Biomedical Engineering and Computational Biology最新文献_第4页

Tissue Phantoms for Biomedical Applications in Raman Spectroscopy: A Review. 组织幻影在拉曼光谱中的生物医学应用综述。

IF 2.8

Biomedical Engineering and Computational Biology Pub Date : 2020-08-19 eCollection Date: 2020-01-01 DOI: 10.1177/1179597220948100

Martha Z Vardaki, Nikolaos Kourkoumelis

引用次数: 19

ZnO Nanowire-Anchored Microfluidic Device With Herringbone Structure Fabricated by Maskless Photolithography. 无掩膜光刻技术制备人字结构ZnO纳米线锚定微流控器件。

IF 2.8

Biomedical Engineering and Computational Biology Pub Date : 2020-07-13 eCollection Date: 2020-01-01 DOI: 10.1177/1179597220941431

Dilshan Sooriyaarachchi, Shahrima Maharubin, George Z Tan

{"title":"ZnO Nanowire-Anchored Microfluidic Device With Herringbone Structure Fabricated by Maskless Photolithography.","authors":"Dilshan Sooriyaarachchi, Shahrima Maharubin, George Z Tan","doi":"10.1177/1179597220941431","DOIUrl":"https://doi.org/10.1177/1179597220941431","url":null,"abstract":"The integration of nanomaterials in microfluidic devices has emerged as a new research paradigm. Microfluidic devices composed of ZnO nanowires have been developed for the collection of urine extracellular vesicles (EVs) at high efficiency and in situ extraction of various microRNAs (miRNAs). The devices can be used for diagnosing various diseases, including kidney diseases and cancers. A major research need for developing micro total analysis systems is to enhance extraction efficiency. This article presents a novel fabrication method for a herringbone-patterned microfluidic device anchored with ZnO nanowire arrays. The substrates with herringbone patterns were created by maskless photolithography. The ZnO nanowire arrays were grown on the substrates by chemical bathing. The patterned design was to introduce turbulent flows as opposed to laminar flow in traditional devices to increase the mixing and contact of the urine sample with ZnO nanowires. The device showed reduced flow rates compared with conventional planar microfluidic channels and successfully extracted urine EV-encapsulated miRNAs.","PeriodicalId":42484,"journal":{"name":"Biomedical Engineering and Computational Biology","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2020-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1179597220941431","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38184752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Integrated Optical Fiber Force Myography Sensor as Pervasive Predictor of Hand Postures. 集成光纤肌力传感器作为手部姿势的普遍预测器。

IF 2.8

Biomedical Engineering and Computational Biology Pub Date : 2020-03-24 eCollection Date: 2020-01-01 DOI: 10.1177/1179597220912825

Yu Tzu Wu, Matheus K Gomes, Willian Ha da Silva, Pedro M Lazari, Eric Fujiwara

引用次数: 10

Extending Classification Algorithms to Case-Control Studies. 将分类算法扩展到案例对照研究。

IF 2.8

Biomedical Engineering and Computational Biology Pub Date : 2019-07-15 eCollection Date: 2019-01-01 DOI: 10.1177/1179597219858954

Bryan Stanfill, Sarah Reehl, Lisa Bramer, Ernesto S Nakayasu, Stephen S Rich, Thomas O Metz, Marian Rewers, Bobbie-Jo Webb-Robertson

{"title":"Extending Classification Algorithms to Case-Control Studies.","authors":"Bryan Stanfill, Sarah Reehl, Lisa Bramer, Ernesto S Nakayasu, Stephen S Rich, Thomas O Metz, Marian Rewers, Bobbie-Jo Webb-Robertson","doi":"10.1177/1179597219858954","DOIUrl":"10.1177/1179597219858954","url":null,"abstract":"Classification is a common technique applied to 'omics data to build predictive models and identify potential markers of biomedical outcomes. Despite the prevalence of case-control studies, the number of classification methods available to analyze data generated by such studies is extremely limited. Conditional logistic regression is the most commonly used technique, but the associated modeling assumptions limit its ability to identify a large class of sufficiently complicated 'omic signatures. We propose a data preprocessing step which generalizes and makes any linear or nonlinear classification algorithm, even those typically not appropriate for matched design data, available to be used to model case-control data and identify relevant biomarkers in these study designs. We demonstrate on simulated case-control data that both the classification and variable selection accuracy of each method is improved after applying this processing step and that the proposed methods are comparable to or outperform existing variable selection methods. Finally, we demonstrate the impact of conditional classification algorithms on a large cohort study of children with islet autoimmunity.","PeriodicalId":42484,"journal":{"name":"Biomedical Engineering and Computational Biology","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2019-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/d6/6d/10.1177_1179597219858954.PMC6630079.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10160328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Critical Care, Critical Data. 重症监护，关键数据。

IF 2.8

Biomedical Engineering and Computational Biology Pub Date : 2019-06-12 eCollection Date: 2019-01-01 DOI: 10.1177/1179597219856564

Christopher V Cosgriff, Leo Anthony Celi, David J Stone

{"title":"Critical Care, Critical Data.","authors":"Christopher V Cosgriff, Leo Anthony Celi, David J Stone","doi":"10.1177/1179597219856564","DOIUrl":"10.1177/1179597219856564","url":null,"abstract":"As big data, machine learning, and artificial intelligence continue to penetrate into and transform many facets of our lives, we are witnessing the emergence of these powerful technologies within health care. The use and growth of these technologies has been contingent on the availability of reliable and usable data, a particularly robust resource in critical care medicine where continuous monitoring forms a key component of the infrastructure of care. The response to this opportunity has included the development of open databases for research and other purposes; the development of a collaborative form of clinical data science intended to fully leverage these data resources, and the creation of data-driven applications for purposes such as clinical decision support. Most recently, data levels have reached the thresholds required for the development of robust artificial intelligence features for clinical purposes. The systematic capture and analysis of clinical data in both individuals and populations allows us to begin to move toward precision medicine in the intensive care unit (ICU). In this perspective review, we examine the fundamental role of data as we present the current progress that has been made toward an artificial intelligence (AI)-supported, data-driven precision critical care medicine.","PeriodicalId":42484,"journal":{"name":"Biomedical Engineering and Computational Biology","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2019-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1179597219856564","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37344611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 30

Multifidelity Analysis for Predicting Rare Events in Stochastic Computational Models of Complex Biological Systems. 复杂生物系统随机计算模型中罕见事件预测的多保真度分析。

IF 2.8

Biomedical Engineering and Computational Biology Pub Date : 2018-08-03 eCollection Date: 2018-01-01 DOI: 10.1177/1179597218790253

Elsje Pienaar

{"title":"Multifidelity Analysis for Predicting Rare Events in Stochastic Computational Models of Complex Biological Systems.","authors":"Elsje Pienaar","doi":"10.1177/1179597218790253","DOIUrl":"https://doi.org/10.1177/1179597218790253","url":null,"abstract":"Rare events such as genetic mutations or cell-cell interactions are important contributors to dynamics in complex biological systems, eg, in drug-resistant infections. Computational approaches can help analyze rare events that are difficult to study experimentally. However, analyzing the frequency and dynamics of rare events in computational models can also be challenging due to high computational resource demands, especially for high-fidelity stochastic computational models. To facilitate analysis of rare events in complex biological systems, we present a multifidelity analysis approach that uses medium-fidelity analysis (Monte Carlo simulations) and/or low-fidelity analysis (Markov chain models) to analyze high-fidelity stochastic model results. Medium-fidelity analysis can produce large numbers of possible rare event trajectories for a single high-fidelity model simulation. This allows prediction of both rare event dynamics and probability distributions at much lower frequencies than high-fidelity models. Low-fidelity analysis can calculate probability distributions for rare events over time for any frequency by updating the probabilities of the rare event state space after each discrete event of the high-fidelity model. To validate the approach, we apply multifidelity analysis to a high-fidelity model of tuberculosis disease. We validate the method against high-fidelity model results and illustrate the application of multifidelity analysis in predicting rare event trajectories, performing sensitivity analyses and extrapolating predictions to very low frequencies in complex systems. We believe that our approach will complement ongoing efforts to enable accurate prediction of rare event dynamics in high-fidelity computational models.","PeriodicalId":42484,"journal":{"name":"Biomedical Engineering and Computational Biology","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2018-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1179597218790253","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36380396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Granular Cell Tumor Imaging Using Optical Coherence Tomography. 利用光学相干断层扫描进行颗粒细胞肿瘤成像。

IF 2.8

Biomedical Engineering and Computational Biology Pub Date : 2018-08-02 eCollection Date: 2018-01-01 DOI: 10.1177/1179597218790250

David Tes, Ahmed Aber, Mohsin Zafar, Luke Horton, Audrey Fotouhi, Qiuyun Xu, Ali Moiin, Andrew D Thompson, Tatiana Cristina Moraes Pinto Blumetti, Steven Daveluy, Wei Chen, Mohammadreza Nasiriavanaki

{"title":"Granular Cell Tumor Imaging Using Optical Coherence Tomography.","authors":"David Tes, Ahmed Aber, Mohsin Zafar, Luke Horton, Audrey Fotouhi, Qiuyun Xu, Ali Moiin, Andrew D Thompson, Tatiana Cristina Moraes Pinto Blumetti, Steven Daveluy, Wei Chen, Mohammadreza Nasiriavanaki","doi":"10.1177/1179597218790250","DOIUrl":"10.1177/1179597218790250","url":null,"abstract":"Background: Granular cell tumor (GCT) is a relatively uncommon tumor that may affect the skin. The tumor can develop anywhere on the body, although it is predominately seen in oral cavities and in the head and neck regions. Here, we present the results of optical coherence tomography (OCT) imaging of a large GCT located on the abdomen of a patient. We also present an analytical method to differentiate between healthy tissue and GCT tissues.Materials and methods: A multibeam, Fourier domain, swept source OCT was used for imaging. The OCT had a central wavelength of 1305 ± 15 nm and lateral and axial resolutions of 7.5 and 10 µm, respectively. Qualitative and quantitative analyses of the tumor and healthy skin are reported.Results: Abrupt changes in architectures of the dermal and epidermal layers in the GCT lesion were observed. These architectural changes were not observed in healthy skin.Discussion: To quantitatively differentiate healthy skin from tumor regions, an optical attenuation coefficient analysis based on single-scattering formulation was performed. The methodology introduced here could have the capability to delineate boundaries of a tumor prior to surgical excision.","PeriodicalId":42484,"journal":{"name":"Biomedical Engineering and Computational Biology","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2018-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/5b/e2/10.1177_1179597218790250.PMC6088518.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36405562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Development and Optimization of a Fluorescent Imaging System to Detect Amyloid-β Proteins: Phantom Study. 淀粉样蛋白-β荧光成像系统的开发与优化:幻影研究。

IF 2.8

Biomedical Engineering and Computational Biology Pub Date : 2018-06-18 eCollection Date: 2018-01-01 DOI: 10.1177/1179597218781081

David Tes, Karl Kratkiewicz, Ahmed Aber, Luke Horton, Mohsin Zafar, Nour Arafat, Afreen Fatima, Mohammad Rn Avanaki

引用次数: 0

Application of a Causal Discovery Algorithm to the Analysis of Arthroplasty Registry Data. 应用因果发现算法分析关节成形术登记数据。

IF 2.8

Biomedical Engineering and Computational Biology Pub Date : 2018-02-22 eCollection Date: 2018-01-01 DOI: 10.1177/1179597218756896

Camden Cheek, Huiyong Zheng, Brian R Hallstrom, Richard E Hughes

{"title":"Application of a Causal Discovery Algorithm to the Analysis of Arthroplasty Registry Data.","authors":"Camden Cheek, Huiyong Zheng, Brian R Hallstrom, Richard E Hughes","doi":"10.1177/1179597218756896","DOIUrl":"10.1177/1179597218756896","url":null,"abstract":"Improving the quality of care for hip arthroplasty (replacement) patients requires the systematic evaluation of clinical performance of implants and the identification of \"outlier\" devices that have an especially high risk of reoperation (\"revision\"). Postmarket surveillance of arthroplasty implants, which rests on the analysis of large patient registries, has been effective in identifying outlier implants such as the ASR metal-on-metal hip resurfacing device that was recalled. Although identifying an implant as an outlier implies a causal relationship between the implant and revision risk, traditional signal detection methods use classical biostatistical methods. The field of probabilistic graphical modeling of causal relationships has developed tools for rigorous analysis of causal relationships in observational data. The purpose of this study was to evaluate one causal discovery algorithm (PC) to determine its suitability for hip arthroplasty implant signal detection. Simulated data were generated using distributions of patient and implant characteristics, and causal discovery was performed using the TETRAD software package. Two sizes of registries were simulated: (1) a statewide registry in Michigan and (2) a nationwide registry in the United Kingdom. The results showed that the algorithm performed better for the simulation of a large national registry. The conclusion is that the causal discovery algorithm used in this study may be a useful tool for implant signal detection for large arthroplasty registries; regional registries may only be able to only detect implants that perform especially poorly.","PeriodicalId":42484,"journal":{"name":"Biomedical Engineering and Computational Biology","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2018-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/9c/ed/10.1177_1179597218756896.PMC5826097.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35889049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Development and Retrospective Clinical Assessment of a Patient-Specific Closed-Form Integro-Differential Equation Model of Plasma Dilution. 血浆稀释患者特异性闭式积分-微分方程模型的建立和回顾性临床评估。

IF 2.8

Biomedical Engineering and Computational Biology Pub Date : 2017-10-26 eCollection Date: 2017-01-01 DOI: 10.1177/1179597217730305

Glen Atlas, John K-J Li, Shawn Amin, Robert G Hahn

{"title":"Development and Retrospective Clinical Assessment of a Patient-Specific Closed-Form Integro-Differential Equation Model of Plasma Dilution.","authors":"Glen Atlas, John K-J Li, Shawn Amin, Robert G Hahn","doi":"10.1177/1179597217730305","DOIUrl":"https://doi.org/10.1177/1179597217730305","url":null,"abstract":"A closed-form integro-differential equation (IDE) model of plasma dilution (PD) has been derived which represents both the intravenous (IV) infusion of crystalloid and the postinfusion period. Specifically, PD is mathematically represented using a combination of constant ratio, differential, and integral components. Furthermore, this model has successfully been applied to preexisting data, from a prior human study, in which crystalloid was infused for a period of 30 minutes at the beginning of thyroid surgery. Using Euler's formula and a Laplace transform solution to the IDE, patients could be divided into two distinct groups based on their response to PD during the infusion period. Explicitly, Group 1 patients had an infusion-based PD response which was modeled using an exponentially decaying hyperbolic sine function, whereas Group 2 patients had an infusion-based PD response which was modeled using an exponentially decaying trigonometric sine function. Both Group 1 and Group 2 patients had postinfusion PD responses which were modeled using the same combination of hyperbolic sine and hyperbolic cosine functions. Statistically significant differences, between Groups 1 and 2, were noted with respect to the area under their PD curves during both the infusion and postinfusion periods. Specifically, Group 2 patients exhibited a response to PD which was most likely consistent with a preoperative hypovolemia. Overall, this IDE model of PD appears to be highly \"adaptable\" and successfully fits clinically-obtained human data on a patient-specific basis, during both the infusion and postinfusion periods. In addition, patient-specific IDE modeling of PD may be a useful adjunct in perioperative fluid management and in assessing clinical volume kinetics, of crystalloid solutions, in real time.","PeriodicalId":42484,"journal":{"name":"Biomedical Engineering and Computational Biology","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2017-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1179597217730305","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35594652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3