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Predictive Modeling Leveraging Electronic Health Records Identifies Early Predictors of Alzheimer’s Disease

Alzheimer's & Dementia 2023年第S15期19卷

作者： Alice S Tang Katherine P Rankin Gabriel Cerano Silvia Miramontes Hunter Mills Tomiko Oskotsky Billy Zeng Charlotte Nelson Karthik Soman Sarah Woldemariam Yaqiao Li Albert M Lee Zachary Miller Isabel Elaine Allen Stephan Sanders Sergio Baranzini Marina Sirota Graduate Program in Bioengineering University of California San Francisco and Berkeley San Francisco CA USA Bakar Computational Health Sciences Institute University of California San Francisco San Francisco CA USA Correspondece Memory and Aging Center UCSF Weill Institute for Neurosciences University of California San Francisco San Francisco CA USA Department of Neurology University of California San Francisco San Francisco CA USA Department of Epidemiology and Biostatistics University of California San Francisco San Francisco CA USA Department of Paediatrics University of Oxford Oxford United Kingdom Department of Pediatrics University of California San Francisco San Francisco CA USA

Background Alzheimer’s Disease (AD) is a neurodegenerative disorder of aging that is difficult to diagnose. Electronic health records (EHR) are a valuable source of longitudinal data for disease phenotyping and prediction. Knowledge graphs, such as SPOKE (Scalable Precision Medicine Open Knowledge Engine), help derive biological meaning from phenotypic data. Here we utilize machine learning on EHR to predict and better understand AD. Method AD patients and controls were identified from the UCSF EHR. The index time was identified as the first dementia diagnosis or drug record among the AD patients, and 1 year before the last visit among controls. Controls matched on demographics and visit-related factors were identified at five time points relative to the index time (-7years, -5years, -3years, -1year, and -1day) at a 1:8 ratio. Random forest (RF) classification models for AD were trained utilizing clinical data (conditions, drugs, abnormal measurements) and evaluated with bootstrapped AUROCs on a 30% held-out set. Top features mapped to phecodes were ranked based on average impurity decrease. Networks were created from SPOKE knowledge graph by identifying shortest paths between top 25 model features and AD, and associations were validated with genetic colocalization analysis on shared variants in the UK Biobank. Result From the UCSF EHR, 749 AD patients and 250,545 Controls were identified with clinical concepts 7 years prior to each individual’s index time. RF trained on AD and matched control individuals’ data performed with AUROC of 0.58 (-7years) to 0.77 (-1day). Top phecode features from models include those that are important earlier (osteoarthritis, allergic rhinitis), progress in importance closer to index time (dizziness, vitamin D deficiency, memory loss), and persist in importance throughout time (hyperlipidemia) (Figure 1). Knowledge networks highlight shared biological relationships between top features (e.g., hyperlipidemia, osteoporosis), including ide

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Toward Quantification and Visualization of Active Stress Waves for Myocardial Biomechanical Function Assessment

Toward Quantification and Visualization of Active Stress Wav...

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2019 Computing in Cardiology, CinC 2019

作者： Otani, Niels F. Linte, Cristian A. Dang, Dylan Beam, Christopher Mohammadi, Fariba Wentz, Brian Kamrul Hasan, S.M. Shontz, Suzanne M. Schwarz, Karl Q. Thomas, Sabu Rochester Institute of Technology School of Mathematical Sciences RochesterNY United States Mechanical Engineering University of Kansas LawrenceKS United States Bioengineering Graduate Program University of Kansas LawrenceKS United States Center for Imaging Science Rochester Institute of Technology RochesterNY United States Electrical Engineering and Computer Science University of Kansas LawrenceKS United States Division of Cardiology University of Rochester Medical Center RochesterNY United States RochesterNY14467 United States

ISBN: (纸本)9781728169361

Estimating and visualizing myocardial active stress wave patterns is crucial to understanding the mechanical activity of the heart and provides a potential non-invasive method to assess myocardial function. These patterns can be reconstructed by analyzing 2D and/or 3D tissue displacement data acquired using medical imaging. Here we describe an application that utilizes a 3D finite element formulation to reconstruct active stress from displacement data. As a proof of concept, a simple cubic mesh was used to represent a myocardial tissue "sample" consisting of a 10 x 10 x 10 lattice of nodes featuring different fiber directions that rotate with depth, mimicking cardiac transverse isotropy. In the forward model, tissue deformation was generated using a test wave with active stresses that mimic the myocardial contractile forces. The generated deformation field was used as input to an inverse model designed to reconstruct the original active stress distribution. We numerically simulated malfunctioning tissue regions (experiencing limited contractility and hence active stress) within the healthy tissue. We also assessed model sensitivity by adding noise to the deformation field generated using the forward model. The difference image between the original and reconstructed active stress distribution suggests that the model accurately estimates active stress from tissue deformation data with a high signal-to-noise ratio. © 2019 Creative Commons.

关键词： Medical imaging

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A micromirror array with annular partitioning for high-speed random-access axial focusing

arXiv

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arXiv 2020年

作者： Ersumo, Nathan Tessema Yalcin, Cem Antipa, Nick Pégard, Nicolas Waller, Laura Lopez, Daniel Muller, Rikky University of California Berkeley - University of California San Francisco Graduate Program in Bioengineering United States Departement of Electrical Engineering & Computer Sciences University of California BerkeleyCA94720 United States Department of Applied Physical Sciences University of North Carolina at Chapel Hill Chapel HillNC27514 United States Chan Zuckerberg Biohub San FranciscoCA94158 United States Physical Measurement Laboratory National Institute of Standards and Technology GaithersburgMD20899 United States

Dynamic axial focusing functionality has recently seen widespread incorporation in microscopy, augmented/virtual reality (AR/VR), adaptive optics, and material processing. Yet the limitations of existing varifocal tools continue to beset the performance capabilities and operating overhead of the optical systems that mobilize such functionality. The varifocal tools that are the least burdensome to drive (ex: liquid crystal, elastomeric or optofluidic lenses) suffer from low (~100 Hz) refresh rates. Conversely, the fastest devices sacrifice either critical capabilities such as dwelling capacity (ex: acoustic gradient lenses or monolithic micromechanical mirrors) or low operating overhead (ex: deformable mirror arrays). Here, we present a general-purpose random-access axial focusing device that bridges these previously conflicting features of high speed, dwelling capacity and lightweight drive by employing low-rigidity micromirrors that exploit the robustness of defocusing phase profiles. Geometrically, the device consists of an 8.2 mm diameter array of piston-motion, 48 μm-pitch micromirror pixels that provide 2π phase shifting for wavelengths shorter than 1 100 nm with 10-90% settling in 64.8 μs (i.e. 15.44 kHz refresh rate). The pixels are electrically partitioned into 32 rings for a driving scheme that enables phase-wrapped operation with circular symmetry and requires less than 30 V/channel. Optical experiments demonstrated the array’s wide focusing range with a measured ability to target 29 distinct, resolvable depth planes. Overall, the features of the proposed array offer the potential for compact, straightforward methods of tackling bottlenecked applications including high-throughput single-cell targeting in neurobiology and the delivery of dense 3D visual information in AR/VR. Copyright © 2020, The Authors. All rights reserved.

关键词： Pixels

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Myelination of Dorsal Root Ganglia Neurons Cocultured with Schwann Cells in a Three-Dimensional Tissue-Engineered Model

SSRN

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SSRN 2020年

作者： Shahidi, Sahar Janmaleki, Mohsen Riaz, Saba Nezhad, Amir Sanati Syed, Naweed Hotchkiss Brain Institute Alberta Children’s Hospital Research Institute University of Calgary CalgaryABT2N 1N4 Canada BioMEMS and Bioinspired Microfluidic Laboratory Biomedical Engineering Graduate Program University of Calgary CalgaryABT2N 1N4 Canada Center for BioEngineering Research and Education University of Calgary CalgaryABT2N 1N4 Canada

Investigating axonal myelination by Schwann cells (SCs) in three-dimensional (3D) tissue constructs is crucial to understand mechanisms underlying demyelination and remyelination, helping to gain insights into incurable disorders like neurodegenerative diseases. The existing 3D tissue models have shown a limited performance to permit visualization of direct between SCs and individual axons. In this study, a biocompatible gelatin-based hydrogel, gelatin methacrylate (GelMA), was refined and optimized to achieve the biocompatibility, porosity, mechanical stability, and degradability needed to provide high cell viability for dorsal root ganglia (DRG) neurons and SCs and to enable their long-term culture needed for myelination studies. The results of cell viability, neurite elongation, SC function and maturation, SC-axon interaction, and myelination were compared to the two other commonly used substrates, namely collagen and Poly-D Lysine (PDL). The tuned GelMA constructs enhanced single axon generation (unlike collagen) and promoted the interaction of DRG neurons and SCs (unlike PDL). Further adjusting the hydrogel properties to achieve two distinct ranges of relatively small and large pores supported SCs to extend their processes freely and enable physical contact with and wrapping around their corresponding axons. Staining the cells with myelin basic protein (MBA) and myelin-associated glycoprotein (MAG) revealed the myelination process on GelMA hydrogels. Moreover, the engineered porosity enhanced DRGs and SCs attachments and flexibility of movement across the substrate. Finally, various cellular mechanisms related to the superiority of GelMA properties for myelination studies were discussed. This engineered GelMA 3D structure can be further used to model demyelination in neurodegenerative diseases and study effect of various drug compounds on myelin regeneration. © 2020, The Authors. All rights reserved.

关键词： Neurons

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Front Cover: Mechanism toward Turn-on of Polysaccharide-Porphyrin Complexes for Fluorescence Probes and Photosensitizers in Photodynamic Therapy in Living Cells (5/2021)

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ChemMedChem 2021年第5期16卷

作者： Shodai Hino Rikushi Funada Dr. Kouta Sugikawa Dr. Riku Kawasaki Prof. Kazuya Koumoto Dr. Toshio Suzuki Prof. Takeshi Nagasaki Prof. Atsushi Ikeda Applied Chemistry Program Graduate School of Advanced Science and Engineering Hiroshima University 1-4-1 Kagamiyama Higashi Hiroshima 739-8527 Japan Department of Nanobiochemistry Frontiers of Innovative Research in Science and Technology (FIRST) Konan University 7-1-20 Minatojima-Minamimachi Kobe Hyogo 650-0047 Japan Department of Applied Chemistry and Bioengineering Graduate School of Engineering Osaka City University 3-3-138 Sugimoto Sumiyoshi-ku Osaka 558-8585 Japan

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Extreme MRI: Large-scale volumetric dynamic imaging from continuous non-gated acquisitions

arXiv

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arXiv 2019年

作者： Ong, Frank Zhu, Xucheng Cheng, Joseph Y. Johnson, Kevin M. Larson, Peder E.Z. Vasanawala, Shreyas S. Lustig, Michael Stanford University CA United States UC Berkeley-UCSF Graduate Program in Bioengineering University of California San Francisco University of California BerkeleyCA United States University of Wisconsin MadisonWI United States University of California San FranciscoCA United States

Purpose: To develop a framework to reconstruct large-scale volumetric dynamic MRI from rapid continuous and non-gated acquisitions, with applications to pulmonary and dynamic contrast enhanced (DCE) imaging. Theory and Methods: The problem considered here is a reconstruction of hundred-gigabytes of dynamic volumetric image data from a few gigabytes of k-space data, acquired continuously over several minutes. This is a vastly under-determined optimization, with problem sizes heavily stressing compute resources as well as memory management and storage. To overcome these challenges, we leverage intrinsic three dimensional (3D) trajectories, such as 3D radial and 3D cones, with ordering that incoherently cover time and k-space over the entire acquisition. We then propose two innovations: (1) A compressed representation using multi-scale low rank matrix factorization that both constrains the reconstruction problem and reduces its memory footprint. (2) Stochastic optimization to reduce computation, improve memory locality and minimize communications between threads and processors. We demonstrate the feasibility of the proposed method on DCE imaging acquired with a golden-angle ordered 3D cones trajectory and pulmonary imaging acquired with a bit-reversed ordered 3D radial trajectory. We compare it with "soft-gated" dynamic reconstruction for DCE and respiratory resolved reconstruction for pulmonary imaging. Results: The proposed technique shows transient dynamics that are not seen in gating based methods. When applied to datasets with irregular, or non-repetitive motions, the proposed method also displays sharper image features. Conclusion: We demonstrated a method that can reconstruct massive 3D dynamic image series in the extreme undersampling and extreme computation setting. Copyright © 2019, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Characterizing cellular mechanical phenotypes with mechanonode-pore sensing

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Microsystems & Nanoengineering 2018年第1期4卷 438-449页

作者： Junghyun Kim Sewoon Han Andy Lei Masaru Miyano Jessica Bloom Vasudha Srivastava Martha R.Stampfer Zev J.Gartner Mark A.LaBarge Lydia L.Sohn Department of Mechanical Engineering University of California at BerkeleyBerkeleyCA 94720-1740USA Department of Bioengineering University of California at BerkeleyBerkeleyCA 94720-1762USA Department of Population Sciences Beckman Research InstituteCity of HopeDuarteCA 91010USA Department of Pharmaceutical Chemistry University of CaliforniaSan FranciscoSan FranciscoCA 94143USA Biological Systems and Engineering Division Lawrence Berkeley National LaboratoryCA 94720USA Graduate Program in Bioengineering University of CaliforniaBerkeleyand University of CaliforniaSan FranciscoBerkeleyCA 94720USA

The mechanical properties of cells change with their differentiation,chronological age,and malignant ***,these properties may be useful label-free biomarkers of various functional or clinically relevant cell ***,we demonstrate mechano-node-pore sensing(mechano-NPS),a multi-parametric single-cell-analysis method that utilizes a four-terminal measurement of the current across a microfluidic channel to quantify simultaneously cell diameter,resistance to compressive deformation,transverse deformation under constant strain,and recovery time after *** define a new parameter,the whole-cell deformability index(wCDI),which provides a quantitative mechanical metric of the resistance to compressive deformation that can be used to discriminate among different cell *** wCDI and the transverse deformation under constant strain show malignant MCF-7 and A549 cell lines are mechanically distinct from non-malignant,MCF-10A and BEAS-2B cell lines,and distinguishes between cells treated or untreated with cytoskeleton-perturbing small *** categorize cell recovery time,ΔT_(r),as instantaneous(ΔTr~0 ms),transient(ΔT_(r)≤40 ms),or prolonged(ΔT_(r)>40 ms),and show that the composition of recovery types,which is a consequence of changes in cytoskeletal organization,correlates with cellular *** the wCDI and cell-recovery time,mechano-NPS discriminates between sub-lineages of normal primary human mammary epithelial cells with accuracy comparable to flow cytometry,but without antibody ***-NPS identifies mechanical phenotypes that distinguishes lineage,chronological age,and stage of malignant progression in human epithelial cells.

关键词： microfluidics mechanical phenotyping node-pore sensing biosensors label-free

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Accessible and accurate cytometry analysis of adherent cells using fluorescence microscopes

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Scientific reports 2025年第1期15卷 18691页

作者： Daniel Foyt Yiming Kuang Samma Rehem Klaus Yserentant Bo Huang UCSF-UC Berkeley Joint Graduate Program in Bioengineering University of California San Francisco San Francisco CA 94143 USA. daniel_foyt@berkeley.edu. Department of Pharmaceutical Chemistry University of California San Francisco San Francisco CA 94143 USA. Barnard College of Columbia University New York City NY 10027 USA. Department of Pharmaceutical Chemistry University of California San Francisco San Francisco CA 94143 USA. bo.huang@ucsf.edu. Department of Biochemistry and Biophysics University of California San Francisco San Francisco CA 94143 USA. bo.huang@ucsf.edu. Chan Zuckerberg Biohub San Francisco San Francisco CA 94158 USA. bo.huang@ucsf.edu.

We have developed a method along with a Python-based analysis tool to capture images and produce flow-cytometry-like data for adherent cell culture utilizing simple accessible microscopes. Leveraging the recently developed generalist algorithms for cell segmentation, our approach efficiently quantifies single-cell fluorescence signals. We demonstrated the utility of this method by screening a set of 88 prime editing conditions using the integration of mNeonGreen2 as a reporter.

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BPS2025 - Illuminating the dynamic kinome in living cells with KARkey

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Biophysical Journal 2025年第3期124卷 502a-502a页

作者： Anne C. Lyons Tomer M. Yaron-Barir Zhou Yuan Qian-yi Zhang Daniel Stehle Dean Boecher Jared Johnson Lewis C. Cantley Danielle Schmitt Sohum Mehta Yingxiao Wang Jin Zhang Department of Pharmacology University of California San Diego La Jolla CA USA Shu Chien-Gene Lay Department of Bioengineering University of California San Diego La Jolla CA USA Vagelos College of Physicians and Surgeons Columbia University New York NY USA Alfred E. Mann Department of Biomedical Engineering University of Southern California Los Angeles CA USA Biomedical Sciences Graduate Program University of California San Diego La Jolla CA USA Meyer Cancer Center Weill Cornell Medicine New York NY USA Department of Cell Biology Harvard Medical School Boston MA USA Department of Chemistry and Biochemistry University of California Los Angeles La Jolla CA USA Department of Chemistry and Biochemistry University of California San Diego La Jolla CA USA

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Integrating Genomic & Transcriptomic Features for Noninvasive Detection, Characterization, and Monitoring of T-Cell Lymphomas

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Blood 2024年 144卷 454-454页

作者： Takeshi Sugio Monica Nesselbush Navika Shukla Andrea Garofalo Jurik A. Mutter Mohammad Shahrokh Esfahani Stefan K. Alig Shuyu Shi Troy Noordenbos Mark P. Hamilton Cedric Rossi Feng Tian Chih Long Liu Mari Olsen Xiaoman Kang David A Russler-Germain Steven Horwitz Koji Kato Ayumu Ito Makoto Yamagishi Ash A. Alizadeh Department of Medicine Divisions of Oncology and Hematology Stanford University Stanford CA Program in Cancer Biology Stanford School of Medicine Stanford CA Divisions of Oncology and Hematology Stanford University Stanford CA Department of Radiation Oncology Stanford University Stanford CA Dept. of Bioengineering Schools of Engineering and Medicine Stanford CA Division of Blood and Marrow Transplantation and Cellular Therapy Stanford University School of Medicine Palo Alto CA CHU Dijon DIJON France Department of Medicine Division of Oncology Stanford University Stanford CA Siteman Cancer Center Division of Oncology Washington University School of Medicine Saint Louis MO Lymphoma Service Department of Medicine Memorial Sloan Kettering Cancer Center New York NY Department of Medicine and Biosystemic Science Kyushu University Graduate School of Medicine Fukuoka Japan Department of Hematopoietic Stem Cell Transplantation National Cancer Center Hospital Tokyo Japan Department of Computational Biology and Medical Sciences Graduate School of Frontier Sciences The University of Tokyo Tokyo JPN

Background : Mature T-cell lymphoma (TCL) outcomes remain inferior to aggressive B-cell counterparts, and thus represent unmet need. Given high relapse rates after remissions induced by current regimens, more sensitive MRD methods are needed to personalize treatment. We tackled these questions by integrating distinct host and viral molecular features for noninvasive detection, risk profiling, and monitoring of diverse mature TCLs. Methods : We studied 132 patients diagnosed with diverse TCLs (PTCL-NOS n=21, ALCL n=13, TFH lymphoma n=21, ATLL n=54, ENKTL n=6, CTCL n=15, others n=2). 37% of pts had newly diagnosed TCL primarily treated with CHOP-based regimens, while 62% had relapsed/refractory disease treated with salvage chemotherapies (26%), nivolumab (5%), and/or allogeneic HSCT (31%). We molecularly profiled 530 specimens, including serial blood plasma samples drawn before, during, and at the end-of-therapy (EOT) (n=274), baseline tumor tissues (n=128), and matched normal cells (n=128). Plasma cfDNA for all cases were profiled to detect somatic mutations in 259 recurrently mutated TCL genes by CAPP-Seq, genome-wide CNVs by CANARy (Chabon 2020 Nature), clonotypic VDJ at all TCR/BCR loci by SABER, T/B-cell abundance by QUARTZ (Sugio ASH 2024 abstract #193441), and inferred expression of 381 TCL/immune-related genes by EPIC-Seq (Esfahani Nature biotechnology 2022). Viral load and genotyping for EBV and HTLV-1 were assessed by VirCAPP-Seq (Garofalo ASH2022). Plasma cfRNA expression was profiled using RARE-Seq, by targeting 6.8k coding genes (Nesselbush 2024 Cancer Research). Results : We found that mutation-based disease burden measurements in cfDNA by CAPP-Seq were significantly correlated with clonotypic cell-free TCR levels (cfTCR; R=0.95, p<0.001) across all patients and plasma HTLV-1 load in ATLL patients (R=0.84, p<0.001). When considering outcomes after CHOP-based regimens, detection of ctDNA-MRD at EOT predicted significantly higher relapse risk (p= 0.003), i

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