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Npj imaging 2024年第1期2卷 56页

作者： Karissa Chan Corinne Fischer Pejman Jabehdar Maralani Sandra E Black Alan R Moody April Khademi Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University Toronto ON Canada. Institute for Biomedical Engineering Science Tech (iBEST) a Partnership between St. Michael's Hospital and Toronto Metropolitan University Toronto ON Canada. Keenan Research Center for Biomedical Science St. Michael's Hospital Unity Health Network Toronto ON Canada. Department of Medical Imaging University of Toronto Toronto ON Canada. Institute of Medical Science University of Toronto Toronto ON Canada. Horvitz Brain Sciences Research Program Sunnybrook Research Institute Toronto ON Canada. Division of Neurology Department of Medicine Sunnybrook Health Sciences Centre Toronto ON Canada. L.C. Campbell Cognitive Neurology Research Unit Sunnybrook Health Sciences Centre Toronto ON Canada. Vector Institute for Artificial Intelligence Toronto ON Canada.

This study proposes a framework to stratify vascular disease patients based on brain health and cerebrovascular disease (CVD) risk using regional FLAIR biomarkers. Intensity and texture biomarkers were extracted from FLAIR volumes of 379 atherosclerosis patients. K-Means clustering identified five homogeneous subgroups. The 15 most important biomarkers for subgroup differentiation, identified via Random Forest classification, were used to generate biomarker profiles. ANOVA tests showed age and white matter lesion volume were significantly ( < 0.05) different across subgroups, while Fisher's tests revealed significant ( < 0.05) differences in the prevalence of several vascular risk factors across subgroup. Based on biomarker and clinical profiles, Subgroup 4 was characterized with neurodegeneration unrelated to CVD, Subgroup 3 identified patients with high CVD risk requiring aggressive intervention, and Subgroups 1, 2, and 5 identified patients with varying levels of moderate risk, suitable for long-term lifestyle interventions. This study supports personalized treatment and risk stratification based on FLAIR biomarkers.

关键词： Brain imaging Image processing

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1,3-Diketone Analogs as Selective Lysyl Hydroxylase 2 (Lh2) Antagonists

SSRN

引用

SSRN 2023年

作者： Lee, Juhoon Guo, Hou-Fu Maghsoud, Yazdan Vázquez-Montelongo, Erik Antonio Jing, Zhifeng Wang, Shike Sammons, Rachel M. Cho, Eun Jeong Ren, Pengyu Andrés, G. Cisneros Kurie, Jonathan M. Dalby, Kevin N. Division of Chemical Biology and Medicinal Chemistry College of Pharmacy University of Texas at Austin AustinTX78712 United States Targeted Therapeutic Drug Discovery and Development Program College of Pharmacy University of Texas AustinTX78712 United States Department of Molecular and Cellular Biochemistry University of Kentucky College of Medicine KY40536 United States Department of Chemistry and Biochemistry The University of Texas at Dallas RichardsonTX75080 United States Department of Physical Medicine and Rehabilitation The University of Texas Southwestern Medical Center DallasTX75390 United States Department of Biomedical Engineering The University of Texas at Austin AustinTX78712 United States Departmnt of Thoracic/Head and Neck Medical Oncology The University of Texas MD Anderson Cancer Center HoustonTX77030 United States Department of Physics The University of Texas at Dallas RichardsonTX75080 United States

Lysyl hydroxylase 2 (LH2), an Fe(II) and α-ketoglutarate (αKG, also called 2-oxoglutarate, or 2OG)-dependent oxygenase, is an endoplasmic reticulum-resident enzyme that hydroxylates telopeptidyl lysine residues on fibrillar collagen molecules. It leads to the formation of hydroxylysine aldehyde-derived collagen cross-links (HLCCs), which are more stable than lysine aldehyde-derived collagen cross-links (LCCs) generated devoid of LH2. It has been reported that LH2 enhances lung cancer metastatic and invasive proclivity and modulates the types of collagen cross-links (HLCC-to-LCC) in the tumor stroma. Herein, we prepared a series of 1,3-diketone analogs 1–18 and identified 12 and 13 that inhibit the LH2-driven hydroxylation of a collagen peptide substrate with IC50 approximately 300 nM and 500 nM, respectively. 12 and 13 demonstrate a 9-fold selectivity for LH2 over LH1 and LH3. Quantum Mechanics/Molecular Mechanics (QM/MM) modeling indicates that in addition to the relatively stronger interactions between compounds 12 and 13 with the active site, the selectivity stems from non-covalent interactions like hydrogen bonding between the morpholine/piperazine rings with LH2-specific Arg661, where the corresponding residue in LH1 and LH3 is Pro. Migration assays in the 344SQ lung adenocarcinoma cell line reveal that 13 shows anti-migration activity. © 2023, The Authors. All rights reserved.

关键词： Aldehydes

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Prioritizing Strategies for Building the Resilience of Public Health Systems to Disasters Across Multiple Communities and Countries

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International Journal of Disaster Risk Science 2024年第1期15卷 1-17页

作者： Benjamin J.Ryan Mayumi Kako Shelby Garner Rok Fink Ismail Tayfur Jonathan Abrahams Sanjaya Bhatia Adriana Campelo Matthew Fendt Alicia Fontenot Nahuel Arenas Garcia Tim Hatch Ryoma Kayano LaShonda Malrey‑Horne Makiko MacDermot Md Moshiur Rahman Chaverle Noel Shuhei Nomura Jeremy P.Novak Maria Opazo Kendell Oliver Luciana Peters Sohel Rahman PerihanŞimşek Andrew Stricklin Raymond Swienton Bryan W.Brooks Frist College of Medicine Belmont UniversityNashvilleTN 37212USA Department of Environmental Science Environmental Health Science ProgramCollege of Arts and SciencesBaylor UniversityWacoTX 76798USA Division of Nursing Science School of Biomedical and Health SciencesHiroshima UniversityHiroshima 734-8553Japan Gordon E.Inman College of Health Sciences and Nursing Belmont UniversityNashvilleTN 37212USA Faculty of Health Sciences University of Ljubljana1000 LjubljanaSlovenia Department of Emergency Medicine University of Health Sciences34668 IstanbulTurkey Monash University Accident Research Center MelbourneVIC 3800Australia Global Education and Training Institute United Nations Ofce for Disaster Risk ReductionIncheon 22004Republic of Korea Regional Ofce for the Americas and Caribbean United Nations Ofce for Disaster Risk ReductionPanama City 0843-03441Panama Engineering and Computer Science Baylor UniversityWacoTX 76798USA Department of Public Health Robbins College of Health and Human SciencesBaylor UniversityWacoTX 76798USA Alabama Department of Public Health MontgomeryAL 36092USA World Health Organization Centre for Health Development(WHO Kobe Centre) Kobe 651-0073Japan Waco-McLennan County Public Health District WacoTX 76707USA Department of Health Policy and Management School of MedicineKeio UniversityTokyo 160-8582Japan Department of Global Health Policy Graduate School of MedicineThe University of TokyoTokyo 113-0033Japan Tokyo Foundation for Policy Research Tokyo 106-6234Japan Global Development College BrisbaneQLD 4000Australia Municipality of Pudahuel 13124 PudahuelChile City Hall of Porto Alegre Porto Alegre 90010-170Brazil Ministry of Health and Family Welfare Government of the People’s Republic of BangladeshDhaka 1000Bangladesh Department of Emergency Aid and Disaster Management Trabzon University61335 TrabzonTurkey Department of Emergency Medicine UT SouthwesternDallasTX 75390USA

The COVID-19 pandemic highlighted the urgent need to strengthen public health *** response,the United Nations Disaster Risk Reduction(UNDRR) Public Health System Resilience Scorecard(Scorecard) was applied in workshops across multiple *** aim of our research was to explore the workshop findings to develop priority strategies for strengthening public health system *** conducted a workshop from 14 to 16 March 2023,at the UNDRR Global Education and Training Institute in Incheon,Republic of Korea.A sequential modified Delphi method was utilized to develop a set of prioritized resilience *** were drawn from 70 strategies identified from 13 distinct workshops in eight *** two surveys,23 strategies were *** received ratings of "High" or Very High" from89% of *** related to the inclusion of public health risks in emergency plans,integrating multidisciplinary teams into public health,enabling local transport mechanisms,and improving the ability to manage an influx of *** Scorecard provides an adaptable framework to identify and prioritize strategies for strengthening public health system *** leveraging this methodology,our study demonstrated how resilience strategies could inform disaster risk reduction funding,policies,and actions.

关键词： Health system resilience Public policy Resilience scorecard Workshops

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SEGMENTING WHITE MATTER HYPERINTENSITIES ON ISOTROPIC THREE-DIMENSIONAL FLUID ATTENUATED INVERSION RECOVERY MAGNETIC RESONANCE IMAGES: ASSESSING DEEP LEARNING TOOLS ON A NORWEGIAN IMAGING DATABASE

arXiv

引用

arXiv 2022年

作者： Røvang, Martin Soria Selnes, Per MacIntosh, Bradley John Groote, Inge Rasmus Pålhaugen, Lene Carole, Sudre Fladby, Tormod Bjørnerud, Atle Division of Radiology and Nuclear Medicine Oslo University Hosptial Oslo Norway Division of Medicine and Laboratory Sciences University of Oslo Oslo Norway Department of Neurology Akershus University Hospital Lørenskog Norway Institute of Clinical Medicine University of Oslo Campus Ahus Oslo Norway Department of Medical Biophysics University of Toronto Toronto Canada Sandra Black Centre for Brain Resilience & Recovery Hurvitz Brain Sciences Program Physical Sciences Platform Sunnybrook Research Institute Toronto Canada Department of Radiology Vestfold Hospital Trust Tønsberg Norway School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom Dementia Research Centre Institute of Neurology University College London London United Kingdom Department of Medical Physics University College London London United Kingdom Department of Physics University of Oslo Oslo Norway

An important step in the analysis of magnetic resonance imaging (MRI) data for neuroimaging is the automated segmentation of white matter hyperintensities (WMHs). Fluid Attenuated Inversion Recovery (FLAIR-weighted) is an MRI contrast that is particularly useful to visualize and quantify WMHs, a hallmark of cerebral small vessel disease and Alzheimer’s disease (AD). In order to achieve high spatial resolution in each of the three voxel dimensions, clinical MRI protocols are evolving to a three-dimensional (3D) FLAIR-weighted acquisition. The current study details the deployment of deep learning tools to enable automated WMH segmentation and characterization from 3D FLAIR-weighted images acquired as part of a national AD imaging initiative. Based on data from the ongoing Norwegian Disease Dementia Initiation (DDI) multicenter study, two 3D models—one off-the-shelf from the NVIDIA nnU-Net framework and the other internally developed—were trained, validated, and tested. A third cutting-edge Deep Bayesian network model (HyperMapp3r) was implemented without any de-novo tuning to serve as a comparison architecture. The 2.5D in-house developed and 3D nnU-Net models were trained and validated in-house across five national collection sites among 441 participants from the DDI study, of whom 194 were men and whose average age was (64.91 +/- 9.32) years. Both an external dataset with 29 cases from a global collaborator and a held-out subset of the internal data from the 441 participants were used to test all three models. These test sets were evaluated independently. The ground truth human-in-the-loop segmentation was compared against five established WMH performance metrics. The 3D nnU-Net had the highest performance out of the three tested networks, outperforming both the internally developed 2.5D model and the SOTA Deep Bayesian network with an average dice similarity coefficient score of 0.76 +/- 0.16. Our findings demonstrate that WMH segmentation models can achieve high p

关键词： Bayesian networks

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Cover Feature: Biocompatible Inhibitor Based on Chitosan and Amphiphilic Peptide against Mutant Huntingtin Toxicity (ChemBioChem 16/2019)

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ChemBioChem 2019年第16期20卷 2014-2014页

作者： Devi Wahyuningtyas Dr. Wen-Hao Chen Dr. Chen-Han Huang Yu-Jung He Dr. Joseph Jen-Tse Huang Institute of Chemistry Academia Sinica No. 128 Sec. 2 Academia Road Nankang Taipei 115 Taiwan Sustainable Chemical Science and Technology Program Taiwan International Graduate Program Academia Sinica No. 128 Sec. 2 Academia Road Nankang Taipei 115 Taiwan Department of Applied Chemistry National Chiao Tung University Science Building 2 1001 Ta Hsueh Road Hsinchu 300 Taiwan Department of Biomedical Sciences and Engineering National Central University No. 300 Zhongda Road Zhongli Taoyuan 32001 Taiwan

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Singlet Oxygen Generation in Dark-Hypoxia by Catalytic Microenvironment-Tailored Nanoreactors for NIR-II Fluorescence-Monitored Chemodynamic Therapy

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Angewandte Chemie 2021年第27期133卷

作者： Tao Chen Peidong Hou Yafei Zhang Rujiang Ao Lichao Su Yifan Jiang Yuanli Zhang Huilan Cai Jun Wang Prof. Qiushui Chen Prof. Jibin Song Prof. Lisen Lin Prof. Huanghao Yang Prof. Xiaoyuan Chen MOE Key Laboratory for Analytical Science of Food Safety and Biology College of Chemistry Fuzhou University Fuzhou 350108 China Departments of Diagnostic Radiology Chemical and Biomolecular Engineering and Biomedical Engineering Yong Loo Lin School of Medicine and Faculty of Engineering National University of Singapore Singapore 117597 Singapore Clinical Imaging Research Centre Centre for Translational Medicine Yong Loo Lin School of Medicine National University of Singapore Singapore 117599 Singapore Nanomedicine Translational Research Program NUS Center for Nanomedicine Yong Loo Lin School of Medicine National University of Singapore Singapore 117597 Singapore

Singlet oxygen ( 1 O 2 ) has a potent anticancer effect, but photosensitized generation of 1 O 2 is inhibited by tumor hypoxia and limited light penetration depth. Despite the potential of chemodynamic therapy (CDT) to circumvent these issues by exploration of 1 O 2 -producing catalysts, engineering efficient CDT agents is still a formidable challenge since most catalysts require specific pH to function and become inactivated upon chelation by glutathione (GSH). Herein, we present a catalytic microenvironment-tailored nanoreactor (CMTN), constructed by encapsulating MoO 4 2− catalyst and alkaline sodium carbonate within liposomes, which offers a favorable pH condition for MoO 4 2− -catalyzed generation of 1 O 2 from H 2 O 2 and protects MoO 4 2− from GSH chelation owing to the impermeability of liposomal lipid membrane to ions and GSH. H 2 O 2 and 1 O 2 can freely cross the liposomal membrane, allowing CMTN with a built-in NIR-II ratiometric fluorescent 1 O 2 sensor to achieve monitored tumor CDT.

关键词： catalytic microenvironment chemodynamic therapy nanoreactors NIR-II ratiometric fluorescence singlet oxygen

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Highly efficient capture of E. coli using amidoximated polyacrylonitrile nanofiber membrane immobilized with reactive green 19 dye/polyhexamethylene biguanide: Antibacterial and cytotoxicity studies

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Separation and Purification Technology 2024年 338卷

作者： Quang-Vinh Le Bing-Lan Liu Penjit Srinophakun Jeng-Ywan Shih Chi-Yun Wang Chen‑Yaw Chiu Shen-Long Tsai Kuei-Hsiang Chen Yu-Kaung Chang Department of Chemical Engineering National Taiwan University of Science and Technology Taipei City 10607 Taiwan Department of Applied Chemistry Chaoyang University of Technology Taichung 413310 Taiwan Department of Chemical Engineering Kasetsart University 50 Ngamwongwan Road Chatuchak Bangkok 10900 Thailand Department of Chemical Engineering Ming Chi University of Technology New Taipei City 243303 Taiwan International Ph.D. Program in Innovative Technology of Biomedical Engineering and Medical Devices Ming Chi University of Technology New Taipei City 243303 Taiwan Bone and Joint Research Centre Chang Gung Memorial Hospital Taoyuan City 333423 Taiwan Department of Chemical Engineering and Materials Science Yuan Ze University Zhongli Dist. Taoyuan City 320315 Taiwan

In this study, we synthesized an amidoximated polyacrylonitrile nanofiber membrane (P-Oxime). Subsequently, we grafted reactive green 19 (RG19) dye onto its surface, resulting in the formation of the P-Oxime-RG19 membrane. Further enhancement was achieved by physically attaching poly(hexamethylene biguanide) hydrochloride (PHMB) to the P-Oxime-RG19 membrane, leading to the formation of the P-Oxime-RG19-PHMB membrane. Extensive characterization of the nanofiber membranes was conducted, focusing on crucial physical and mechanical properties such as functional group content, morphology, and thermostability. The optimization of various modification conditions, including initial dye and PHMB concentrations, duration, temperature, and nitrile group conversion to oxime concentrations, played a pivotal role in achieving superior antibacterial performance in P-Oxime-RG19-PHMB nanofiber membranes. In-depth analyses involved the application of kinetic and equilibrium thermodynamic models to unravel the modification processes of RG19 dye and PHMB in the nanofiber membranes. Under the carefully tuned optimal modification conditions, the P-Oxime-RG19-PHMB nanofiber membrane exhibited remarkable antibacterial efficacy, achieving nearly 100% disinfection of E. coli . In addition to exploring its antimicrobial capabilities, we thoroughly examined the repeatability and biocompatibility of the nanofiber membranes. The results indicated not only the P-Oxime-RG19-PHMB nanofiber membrane was a highly promising antibacterial material but also highlighted its potential for diverse applications, particularly in food and textile fabrication, owing to its exceptional performance and suitability.

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Erratum to: Digital Drug Safety Surveillance: Monitoring Pharmaceutical Products in Twitter

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Drug Saf 2014年第7期37卷 555-555页

作者： Clark C. Freifeld John S. Brownstein Christopher M. Menone Wenjie Bao Ross Filice Taha Kass-Hout Nabarun Dasgupta Department of Biomedical Engineering Boston University Boston USA Children’s Hospital Informatics Program Boston Children’s Hospital Boston USA Department of Pediatrics Harvard Medical School Boston USA Center for Biomedical Informatics Harvard Medical School Boston USA Epidemico Inc. Boston USA Georgetown University Medical Center Washington DC USA US Food and Drug Administration (FDA) Silver Spring USA University of North Carolina at Chapel Hill Chapel Hill 27599 USA 66 Newbury Street 2nd Floor Boston MA 02116 USA

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Sensors: Stretchable, Multiplexed pH Sensors With Demonstrations on Rabbit and Human Hearts Undergoing Ischemia (Adv. Healthcare Mater. 1/2014)

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Advanced Healthcare Materials 2014年第1期3卷

作者： Hyun‐Joong Chung Matthew S. Sulkin Jong‐Seon Kim Camille Goudeseune Hsin‐Yun Chao Joseph W. Song Sang Yoon Yang Yung‐Yu Hsu Roozbeh Ghaffari Igor R. Efimov John A. Rogers Department of Chemical and Materials Engineering University of Alberta Edmonton AB T6G 2V4 Canada Department of Materials Science and Engineering University of Illinois at Urbana‐Champaign Urbana IL 61801 USA Department of Biomedical Engineering Washington University in Saint Louis Saint Louis MO USA Department of Chemical and Biomolecular Engineering (BK21 Program) Korea Advanced Institute of Science and Technology Daejeon Korea Illinois Simulator Laboratory Beckman Institute University of Illinois Urbana IL 61801 USA MC10 Inc. 36 Cameron Avenue Cambridge MA 02140 USA Department of Materials Science and Engineering Chemistry Mechanical Science and Engineering Electrical and Computer Engineering Beckman Institute for Advanced Science and Technology and Frederick Seitz Materials Research Laboratory University of Illinois at Urbana‐Champaign Urbana IL 61801 USA

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An Open-Source Knowledge Graph Ecosystem for the Life Sciences

arXiv

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arXiv 2023年

作者： Callahan, Tiffany J. Tripodi, Ignacio J. Stefanski, Adrianne L. Cappelletti, Luca Taneja, Sanya B. Wyrwa, Jordan M. Casiraghi, Elena Matentzoglu, Nicolas A. Reese, Justin Silverstein, Jonathan C. Hoyt, Charles Tapley Boyce, Richard D. Malec, Scott A. Unni, Deepak R. Joachimiak, Marcin P. Robinson, Peter N. Mungall, Christopher J. Cavalleri, Emanuele Fontana, Tommaso Valentini, Giorgio Mesiti, Marco Gillenwater, Lucas A. Santangelo, Brook Vasilevsky, Nicole A. Hoehndorf, Robert Bennett, Tellen D. Ryan, Patrick B. Hripcsak, George Kahn, Michael G. Bada, Michael Baumgartner, William A. Hunter, Lawrence E. Computational Bioscience Program University of Colorado Anschutz Medical Campus AuroraCO80045 United States Department of Biomedical Informatics Columbia University Irving Medical Center New YorkNY10032 United States Computer Science Department Interdisciplinary Quantitative Biology University of Colorado Boulder BoulderCO80301 United States AnacletoLab Computer Science Department University of Milan 20122 Italy Intelligent Systems Program University of Pittsburgh PittsburghPA15260 United States Department of Physical Medicine and Rehabilitation School of Medicine University of Colorado Anschutz Medical Campus AuroraCO80045 United States Division of Environmental Genomics and Systems Biology Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Semanticly Ltd Athens Greece Department of Biomedical Informatics University of Pittsburgh School of Medicine PittsburghPA15206 United States Laboratory of Systems Pharmacology Harvard Medical School BostonMA02115 United States Division of Translational Informatics University of New Mexico School of Medicine AlbuquerqueNM87131 United States SIB Swiss Institute of Bioinformatics Basel Switzerland Berlin Institute of Health at Charité-Universitatsmedizin Berlin10117 Germany ELLIS European Laboratory for Learning and Intelligent Systems Germany Department of Biomedical Informatics University of Colorado School of Medicine AuroraCO80045 United States Data Collaboration Center Critical Path Institute 1840 E River Rd. Suite 100 TucsonAZ85718 United States Computer Electrical and Mathematical Sciences & Engineering Division Computational Bioscience Research Center King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Department of Pediatrics University of Colorado School of Medicine AuroraCO80045 United States Janssen Research and Development RaritanNJ08869 United States Division of General Internal Medicine University of Colorado School of Medicine A

Translational research requires data at multiple scales of biological organization. Advancements in sequencing and multi-omics technologies have increased the availability of these data, but researchers face significant integration challenges. Knowledge graphs (KGs) are used to model complex phenomena, and methods exist to construct them automatically. However, tackling complex biomedical integration problems requires flexibility in the way knowledge is modeled. Moreover, existing KG construction methods provide robust tooling at the cost of fixed or limited choices among knowledge representation models. PheKnowLator (Phenotype Knowledge Translator) is a semantic ecosystem for automating the FAIR (Findable, Accessible, Interoperable, and Reusable) construction of ontologically grounded KGs with fully customizable knowledge representation. The ecosystem includes KG construction resources (e.g., data preparation APIs), analysis tools (e.g., SPARQL endpoints and abstraction algorithms), and benchmarks (e.g., prebuilt KGs and embeddings). We evaluated the ecosystem by systematically comparing it to existing open-source KG construction methods and by analyzing its computational performance when used to construct 12 large-scale KGs. With flexible knowledge representation, PheKnowLator enables fully customizable KGs without compromising performance or usability. © 2023, CC BY.

关键词： Ecosystems

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