non-linear and non-smooth signals with the powerful image classification capability of ViT. The vibration signals reflecting the diesel engine status are collected by sensors. To solve the problems of low time-frequency resolution and weak energy aggregation in traditional signal time-frequency analysis methods, the SSST method is used to convert the vibration signals into two-dimensional time-frequency maps; the ViT model is used to extract time-frequency image features for training to achieve diesel engine status assessment. Pre-set fault experiments are carried out using the diesel engine condition monitoring experimental bench, and the proposed method is compared with three traditional methods, namely, ST-ViT, SSST-2DCNN and FFT spectrum-1DCNN. The experimental results show that the overall fault status identification accuracy in the public dataset and the actual laboratory data reaches 98.31% and 95.67%, respectively, providing a new idea for diesel engine fault status identification.
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Affiliation(s)
- Siyu Li
- Shijiazhuang Campus of Army Engineering University of PLA, Shijiazhuang 050003, China
| | - Zichang Liu
- Shijiazhuang Campus of Army Engineering University of PLA, Shijiazhuang 050003, China
| | - Yunbin Yan
- Shijiazhuang Campus of Army Engineering University of PLA, Shijiazhuang 050003, China
| | - Rongcai Wang
- Shijiazhuang Campus of Army Engineering University of PLA, Shijiazhuang 050003, China
| | - Enzhi Dong
- Shijiazhuang Campus of Army Engineering University of PLA, Shijiazhuang 050003, China
| | - Zhonghua Cheng
- Shijiazhuang Campus of Army Engineering University of PLA, Shijiazhuang 050003, China
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Kaba Ş, Haci H, Isin A, Ilhan A, Conkbayir C. The Application of Deep Learning for the Segmentation and Classification of Coronary Arteries. Diagnostics (Basel) 2023; 13:2274. [PMID: 37443668 DOI: 10.3390/diagnostics13132274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 06/30/2023] [Accepted: 07/03/2023] [Indexed: 07/15/2023] Open
Abstract
In recent years, the prevalence of coronary artery disease (CAD) has become one of the leading causes of death around the world. Accurate stenosis detection of coronary arteries is crucial for timely treatment. Cardiologists use visual estimations when reading coronary angiography images to diagnose stenosis. As a result, they face various challenges which include high workloads, long processing times and human error. Computer-aided segmentation and classification of coronary arteries, as to whether stenosis is present or not, significantly reduces the workload of cardiologists and human errors caused by manual processes. Moreover, deep learning techniques have been shown to aid medical experts in diagnosing diseases using biomedical imaging. Thus, this study proposes the use of automatic segmentation of coronary arteries using U-Net, ResUNet-a, UNet++, models and classification using DenseNet201, EfficientNet-B0, Mobilenet-v2, ResNet101 and Xception models. In the case of segmentation, the comparative analysis of the three models has shown that U-Net achieved the highest score with a 0.8467 Dice score and 0.7454 Jaccard Index in comparison with UNet++ and ResUnet-a. Evaluation of the classification model's performances has shown that DenseNet201 performed better than other pretrained models with 0.9000 accuracy, 0.9833 specificity, 0.9556 PPV, 0.7746 Cohen's Kappa and 0.9694 Area Under the Curve (AUC).
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Affiliation(s)
- Şerife Kaba
- Department of Biomedical Engineering, Near East University, TRNC Mersin 10, Nicosia 99138, Turkey
| | - Huseyin Haci
- Department of Electrical-Electronic Engineering, Near East University, TRNC Mersin 10, Nicosia 99138, Turkey
| | - Ali Isin
- Department of Biomedical Engineering, Cyprus International University, TRNC Mersin 10, Nicosia 99138, Turkey
| | - Ahmet Ilhan
- Department of Computer Engineering, Near East University, TRNC Mersin 10, Nicosia 99138, Turkey
| | - Cenk Conkbayir
- Department of Cardiology, Near East University, TRNC Mersin 10, Nicosia 99138, Turkey
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