Pub Date : 2022-01-27DOI: 10.1109/RBME.2022.3146293
Jose Caceres-Alban;Midori Sanchez;Fanny L. Casado
Novel additive manufacturing techniques are revolutionizing fields of industry providing more dimensions to control and the versatility of fabricating multi-material products. Medical applications hold great promise to manufacture constructs of mixed biologically compatible materials together with functional cells and tissues. We reviewed technologies and promising developments nurturing innovation of physiologically relevant models to study safety of chemicals that are hard to reproduce in current models, or diseases for which there are no models available. Extrusion-, inkjet- and laser-assisted bioprinting are the most used techniques. Hydrogels as constituents of bioinks and biomaterial inks are the most versatile materials to recreate physiological and pathophysiological microenvironments. The highlighted bioprinted models were chosen because they guarantee post-printing cellular viability while maintaining desirable mechanical properties of their constitutive bioinks or biomaterial inks to ensure their printability. Bioprinting is being readily adopted to overcome ethical concerns of in vivo models and improve the automation, reproducibility, geometry stability of traditional in vitro models. The challenges for advancing the technological level readiness of bioprinting require overcoming heterogeneity, microstructural complexity, dynamism and integration with other models, to generate multi-organ platforms that can inform about biological responses to chemical exposure, disease development and efficacy of novel therapies.
{"title":"Bioprinting: A Strategy to Build Informative Models of Exposure and Disease","authors":"Jose Caceres-Alban;Midori Sanchez;Fanny L. Casado","doi":"10.1109/RBME.2022.3146293","DOIUrl":"10.1109/RBME.2022.3146293","url":null,"abstract":"Novel additive manufacturing techniques are revolutionizing fields of industry providing more dimensions to control and the versatility of fabricating multi-material products. Medical applications hold great promise to manufacture constructs of mixed biologically compatible materials together with functional cells and tissues. We reviewed technologies and promising developments nurturing innovation of physiologically relevant models to study safety of chemicals that are hard to reproduce in current models, or diseases for which there are no models available. Extrusion-, inkjet- and laser-assisted bioprinting are the most used techniques. Hydrogels as constituents of bioinks and biomaterial inks are the most versatile materials to recreate physiological and pathophysiological microenvironments. The highlighted bioprinted models were chosen because they guarantee post-printing cellular viability while maintaining desirable mechanical properties of their constitutive bioinks or biomaterial inks to ensure their printability. Bioprinting is being readily adopted to overcome ethical concerns of in vivo models and improve the automation, reproducibility, geometry stability of traditional \u0000<italic>in vitro</i>\u0000 models. The challenges for advancing the technological level readiness of bioprinting require overcoming heterogeneity, microstructural complexity, dynamism and integration with other models, to generate multi-organ platforms that can inform about biological responses to chemical exposure, disease development and efficacy of novel therapies.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"16 ","pages":"594-610"},"PeriodicalIF":17.6,"publicationDate":"2022-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/4664312/10007429/09695320.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9371124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-20DOI: 10.1109/RBME.2021.3130485
Yuan-Ting Zhang
Presents the editorial for this issue of the publication.
介绍本期出版物的社论。
{"title":"Editorial: A Message From the Outgoing Editor-in-Chief","authors":"Yuan-Ting Zhang","doi":"10.1109/RBME.2021.3130485","DOIUrl":"https://doi.org/10.1109/RBME.2021.3130485","url":null,"abstract":"Presents the editorial for this issue of the publication.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"15 ","pages":"3-3"},"PeriodicalIF":17.6,"publicationDate":"2022-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/4664312/9686800/09686995.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67748171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-20DOI: 10.1109/RBME.2021.3130508
Provides a listing of current staff, committee members and society officers.
提供现有工作人员、委员会成员和社会官员的名单。
{"title":"IEEE Engineering in Medicine and Biology Society","authors":"","doi":"10.1109/RBME.2021.3130508","DOIUrl":"https://doi.org/10.1109/RBME.2021.3130508","url":null,"abstract":"Provides a listing of current staff, committee members and society officers.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"15 ","pages":"C2-C2"},"PeriodicalIF":17.6,"publicationDate":"2022-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/4664312/9686800/09686801.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67748173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-20DOI: 10.1109/RBME.2021.3130482
Presents the front cover for this issue of the publication.
呈现本期出版物的封面。
{"title":"Frontcover","authors":"","doi":"10.1109/RBME.2021.3130482","DOIUrl":"https://doi.org/10.1109/RBME.2021.3130482","url":null,"abstract":"Presents the front cover for this issue of the publication.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"15 ","pages":"C1-C1"},"PeriodicalIF":17.6,"publicationDate":"2022-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/4664312/9686800/09686974.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67748172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-20DOI: 10.1109/RBME.2021.3130484
Provides a listing of current committee members and society officers.
提供现任委员会成员和协会官员的名单。
{"title":"IEEE Reviews in Biomedical Engineering (R-BME)","authors":"","doi":"10.1109/RBME.2021.3130484","DOIUrl":"https://doi.org/10.1109/RBME.2021.3130484","url":null,"abstract":"Provides a listing of current committee members and society officers.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"15 ","pages":"C3-C3"},"PeriodicalIF":17.6,"publicationDate":"2022-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/4664312/9686800/09686976.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67748170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-11DOI: 10.1109/RBME.2022.3142058
Mason Kadem;Louis Garber;Mohamed Abdelkhalek;Baraa K. Al-Khazraji;Zahra Keshavarz-Motamed
Cardiovascular disease is a deadly global health crisis that carries a substantial financial burden. Innovative treatment and management of cardiovascular disease straddles medicine, personalized hemodynamic modeling, machine learning, and modern imaging to help improve patient outcomes and reduce the economic impact. Hemodynamic modeling offers a non-invasive method to provide clinicians with new pre- and post- procedural metrics and aid in the selection of treatment options. Medical imaging is an integral part in clinical workflows for understanding and managing cardiac disease and interventions. Coupling machine learning with modeling, and cardiovascular imaging, provides faster modeling, improved data fidelity, and an enhanced understanding and earlier detection of cardiovascular anomalies, leading to the development of patient-specific diagnostic and predictive tools for characterizing and assessing cardiovascular outcomes. Herein, we provide a scoping review of translational hemodynamic modeling, medical imaging, and machine learning and their applications to cardiovascular interventions. We particularly focus on providing an intuitive understanding of each of these approaches and their ability to support decision making during important clinical milestones.
{"title":"Hemodynamic Modeling, Medical Imaging, and Machine Learning and Their Applications to Cardiovascular Interventions","authors":"Mason Kadem;Louis Garber;Mohamed Abdelkhalek;Baraa K. Al-Khazraji;Zahra Keshavarz-Motamed","doi":"10.1109/RBME.2022.3142058","DOIUrl":"10.1109/RBME.2022.3142058","url":null,"abstract":"Cardiovascular disease is a deadly global health crisis that carries a substantial financial burden. Innovative treatment and management of cardiovascular disease straddles medicine, personalized hemodynamic modeling, machine learning, and modern imaging to help improve patient outcomes and reduce the economic impact. Hemodynamic modeling offers a non-invasive method to provide clinicians with new pre- and post- procedural metrics and aid in the selection of treatment options. Medical imaging is an integral part in clinical workflows for understanding and managing cardiac disease and interventions. Coupling machine learning with modeling, and cardiovascular imaging, provides faster modeling, improved data fidelity, and an enhanced understanding and earlier detection of cardiovascular anomalies, leading to the development of patient-specific diagnostic and predictive tools for characterizing and assessing cardiovascular outcomes. Herein, we provide a scoping review of translational hemodynamic modeling, medical imaging, and machine learning and their applications to cardiovascular interventions. We particularly focus on providing an intuitive understanding of each of these approaches and their ability to support decision making during important clinical milestones.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"16 ","pages":"403-423"},"PeriodicalIF":17.6,"publicationDate":"2022-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9364000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hypertension, or elevated blood pressure (BP), is a marker for many cardiovascular diseases and can lead to life threatening conditions such as heart failure, coronary artery disease and stroke. Several techniques have recently been proposed and investigated for non-invasive BP monitoring. The increasing desire for telemonitoring solutions that allow patients to manage their own conditions from home has accelerated the development of new BP monitoring techniques. In this review, we present the recent progress in non-invasive blood pressure monitoring solutions emphasizing clinical validation and trade-offs between available techniques. We introduce the current BP measurement techniques with their underlying operating principles. New promising proof-of-concept studies are presented and recent modeling and machine learning approaches for improved BP estimation are summarized. This aids discussions on how new BP monitors should evaluated in order to bring forth new home monitoring solutions in wearable form factor. Finally, we discuss on unresolved challenges in making convenient, reliable and validated BP monitoring solutions.
{"title":"Advances in Non-Invasive Blood Pressure Measurement Techniques","authors":"Tuukka Panula;Jukka-Pekka Sirkiä;David Wong;Matti Kaisti","doi":"10.1109/RBME.2022.3141877","DOIUrl":"10.1109/RBME.2022.3141877","url":null,"abstract":"Hypertension, or elevated blood pressure (BP), is a marker for many cardiovascular diseases and can lead to life threatening conditions such as heart failure, coronary artery disease and stroke. Several techniques have recently been proposed and investigated for non-invasive BP monitoring. The increasing desire for telemonitoring solutions that allow patients to manage their own conditions from home has accelerated the development of new BP monitoring techniques. In this review, we present the recent progress in non-invasive blood pressure monitoring solutions emphasizing clinical validation and trade-offs between available techniques. We introduce the current BP measurement techniques with their underlying operating principles. New promising proof-of-concept studies are presented and recent modeling and machine learning approaches for improved BP estimation are summarized. This aids discussions on how new BP monitors should evaluated in order to bring forth new home monitoring solutions in wearable form factor. Finally, we discuss on unresolved challenges in making convenient, reliable and validated BP monitoring solutions.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"16 ","pages":"424-438"},"PeriodicalIF":17.6,"publicationDate":"2022-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/4664312/10007429/09677909.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9363999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-07DOI: 10.1109/RBME.2022.3141369
Lekshmy Sudha Kumari;Abbas Z. Kouzani
With its potential of single cell specificity, optogenetics has made the investigation into the brain circuits more controllable. Closed loop optogenetic brain stimulation enhances the efficacy of the stimulation by adjusting the stimulation parameters based on direct feedback from the target area of the brain. It combines the principles of genetics, physiology, electrical engineering, optics, signal processing and control theory to create an efficient brain stimulation system. To read the underlying neuronal condition from the electrical activity of neurons, a sensor, sensor interface circuit, and signal conditioning are needed. Also, efficient feature extraction, classification, and control algorithms should be in place to interpret and use the sensed data for closing the feedback loop. Finally, a stimulation circuitry is required to effectively control a light source to deliver light based stimulation according to the feedback signal. Thus, the backbone to a functioning closed loop optogenetic brain stimulation device is a well-built electronic circuitry for sensing and processing of brain signals, running efficient signal processing and control algorithm, and delivering timed light stimulations. This paper presents a review of electronic and software concepts and components used in recent closed-loop optogenetic devices based on neuro-electrophysiological reading and an outlook on the future design possibilities with the aim of providing a compact and easy reference for developing closed loop optogenetic brain stimulation devices.
{"title":"Electrophysiology-Based Closed Loop Optogenetic Brain Stimulation Devices: Recent Developments and Future Prospects","authors":"Lekshmy Sudha Kumari;Abbas Z. Kouzani","doi":"10.1109/RBME.2022.3141369","DOIUrl":"10.1109/RBME.2022.3141369","url":null,"abstract":"With its potential of single cell specificity, optogenetics has made the investigation into the brain circuits more controllable. Closed loop optogenetic brain stimulation enhances the efficacy of the stimulation by adjusting the stimulation parameters based on direct feedback from the target area of the brain. It combines the principles of genetics, physiology, electrical engineering, optics, signal processing and control theory to create an efficient brain stimulation system. To read the underlying neuronal condition from the electrical activity of neurons, a sensor, sensor interface circuit, and signal conditioning are needed. Also, efficient feature extraction, classification, and control algorithms should be in place to interpret and use the sensed data for closing the feedback loop. Finally, a stimulation circuitry is required to effectively control a light source to deliver light based stimulation according to the feedback signal. Thus, the backbone to a functioning closed loop optogenetic brain stimulation device is a well-built electronic circuitry for sensing and processing of brain signals, running efficient signal processing and control algorithm, and delivering timed light stimulations. This paper presents a review of electronic and software concepts and components used in recent closed-loop optogenetic devices based on neuro-electrophysiological reading and an outlook on the future design possibilities with the aim of providing a compact and easy reference for developing closed loop optogenetic brain stimulation devices.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"16 ","pages":"91-108"},"PeriodicalIF":17.6,"publicationDate":"2022-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9363998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-17DOI: 10.1109/RBME.2021.3136343
Simon Bohlender;Ilkay Oksuz;Anirban Mukhopadhyay
Since the advent of U-Net, fully convolutional deep neural networks and its many variants have completely changed the modern landscape of deep-learning based medical image segmentation. However, the over-dependence of these methods on pixel-level classification and regression has been identified early on as a problem. Especially when trained on medical databases with sparse available annotation, these methods are prone to generate segmentation artifacts such as fragmented structures, topological inconsistencies and islands of pixel. These artifacts are especially problematic in medical imaging since segmentation is almost always a pre-processing step for some downstream evaluations like surgical planning, visualization, prognosis, or treatment planning. However, one common thread across all these downstream tasks is the demand of anatomical consistency. To ensure the segmentation result is anatomically consistent, approaches based on Markov/ Conditional Random Fields, Statistical Shape Models, Active Contours are becoming increasingly popular over the past 5 years. In this review paper, a broad overview of recent literature on bringing explicit anatomical constraints for medical image segmentation is given, the shortcomings and opportunities are discussed and the potential shift towards implicit shape modelling is elaborated. We review the most relevant papers published until the submission date and provide a tabulated view with method details for quick access.
{"title":"A Survey on Shape-Constraint Deep Learning for Medical Image Segmentation","authors":"Simon Bohlender;Ilkay Oksuz;Anirban Mukhopadhyay","doi":"10.1109/RBME.2021.3136343","DOIUrl":"10.1109/RBME.2021.3136343","url":null,"abstract":"Since the advent of U-Net, fully convolutional deep neural networks and its many variants have completely changed the modern landscape of deep-learning based medical image segmentation. However, the over-dependence of these methods on pixel-level classification and regression has been identified early on as a problem. Especially when trained on medical databases with sparse available annotation, these methods are prone to generate segmentation artifacts such as fragmented structures, topological inconsistencies and islands of pixel. These artifacts are especially problematic in medical imaging since segmentation is almost always a pre-processing step for some downstream evaluations like surgical planning, visualization, prognosis, or treatment planning. However, one common thread across all these downstream tasks is the demand of anatomical consistency. To ensure the segmentation result is anatomically consistent, approaches based on Markov/ Conditional Random Fields, Statistical Shape Models, Active Contours are becoming increasingly popular over the past 5 years. In this review paper, a broad overview of recent literature on bringing explicit anatomical constraints for medical image segmentation is given, the shortcomings and opportunities are discussed and the potential shift towards implicit shape modelling is elaborated. We review the most relevant papers published until the submission date and provide a tabulated view with method details for quick access.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"16 ","pages":"225-240"},"PeriodicalIF":17.6,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9416394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-14DOI: 10.1109/RBME.2021.3134994
Dang-Khoa Tran;Thuy Nguyen Thi Phuong;Nhat-Le Bui;Vijai Singh;Qi Hao Looi;Benson Koh;Ungku Mohd Shahrin B Mohd Zaman;Jhi Biau Foo;Chia-Ching Wu;Pau Loke Show;Dinh-Toi Chu
Over the last decade, stem cell-associated therapies are widely used because of their potential in self-renewable and multipotent differentiation ability. Stem cells have become more attractive for aesthetic uses and plastic surgery, including scar reduction, breast augmentation, facial contouring, hand rejuvenation, and anti-aging. The current preclinical and clinical studies of stem cells on aesthetic uses also showed promising outcomes. Adipose-derived stem cells are commonly used for fat grafting that demonstrated scar improvement, anti-aging, skin rejuvenation properties, etc. While stem cell-based products have yet to receive approval from the FDA for aesthetic medicine and plastic surgery. Moving forward, the review on the efficacy and potential of stem cell-based therapy for aesthetic and plastic surgery is limited. In the present review, we discuss the current status and recent advances of using stem cells for aesthetic and plastic surgery. The potential of cell-free therapy and tissue engineering in this field is also highlighted. The clinical applications, advantages, and limitations are also discussed. This review also provides further works that need to be investigated to widely apply stem cells in the clinic, especially in aesthetic and plastic contexts.
{"title":"Exploring the Potential of Stem Cell-Based Therapy for Aesthetic and Plastic Surgery","authors":"Dang-Khoa Tran;Thuy Nguyen Thi Phuong;Nhat-Le Bui;Vijai Singh;Qi Hao Looi;Benson Koh;Ungku Mohd Shahrin B Mohd Zaman;Jhi Biau Foo;Chia-Ching Wu;Pau Loke Show;Dinh-Toi Chu","doi":"10.1109/RBME.2021.3134994","DOIUrl":"10.1109/RBME.2021.3134994","url":null,"abstract":"Over the last decade, stem cell-associated therapies are widely used because of their potential in self-renewable and multipotent differentiation ability. Stem cells have become more attractive for aesthetic uses and plastic surgery, including scar reduction, breast augmentation, facial contouring, hand rejuvenation, and anti-aging. The current preclinical and clinical studies of stem cells on aesthetic uses also showed promising outcomes. Adipose-derived stem cells are commonly used for fat grafting that demonstrated scar improvement, anti-aging, skin rejuvenation properties, etc. While stem cell-based products have yet to receive approval from the FDA for aesthetic medicine and plastic surgery. Moving forward, the review on the efficacy and potential of stem cell-based therapy for aesthetic and plastic surgery is limited. In the present review, we discuss the current status and recent advances of using stem cells for aesthetic and plastic surgery. The potential of cell-free therapy and tissue engineering in this field is also highlighted. The clinical applications, advantages, and limitations are also discussed. This review also provides further works that need to be investigated to widely apply stem cells in the clinic, especially in aesthetic and plastic contexts.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"16 ","pages":"386-402"},"PeriodicalIF":17.6,"publicationDate":"2021-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9713742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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