IEEE Reviews in Biomedical Engineering最新文献

IEEE Reviews in Biomedical Engineering (R-BME)

IF 17.2 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2025-01-28 DOI: 10.1109/RBME.2024.3518719

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Editorial: Harnessing Reviews to Advance Biomedical Engineering's New Horizons

IF 17.2 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2025-01-28 DOI: 10.1109/RBME.2024.3518852

Bin He

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IEEE Engineering in Medicine and Biology Society

IF 17.2 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2025-01-28 DOI: 10.1109/RBME.2024.3518715

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Advancing Cardiac Organoid Engineering Through Application of Biophysical Forces

IF 17.2 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2024-12-09 DOI: 10.1109/RBME.2024.3514378

Adriana Blazeski;Guillermo García-Cardeña;Roger D. Kamm

{"title":"Advancing Cardiac Organoid Engineering Through Application of Biophysical Forces","authors":"Adriana Blazeski;Guillermo García-Cardeña;Roger D. Kamm","doi":"10.1109/RBME.2024.3514378","DOIUrl":"https://doi.org/10.1109/RBME.2024.3514378","url":null,"abstract":"Cardiac organoids represent an important bioengineering opportunity in the development of models to study human heart pathophysiology. By incorporating multiple cardiac cell types in three-dimensional culture and developmentally-guided biochemical signaling, cardiac organoids recapitulate numerous features of heart tissue. However, cardiac tissue also experiences a variety of mechanical forces as the heart develops and over the course of each contraction cycle. It is now clear that these forces impact cellular specification, phenotype, and function, and should be incorporated into the engineering of cardiac organoids in order to generate better models. In this review, we discuss strategies for engineering cardiac organoids and report the effects of organoid design on the function of cardiac cells. We then discuss the mechanical environment of the heart, including forces arising from tissue elasticity, contraction, blood flow, and stretch, and report on efforts to mimic these biophysical cues in cardiac organoids. Finally, we review emerging areas of cardiac organoid research, for the study of cardiac development, the formation of multi-organ models, and the simulation of the effects of spaceflight on cardiac tissue, and consider how these investigations might benefit from the inclusion of mechanical cues.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"18 ","pages":"211-230"},"PeriodicalIF":17.2,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105751","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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Earable Multimodal Sensing and Stimulation: A Prospective Toward Unobtrusive Closed-Loop Biofeedback

IF 17.2 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2024-11-29 DOI: 10.1109/RBME.2024.3508713

Yuchen Xu;Abhinav Uppal;Min Suk Lee;Kuldeep Mahato;Brian L. Wuerstle;Muyang Lin;Omeed Djassemi;Tao Chen;Rui Lin;Akshay Paul;Soumil Jain;Florian Chapotot;Esra Tasali;Patrick Mercier;Sheng Xu;Joseph Wang;Gert Cauwenberghs

{"title":"Earable Multimodal Sensing and Stimulation: A Prospective Toward Unobtrusive Closed-Loop Biofeedback","authors":"Yuchen Xu;Abhinav Uppal;Min Suk Lee;Kuldeep Mahato;Brian L. Wuerstle;Muyang Lin;Omeed Djassemi;Tao Chen;Rui Lin;Akshay Paul;Soumil Jain;Florian Chapotot;Esra Tasali;Patrick Mercier;Sheng Xu;Joseph Wang;Gert Cauwenberghs","doi":"10.1109/RBME.2024.3508713","DOIUrl":"https://doi.org/10.1109/RBME.2024.3508713","url":null,"abstract":"The human ear has emerged as a bidirectional gateway to the brain's and body's signals. Recent advances in around-the-ear and in-ear sensors have enabled the assessment of biomarkers and physiomarkers derived from brain and cardiac activity using ear-electroencephalography (ear-EEG), photoplethysmography (ear-PPG), and chemical sensing of analytes from the ear, with ear-EEG having been taken beyond-the-lab to outer space. Parallel advances in non-invasive and minimally invasive brain stimulation techniques have leveraged the ear's access to two cranial nerves to modulate brain and body activity. The vestibulocochlear nerve stimulates the auditory cortex and limbic system with sound, while the auricular branch of the vagus nerve indirectly but significantly couples to the autonomic nervous system and cardiac output. Acoustic and current mode stimuli delivered using discreet and unobtrusive earables are an active area of research, aiming to make biofeedback and bioelectronic medicine deliverable outside of the clinic, with remote and continuous monitoring of therapeutic responsivity and long-term adaptation. Leveraging recent advances in ear-EEG, transcutaneous auricular vagus nerve stimulation (taVNS), and unobtrusive acoustic stimulation, we review accumulating evidence that combines their potential into an integrated earable platform for closed-loop multimodal sensing and neuromodulation, towards personalized and holistic therapies that are near, in- and around-the-ear.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"18 ","pages":"5-25"},"PeriodicalIF":17.2,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105660","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}

引用次数: 0

Immunomechanobiology: Engineering the Activation and Function of Immune Cells With the Mechanical Signal of Fluid Shear Stress

IF 17.2 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2024-11-22 DOI: 10.1109/RBME.2024.3505073

N. S. Sarna;N. M. Curry;E. Aalaei;B. G. Kaufman;M. R. King

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Utilizing Neurons to Interrogate Cancer: Integrative Analysis of Cancer Omics Data With Deep Learning Models

IF 17.2 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2024-11-21 DOI: 10.1109/RBME.2024.3503761

Raid Halawani;Michael Buchert;Yi-Ping Phoebe Chen

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Foundation Model for Advancing Healthcare: Challenges, Opportunities and Future Directions 推进医疗保健的基金会模式：挑战、机遇和未来方向。

IF 17.2 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2024-11-12 DOI: 10.1109/RBME.2024.3496744

Yuting He;Fuxiang Huang;Xinrui Jiang;Yuxiang Nie;Minghao Wang;Jiguang Wang;Hao Chen

{"title":"Foundation Model for Advancing Healthcare: Challenges, Opportunities and Future Directions","authors":"Yuting He;Fuxiang Huang;Xinrui Jiang;Yuxiang Nie;Minghao Wang;Jiguang Wang;Hao Chen","doi":"10.1109/RBME.2024.3496744","DOIUrl":"10.1109/RBME.2024.3496744","url":null,"abstract":"Foundation model, trained on a diverse range of data and adaptable to a myriad of tasks, is advancing healthcare. It fosters the development of healthcare artificial intelligence (AI) models tailored to the intricacies of the medical field, bridging the gap between limited AI models and the varied nature of healthcare practices. The advancement of a healthcare foundation model (HFM) brings forth tremendous potential to augment intelligent healthcare services across a broad spectrum of scenarios. However, despite the imminent widespread deployment of HFMs, there is currently a lack of clear understanding regarding their operation in the healthcare field, their existing challenges, and their future trajectory. To answer these critical inquiries, we present a comprehensive and in-depth examination that delves into the landscape of HFMs. It begins with a comprehensive overview of HFMs, encompassing their methods, data, and applications, to provide a quick understanding of the current progress. Subsequently, it delves into a thorough exploration of the challenges associated with data, algorithms, and computing infrastructures in constructing and widely applying foundation models in healthcare. Furthermore, this survey identifies promising directions for future development in this field. We believe that this survey will enhance the community's understanding of the current progress of HFMs and serve as a valuable source of guidance for future advancements in this domain.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"18 ","pages":"172-191"},"PeriodicalIF":17.2,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142630123","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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A Manual for Genome and Transcriptome Engineering 基因组和转录组工程手册》。

IF 17.2 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2024-11-08 DOI: 10.1109/RBME.2024.3494715

Yesh Doctor;Milan Sanghvi;Prashant Mali

{"title":"A Manual for Genome and Transcriptome Engineering","authors":"Yesh Doctor;Milan Sanghvi;Prashant Mali","doi":"10.1109/RBME.2024.3494715","DOIUrl":"10.1109/RBME.2024.3494715","url":null,"abstract":"Genome and transcriptome engineering have emerged as powerful tools in modern biotechnology, driving advancements in precision medicine and novel therapeutics. In this review, we provide a comprehensive overview of the current methodologies, applications, and future directions in genome and transcriptome engineering. Through this, we aim to provide a guide for tool selection, critically analyzing the strengths, weaknesses, and best use cases of these tools to provide context on their suitability for various applications. We explore standard and recent developments in genome engineering, such as base editors and prime editing, and provide insight into tool selection for change of function (knockout, deletion, insertion, substitution) and change of expression (repression, activation) contexts. Advancements in transcriptome engineering are also explored, focusing on established technologies like antisense oligonucleotides (ASOs) and RNA interference (RNAi), as well as recent developments such as CRISPR-Cas13 and adenosine deaminases acting on RNA (ADAR). This review offers a comparison of different approaches to achieve similar biological goals, and consideration of high-throughput applications that enable the probing of a variety of targets. This review elucidates the transformative impact of genome and transcriptome engineering on biological research and clinical applications that will pave the way for future innovations in the field.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"18 ","pages":"250-267"},"PeriodicalIF":17.2,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142606573","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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Artificial General Intelligence for Medical Imaging Analysis 用于医学影像分析的人工通用智能。

IF 17.2 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2024-11-07 DOI: 10.1109/RBME.2024.3493775

Xiang Li;Lin Zhao;Lu Zhang;Zihao Wu;Zhengliang Liu;Hanqi Jiang;Chao Cao;Shaochen Xu;Yiwei Li;Haixing Dai;Yixuan Yuan;Jun Liu;Gang Li;Dajiang Zhu;Pingkun Yan;Quanzheng Li;Wei Liu;Tianming Liu;Dinggang Shen

{"title":"Artificial General Intelligence for Medical Imaging Analysis","authors":"Xiang Li;Lin Zhao;Lu Zhang;Zihao Wu;Zhengliang Liu;Hanqi Jiang;Chao Cao;Shaochen Xu;Yiwei Li;Haixing Dai;Yixuan Yuan;Jun Liu;Gang Li;Dajiang Zhu;Pingkun Yan;Quanzheng Li;Wei Liu;Tianming Liu;Dinggang Shen","doi":"10.1109/RBME.2024.3493775","DOIUrl":"10.1109/RBME.2024.3493775","url":null,"abstract":"Large-scale Artificial General Intelligence (AGI) models, including Large Language Models (LLMs) such as ChatGPT/GPT-4, have achieved unprecedented success in a variety of general domain tasks. Yet, when applied directly to specialized domains like medical imaging, which require in-depth expertise, these models face notable challenges arising from the medical field's inherent complexities and unique characteristics. In this review, we delve into the potential applications of AGI models in medical imaging and healthcare, with a primary focus on LLMs, Large Vision Models, and Large Multimodal Models. We provide a thorough overview of the key features and enabling techniques of LLMs and AGI, and further examine the roadmaps guiding the evolution and implementation of AGI models in the medical sector, summarizing their present applications, potentialities, and associated challenges. In addition, we highlight potential future research directions, offering a holistic view on upcoming ventures. This comprehensive review aims to offer insights into the future implications of AGI in medical imaging, healthcare, and beyond.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":"18 ","pages":"113-129"},"PeriodicalIF":17.2,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142606494","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}

引用次数: 0