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Single-cell RNA-sequencing and subcellular spatial transcriptomics facilitate the translation of liver microphysiological systems for regulatory application

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Journal of Pharmaceutical Analysis 2023年第7期13卷 691-693页

作者： Dan Li Zhou Fang Qiang Shi Nicholas Zhang Binsheng Gong Weida Tong Ahmet F.Coskun Joshua Xu Division of Bioinformatics and Biostatistics National Center for Toxicological ResearchU.S.Food and Drug AdministrationJeffersonAR72079USA Wallace H.Coulter Department of Biomedical Engineering Georgia Institute of Technology and Emory UniversityAtlantaGA30332USA Machine Learning Graduate Program Georgia Institute of TechnologyAtlantaGA30332USA Division of Systems Biology National Center for Toxicological ResearchU.S.Food and Drug AdministrationJeffersonAR72079USA Interdisciplinary Bioengineering Graduate Program Georgia Institute of TechnologyAtlantaGA30332USA Parker H.Petit Institute for Bioengineering and Bioscience Georgia Institute of TechnologyAtlantaGA30332USA

Reducing the use of animal models in drug development and safety assessment has long been supported by the *** and Drug Administration(FDA).The report by Royal Society for the Prevention of Cruelty to Animals indicates that in 2020,experiments involved the use of over 100 million animals,with the United States leading the list by utilizing 20 million *** ethical considerations associated with animal testing and the costs in terms of time and money,animal models are not always effective in predicting human reactions to drug *** animal testing has been the traditional method for assessing the safety and efficacy of drugs.

关键词： money utilizing testing

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Engineering Distance Learning: The Promise and Challenges of Microfluidics

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Biomedical Engineering Education 2023年第2期3卷 267-278页

作者： Levis, Megan Hyland, Clare Zartman, Jeremiah Chemical and Biomolecular Engineering Department The University of Notre Dame Notre Dame USA Bioengineering Graduate Program The University of Notre Dame Notre Dame USA

The recent trend of expanding online education in science and engineering creates the need for remote experiential learning opportunities. Microfluidic devices show great promise in fulfilling that need because they are small, transportable, cheap, and use safe volumes of reagents to complete experiments. The rapid increase of microfluidic devices for point-of-care diagnostics serves as a proof of concept for remote learning applications. This perspective highlights potential scenarios and issues that need to be considered in translating multimodal microfluidics into educational settings. Several educational scenarios are developed, and a Strengths, Weaknesses, Opportunities, and Threats gap analysis highlights the barriers and opportunities for implementing microfluidics in the classroom or in online education formats. Providing students with microfluidic competencies in increasing diverse educational formats will create new opportunities as the microfabrication and diagnostics industries continue to grow.

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Label-Free Classification of L-Histidine Vs Artificial Human Sweat Using Laser Scribed Electrodes and a Multi-Layer Perceptron Neural Network

Label-Free Classification of L-Histidine Vs Artificial Human...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： William García-Rodríguez Andrés Saavedra-Ruiz Pedro J. Resto-Irizarry Department of Mechanical Engineering University of Puerto Rico Mayagüez PR USA Bioengineering Graduate Program University of Puerto Rico Mayagüez PR USA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

A challenge in wearable technology lies in the realtime monitoring of molecular biomarkers associated with human health. Electrochemical sensors are one of the most useful tools for this purpose and are commonly used in health monitoring devices. Electrochemical biosensing is particularly convenient when used in user-friendly, low-cost devices for testing noninvasive body fluids such as sweat and saliva. However, achieving high selectivity and specificity in measurements depends on the complexity of the biomarker and the stability of the biomarker capture molecule. In this study, laser-scribed electrodes (LSEs) were manufactured using a CO2 laser cutter on Polyimide for the label-free classification of sweat components. Cyclic voltammetry experiments were performed on artificial human sweat and the sweat component L-Histidine. The resulting voltammogram data served as input to train a Multi-Layer Perceptron Neural Network (MLP-NN) algorithm capable of classifying L-Histidine and artificial sweat.

关键词： Electrodes Neural networks Measurement by laser beam Polyimides Laser stability Sensors Molecular biomarkers Laser beam cutting Monitoring Testing

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Advances in AAV capsid engineering: Integrating rational design, directed evolution and machine learning

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Molecular Therapy 2025年第5期33卷 1937-1945页

作者： Nisanov, Alan M. Rivera de Jesús, Julio A. Schaffer, David V. Department of Chemistry University of California Berkeley Berkeley 94720 CA United States Department of Chemical and Biomolecular Engineering University of California Berkeley Berkeley 94720 CA United States Department of Bioengineering University of California Berkeley Berkeley 94720 CA United States Graduate Program in Bioengineering University of California Berkeley San Francisco and University of California Berkeley 94720 CA United States Department of Neurological Surgery University of California San Francisco 94143 CA United States Department of Molecular and Cell Biology University of California Berkeley Berkeley 94720 CA United States

Adeno-associated virus (AAV) has emerged as a highly promising vector for human gene therapy due to its favorable safety profile, versatility, and ability to transduce a wide range of tissues. However, natural AAV serotypes have shortcomings, including suboptimal transduction efficiency, pre-existing immunity, and a lack of tissue specificity, that hinder their therapeutic potential. To address these challenges, significant efforts are being applied to engineer novel AAV capsids. Rational design leverages structural insights to enhance capsid properties, directed evolution enables unbiased selection of superior variants, and machine learning accelerates discovery by computational analysis of high-throughput screening results to enable predictive algorithms. These strategies have yielded novel capsids with improved transduction efficiency, reduced immunogenicity, and enhanced tissue targeting. Future advances that continue to integrate such multi-disciplinary approaches will further drive the clinical translation of AAV-based therapies. © 2025 The Authors

关键词： AAV directed evolution gene therapy high-throughput screening machine learning next generation sequencing NGS rational design

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Translational Bioinformatics to Enable Precision Medicine for All: Elevating Equity across Molecular, Clinical, and Digital Realms

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Yearbook of Medical Informatics 2022年第1期31卷 106-115页

作者： Tang, Alice Woldemariam, Sarah Roger, Jacquelyn Sirota, Marina Bakar Computational Health Sciences Institute UCSF San Francisco CA United States Graduate Program in Bioengineering UCSF San Francisco CA United States School of Medicine UCSF San Francisco CA United States Graduate Program in Biological and Medical Informatics UCSF San Francisco CA United States Department of Pediatrics UCSF San Francisco CA United States

Objectives: Over the past few years, challenges from the pandemic have led to an explosion of data sharing and algorithmic development efforts in the areas of molecular measurements, clinical data, and digital health. We aim to characterize and describe recent advanced computational approaches in translational bioinformatics across these domains in the context of issues or progress related to equity and inclusion. Methods: We conducted a literature assessment of the trends and approaches in translational bioinformatics in the past few years. Results: We present a review of recent computational approaches across molecular, clinical, and digital realms. We discuss applications of phenotyping, disease subtype characterization, predictive modeling, biomarker discovery, and treatment selection. We consider these methods and applications through the lens of equity and inclusion in biomedicine. Conclusion: Equity and inclusion should be incorporated at every step of translational bioinformatics projects, including project design, data collection, model creation, and clinical implementation. These considerations, coupled with the exciting breakthroughs in big data and machine learning, are pivotal to reach the goals of precision medicine for all. © 2022 IMIA and Georg Thieme Verlag KG.

关键词： Bioinformatics genomics health equity precision medicine

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Development of a Computational Simulator of the Physiological Control of Ventricular Assist Devices (VADs) 9th

Development of a Computational Simulator of the Physiologica...

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9th Latin American Congress on Biomedical Engineering, CLAIB 2022 and 28th Brazilian Congress on Biomedical Engineering, CBEB 2022

作者： Santos, B.J. Cestari, I.A. Biomedical Engineering Graduate Program Escola Politécnica de Engenharia Universidade de Sao Paulo Sao Paulo Brazil Federal Institute of Sao Paulo Sao Paulo Brazil Bioengineering Division—Heart Institute Hospital das Clinicas HCFMUSP Faculdade de Medicina Universidade de Sao Paulo Sao Paulo Brazil

ISBN: (纸本)9783031494000

Congestive Heart Failure (CHF) is a significant global health issue and considered the most debilitating form of Heart Failure with the heart unable to efficiently pump enough blood to the body, resulting in a high risk of mortality. Left Ventricular Assist Devices (LVADs) are utilized for providing mechanical circulatory assistance for patients with CHF. LVADs can generate pulsatile or continuous flows. Continuous flow LVADs may induce a more physiological flow pattern with some degree of pulsatility when the device is operated using a physiological type of controller. We present a computational simulator that can be used to compare hemodynamic responses of a simulated patient implanted with a LVAD, operated with or without a physiological controller. The computational simulator described here includes: (i) simulations of the patient’s condition;(ii) mathematical models of the estimators and sensors;and (iii) LVAD physiological control algorithms. The application of the simulator allowed us to investigate the performance of physiological control strategies considering various scenarios for LVAD applications. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Controllers

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OLIGONUCLEOTIDE FUNCTIONALIZED POLYACRYLAMIDE BEADS FOR AMPLIFICATION BASED BIOMARKER DETECTION 25

OLIGONUCLEOTIDE FUNCTIONALIZED POLYACRYLAMIDE BEADS FOR AMPL...

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25th International Conference on Miniaturized Systems for Chemistry and Life Sciences, MicroTAS 2021

作者： Chaves, Rodrigo Cotrim Streets, Aaron University of California BerkeleyCA United States UC Berkeley UCSF Graduate Program in Bioengineering CA United States

ISBN: (纸本)9781733419031

Single-cell quantitative biomarker analysis is a powerful technique for studying tissue heterogeneity, but commonly used staining techniques cannot detect markers with lower than 100 target molecules per cell [1]. Here we report a droplet microfluidic platform for encapsulation of samples in Polyacrylamide (PA) hydrogel beads, and staining for fluorescence analysis. This is achieved by functionalizing the PA with oligonucleotides designed to capture biomarkers of interest, and amplifying targets through PCR, enabling sensitive detection of DNA, RNA or protein molecules with low copy number for quantification of cellular populations with traditional flow cytometry. © 2021 MicroTAS 2021 - 25th International Conference on Miniaturized Systems for Chemistry and Life Sciences. All rights reserved.

关键词： Oligonucleotides

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Multi-class Segmentation of Anatomical Structures Using Deep Learning in CBCT Images Containing Metal Artifacts

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Transactions of the Korean Institute of Electrical Engineers 2022年第1期71卷 253-260页

作者： Yang, Su Chun, Soyoung Kim, Dael Jeoun, Bo Soung Yoo, Jiyong Kang, Se-Ryong Choi, Min-Hyuk Kim, Jo-Eun Huh, Kyung-Hoe Lee, Sam-Sun Heo, Min-Suk Yi, Won-Jin Dept. of Applied Bioengineering Graduate School of Convergence Science and Technology Seoul National University Seoul Korea Republic of Interdisciplinary Program of Bioengineering Seoul National University Seoul Korea Republic of Dept. of Biomedical Radiation Sciences Graduate School of Convergence Science and Technology Seoul National University Seoul Korea Republic of Dept. of Oral and Maxillofacial Radiology Seoul National University Dental Hospital Seoul Korea Republic of Dept. of Oral and Maxillofacial Radiology School of Dentistry and Dental Research Institute Seoul National University Seoul Korea Republic of

In order to perform preoperative surgical planning, accurate segmentation of anatomical structures in cone-beam computed tomography (CBCT) images is required. However, this image segmentation is often impeded by metal artifacts, and it takes a lot of time due to morphological variability in patients. In this paper, we proposed a deep learning based automatic multi-calss segmentation method for anatomical structures in CBCT images containing metal artifacts. Four U-Net based deep learning models were used for anatomical structure segmentation. Each deep learning model was constructed by changing the encoder of U-Net architecture to the backbones (DenseNet121, VGGNet16, ResNet101, and EfficienNetB4). For training and testing our method, we used 20744 CBCT images containing metal artifacts from 30 patient datasets. Experimental results show that the segmentation performances of the mandible, midfacial bone, mandibular canal, and maxillary sinus were achieved F1 scores of , , , and using DenseNet121 with Tversky loss, respectively. Furthermore, our method was able to perform robust and accurate segmentation of anatomical structures in CBCT images containing metal artifacts. © 2022 Korean Institute of Electrical Engineers. All rights reserved.

关键词： Image segmentation

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RRED: A Radiology Report Error Detector based on Deep Learning Framework 4

RRED: A Radiology Report Error Detector based on Deep Learni...

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4th Workshop on Clinical Natural Language Processing, ClinicalNLP 2022

作者： Min, Dabin Kim, Kaeun Lee, Jong Hyuk Kim, Yisak Park, Chang Min Department of Radiology Seoul National University Hospital Korea Republic of Interdisciplinary Program in Bioengineering Seoul National University Graduate School Korea Republic of Department of Radiology Seoul National University College of Medicine Korea Republic of

ISBN: (纸本)9781955917773

Radiology report is an official record of radiologists’ interpretation of patients’ radiographs and it’s a crucial component in the overall medical diagnostic process. However, it can contain various types of errors that can lead to inadequate treatment or delay in diagnosis. To address this problem, we propose a deep learning framework to detect errors in radiology reports. Specifically, our method detects errors between findings and conclusion of chest X-ray reports based on a supervised learning framework. To compensate for the lack of data availability of radiology reports with errors, we develop an error generator to systematically create artificial errors in existing reports. In addition, we introduce a Medical Knowledge-enhancing Pre-training to further utilize the knowledge of abbreviations and key phrases frequently used in the medical domain. We believe that this is the first work to propose a deep learning framework for detecting errors in radiology reports based on a rich contextual and medical understanding. Validation on our radiologist-synthesized dataset, based on MIMIC-CXR, shows 0.80 and 0.95 of the area under precision-recall curve (AUPRC) and the area under the ROC curve (AUROC) respectively, indicating that our framework can effectively detect errors in the real-world radiology reports. © 2022 Association for Computational Linguistics.

关键词： Errors

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Spectral DiffuserScope - Computational Snapshot Hyperspectral Imager for Microscopy

Spectral DiffuserScope - Computational Snapshot Hyperspectra...

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2023 Computational Optical Sensing and Imaging, COSI 2023 in Optica Imaging Congress - Part of Imaging and Applied Optics Congress 2023

作者： Aggarwal, Neerja Markley, Eric Raniwala, Yashovardhan Houck, William D. Lee, Kyung Chul Bae, Junghyun Baek, Nakkyu Park, Wook Lee, Seung Ah Waller, Laura Department of Electrical Engineering and Computer Sciences University of California Berkeley United States UCB/UCSF Joint Graduate Program in Bioengineering University of California Berkeley United States VIAVI Solutions Inc Santa Rosa United States School of Electrical and Electronic Engineering Yonsei University Seoul Korea Republic of

We introduce a computational snapshot hyperspectral imager for benchtop microscopy. The compact device uses a diffuser and spectral filter array to multiplex spatio-spectral information into a 2D measurement, then rec... 详细信息

ISBN: (纸本)9781957171289

关键词： Blue light emitting diodes Diffusers Fluorescence microscopy Image sensors Spatial resolution Ultraviolet light emitting diodes

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