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Derivatives (halogen, nitro and amino) of 8-hydroxyquinoline with highly potent antimicrobial and antioxidant activities

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Biochemistry and Biophysics Reports 2016年 6卷 135-141页

作者： Cherdtrakulkiat, Rungrot Boonpangrak, Somchai Sinthupoom, Nujarin Prachayasittikul, Supaluk Ruchirawat, Somsak Prachayasittikul, Virapong Department of Clinical Microbiology and Applied Technology Faculty of Medical Technology Mahidol University Bangkok 10700 Thailand Center for Innovation Development and Technology Transfer Faculty of Medical Technology Mahidol University Bangkok 10700 Thailand Center of Data Mining and Biomedical Informatics Faculty of Medical Technology Mahidol University Bangkok 10700 Thailand Laboratory of Medicinal Chemistry Chulabhorn Research Institute and Program in Chemical Biology Chulabhorn Graduate Institute Bangkok 10210 Thailand Center of Excellence on Environmental Health and Toxicology Commission on Higher Education (CHE) Ministry of Education Thailand

8-Hydroxyquinoline (8HQ) compounds have been reported to possess diverse bioactivities. In recent years, drug repositioning has gained considerable attention in drug discovery and development. Herein, 8HQ (1) and its derivatives (2-9) bearing various substituents (amino, nitro, cyano and halogen) were investigated for their antimicrobial against 27 microorganisms (agar dilution method) and antioxidant (DPPH method) activities. The parent 8HQ (1) exerted a highly potent antimicrobial activity against Gram-positive bacteria including diploid fungi and yeast with MIC values in the range of 3.44-13.78 μM. Moreover, the halogenated 8HQ, especially 7-bromo-8HQ (4) and clioquinol (6), displayed a high antigrowth activity against Gram-negative bacteria compared with the parent compound (1). Apparently, the derivatives with a relatively high safely index, e.g., nitroxoline (2), exhibited strong antibacterial activity against Aeromonas hydrophila (MIC=5.26 μM) and selectively inhibited the growth of P. aeruginosa with the MIC value of 84.14 μM;cloxyquin (3) showed a strong activity against L. monocytogenes and P. shigelloides with MIC values of 5.57 and 11.14 μM, respectively. Most compounds displayed an antioxidant activity. Specifically, 5-amino-8HQ (8) was shown to be the most potent antioxidant (IC₅₀=8.70 μM) compared with the positive control (α-tocopherol) with IC₅₀ of 13.47 μM. The findings reveal that 8HQ derivatives are potential candidates to be further developed as antimicrobial and antioxidant agents. © 2016 The Authors.

关键词： 8-Hydroxyquinoline Antimicrobial Antioxidant Clioquinol Cloxyquin Nitroxoline

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Developmental exposure of California mice to endocrine disrupting chemicals and potential effects on the microbiome-gut-brain axis at adulthood (vol 10, 10902, 2020)

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SCIENTIFIC REPORTS 2020年第1期10卷 1-17页

作者： Kaur, Sarabjit Sarma, Saurav J. Marshall, Brittney L. Liu, Yang Kinkade, Jessica A. Bellamy, Madison M. Mao, Jiude Helferich, William G. Schenk, A. Katrin Bivens, Nathan J. Lei, Zhentian Sumner, Lloyd W. Bowden, John A. Koelmel, Jeremy P. Joshi, Trupti Rosenfeld, Cheryl S. Christopher S Bond Life Sciences Center University of Missouri Columbia USA Biomedical Sciences University of Missouri Columbia USA MU Metabolomics Center University of Missouri Columbia USA MU Institute of Data Science and Informatics University of Missouri Columbia USA Food Science and Human Nutrition University of Illinois Urbana USA Physics Randolph College Lynchburg USA DNA Core Facility University of Missouri Columbia USA Department of Biochemistry University of Missouri Columbia USA Department of Physiological Sciences College of Veterinary Medicine University of Florida Gainesville USA Center for Environmental and Human Toxicology College of Veterinary Medicine University of Florida Gainesville USA Environmental Health Sciences Yale University New Haven USA Department of Health Management and Informatics School of Medicine University of Missouri Columbia USA Thompson Center for Autism and Neurobehavioral Disorders University of Missouri Columbia USA Genetics Area Program University of Missouri Columbia USA

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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A semi-supervised machine learning approach to detecting recurrent metastatic breast cancer cases using linked cancer registry and electronic medical record data

arXiv

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arXiv 2019年

作者： Ling, Albee Y. Kurian, Allison W. Caswell-Jin, Jennifer L. Sledge, George W. Shah, Nigam H. Tamang, Suzanne R. Stanford University Biomedical Informatics Training Program United States Stanford University School of Medicine Department of Medicine United States Stanford University School of Medicine Department of Health Research and Policy United States Stanford University Department of Biomedical Data Science United States Stanford University Center for Biomedical Informatics Research United States Stanford University Center for Population Health Sciences United States

Purpose: Most cancer data sources lack information on an important outcome: metastatic recurrence. Electronic medical records (EMRs) and population-based cancer registries contain complementary information on cancer outcomes and treatment, yet are rarely used synergistically. To enable detection of metastatic breast cancer (MBC) recurrence, we applied a semi-supervised machine learning framework to linked EMR-California Cancer Registry (CCR) data. Patients and Methods: We studied 11,459 female patients who received an incident breast cancer diagnosis from 2000-2014 and were treated at Stanford health Care. The dataset consisted of structured data and unstructured free-text clinical notes from each patient’s EMR, linked to the population-based CCR, a component of the Surveillance, Epidemiology and End Results (SEER) database. We extracted information on metastatic disease from patient notes to infer a class label and then trained a logistic regression model with regularization for metastatic recurrence classification. We evaluated model performance on an oncologist-labeled set of 146 patients. Results: Among 11,459 patients studied, 495 (4.3%) had de novo stage IV MBC. Of the remaining 10,964 patients with Stage 0-III disease, 1,374 (12.5%) were classified as having recurrent MBC and 9,590 (87.5%) were classified as not having MBC. The median follow-up time is 96.3 months (mean 97.8, standard deviation 46.7). The best-performing model incorporated natural language processing of EMR-derived features with CCR-derived features and had an area under the receiver-operating characteristic curve=0.925 [95% confidence interval: 0.880-0.969], sensitivity=0.861, specificity=0.878 and overall accuracy=0.870. Conclusion: A framework for MBC case detection combining EMR and SEER registry data achieved good sensitivity, specificity and discrimination without requiring expert-labeled examples. This approach enables population-based research on how patients die from cancer and may i

关键词： Natural language processing systems

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Application for Airport Tenant Selection Using Weighted Product-based Decision Support System

Application for Airport Tenant Selection Using Weighted Prod...

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Electrical and Information Technology (IEIT), International Conference on

作者： Arwin Datumaya Wahyudi Sumari Muhamad Andika Saputra Astika Ayuningtyas Catur Sudarmadi Aerospace Intelligent System and Data Engineering Research Group Adisutjipto Institute of Aerospace Technology Yogyakarta Indonesia Cognitive Artificial Intelligence Research Group Politeknik Negeri Malang Malang Indonesia Informatics Study Program Faculty of Industrial Technology Adisutjipto Institute of Aerospace Technology Yogyakarta Indonesia Aerospace Health Informatics Research Group Faculty of Industrial Technology Adisutjipto Institute of Aerospace Technology Yogyakarta Indonesia

This research aims to develop an application for selecting the best new tenant at PT Angkasa Pura II Sultan Thaha Jambi Airport Branch by utilizing Weighted Product (WP) method as the decision support system. Our approach is descriptive research with data analysis technique using four prospective tenants coded A_1 to A_4. The data was collected through observation, interviews, documentation, and literature study. The steps for selecting the best new tenant using the WP method include determining the criteria, sorting the suitability of alternatives and criteria, determining the weight of each criterion and its normalization, calculating the score, S value as an alternative preference, and calculating the ranking, V value as the final alternative preference. Based on the final calculation results, the new tenant coded A_4 is the best alternative based on the data collected from the Financial Management (FHFM) Unit and the Commercial Unit, with a value of V_4 is 0.294.

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The EMPOWER blended digital intervention for relapse prevention in schizophrenia: a feasibility cluster randomised controlled trial in Scotland and Australia

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The Lancet Psychiatry 2022年第6期9卷 477-486页

作者： Gumley, Andrew I Bradstreet, Simon Ainsworth, John Allan, Stephanie Alvarez-Jimenez, Mario Aucott, Lorna Birchwood, Maximillian Briggs, Andrew Bucci, Sandra Cotton, Sue M Engel, Lidia French, Paul Lederman, Reeva Lewis, Shôn Machin, Matthew MacLennan, Graeme McLeod, Hamish McMeekin, Nicola Mihalopoulos, Cathy Morton, Emma Norrie, John Schwannauer, Matthias Singh, Swaran P Sundram, Suresh Thompson, Andrew Williams, Chris Yung, Alison R Farhall, John Gleeson, John Glasgow Institute of Health and Wellbeing University of Glasgow Glasgow United Kingdom Division of Psychology and Mental Health School of Health Sciences Faculty of Biology Medicine and Health Manchester Academic Health Science Centre University of Manchester Manchester United Kingdom Division of Informatics Imaging and Data Sciences School of Health Sciences Faculty of Biology Medicine and Health Manchester Academic Health Science Centre University of Manchester Manchester United Kingdom Orygen Melbourne Melbourne VIC Australia Centre for Youth Mental Health University of Melbourne Melbourne VIC Australia School of Computing and Information Systems Faculty of Engineering and Information Technology University of Melbourne Melbourne VIC Australia Centre for Healthcare Randomised Trials (CHaRT) University of Aberdeen Aberdeen United Kingdom Centre for Mental Health and Wellbeing Research Warwick Medical School University of Warwick Warwick United Kingdom Department of Health Services Research and Policy London School of Hygiene & Tropical Medicine London United Kingdom Greater Manchester Mental Health NHS Foundation Trust Manchester United Kingdom School of Public Health and Preventive Medicine Monash University Melbourne VIC Australia Department of Psychiatry School of Clinical Sciences Monash University Melbourne VIC Australia School of Medicine Deakin University Melbourne VIC Australia Department of Psychiatry Manchester Metropolitan University Manchester United Kingdom Department of Psychiatry University of British Columbia Vancouver BC Canada The Usher Institute University of Edinburgh Edinburgh United Kingdom School for Health in Social Sciences University of Edinburgh Edinburgh United Kingdom Mental Health Program Monash Health Melbourne VIC Australia Department of Psychology and Counselling La Trobe University Melbourne VIC Australia NorthWestern Mental Health The Royal Melbourne Hospital Epping VIC Australia Healthy Brain and Mi

Background: Early warning signs monitoring by service users with schizophrenia has shown promise in preventing relapse but the quality of evidence is low. We aimed to establish the feasibility of undertaking a definitive randomised controlled trial to determine the effectiveness of a blended digital intervention for relapse prevention in schizophrenia. Methods: This multicentre, feasibility, cluster randomised controlled trial aimed to compare Early signs Monitoring to Prevent relapse in psychosis and prOmote Well-being, Engagement, and Recovery (EMPOWER) with treatment as usual in community mental health services (CMHS) in Glasgow and Melbourne. CMHS were the unit of randomisation, selected on the basis of those that probably had five or more care coordinators willing to participate. Participants were eligible if they were older than 16 years, had a schizophrenia or related diagnosis confirmed via case records, were able to provide informed consent, had contact with CMHS, and had had a relapse within the previous 2 years. Participants were randomised within stratified clusters to EMPOWER or to continue their usual approach to care. EMPOWER blended a smartphone for active monitoring of early warning signs with peer support to promote self-management and clinical triage to promote access to relapse prevention. Main outcomes were feasibility, acceptability, usability, and safety, which was assessed through face-to-face interviews. App usage was assessed via the smartphone and self-report. Primary end point was 12 months. Participants, research assistants and other team members involved in delivering the intervention were not masked to treatment conditions. Assessment of relapse was done by an independent adjudication panel masked to randomisation group. The study is registered at ISRCTN (99559262). Findings: We identified and randomised eight CMHS (six in Glasgow and two in Melbourne) comprising 47 care coordinators. We recruited 86 service users between Jan 19 and Au

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Scalable Numerical Embeddings for Multivariate Time Series: Enhancing healthcare data Representation Learning

arXiv

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arXiv 2024年

作者： Huang, Chun-Kai Hsieh, Yi-Hsien Chien, Ta-Jung Chien, Li-Cheng Sun, Shao-Hua Su, Tung-Hung Kao, Jia-Horng Lin, Che Taiwan Data Science Degree Program NTU Taiwan Department of Electrical Engineering NTU Taiwan Division of Gastroenterology and Hepatology NTU Hospital Taiwan Hepatitis Research Center NTU Hospital Taiwan Graduate Institute of Clinical Medicine College of Medicine NTU Taiwan Center for Advanced Computing and Imaging in Biomedicine NTU Taiwan Smart Medicine and Health Informatics Program NTU Taiwan

Multivariate time series (MTS) data, when sampled irregularly and asynchronously, often present extensive missing values. Conventional methodologies for MTS analysis tend to rely on temporal embeddings based on timestamps that necessitate subsequent imputations, yet these imputed values frequently deviate substantially from their actual counterparts, thereby compromising prediction accuracy. Furthermore, these methods typically fail to provide robust initial embeddings for values infrequently observed or even absent within the training set, posing significant challenges to model generalizability. In response to these challenges, we propose SCAlable Numerical Embedding (SCANE), a novel framework that treats each feature value as an independent token, effectively bypassing the need for imputation. SCANE regularizes the traits of distinct feature embeddings and enhances representational learning through a scalable embedding mechanism. Coupling SCANE with the Transformer Encoder architecture, we develop the Scalable nUMerical eMbeddIng Transformer (SUMMIT), which is engineered to deliver precise predictive outputs for MTS characterized by prevalent missing entries. Our experimental validation, conducted across three disparate electronic health record (EHR) datasets marked by elevated missing value frequencies, confirms the superior performance of SUMMIT over contemporary state-of-the-art approaches addressing similar challenges. These results substantiate the efficacy of SCANE and SUMMIT, underscoring their potential applicability across a broad spectrum of MTS data analytical tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Embeddings

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data gaps and opportunities for modeling cancer health equity

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Journal of the National Cancer Institute - Monographs 2023年第62期2023卷 246-254页

作者： Trentham-Dietz, Amy Corley, Douglas A. Del Vecchio, Natalie J. Greenlee, Robert T. Haas, Jennifer S. Hubbard, Rebecca A. Hughes, Amy E. Kim, Jane J. Kobrin, Sarah Li, Christopher I. Meza, Rafael Neslund-Dudas, Christine M. Tiro, Jasmin A. Department of Population Health Sciences and Carbone Cancer Center School of Medicine and Public Health University of Wisconsin-Madison Madison WI United States Division of Research Kaiser Permanente Northern California Oakland CA United States Division of Public Health Sciences Fred Hutchinson Cancer Research Center Seattle WA United States Marshfield Clinic Research Institute Marshfield WI United States Division of General Internal Medicine Massachusetts General Hospital Boston MA United States Department of Biostatistics Epidemiology and Informatics Perelman School of Medicine University of Pennsylvania Philadelphia PA United States Department of Population and Data Sciences University of Texas Southwestern Medical Center Dallas TX United States Department of Health Policy and Management Center for Health Decision Science Harvard T.H. Chan School of Public Health Boston MA United States Healthcare Delivery Research Program Division of Cancer Control & Population Sciences National Cancer Institute National Institutes of Health Rockville MD United States Department of Integrative Oncology British Columbia (BC) Cancer Research Institute Vancouver BC Canada Department of Public Health Sciences and Henry Ford Cancer Henry Ford Health Detroit MI United States Department of Public Health Sciences University of Chicago Biological Sciences Division University of Chicago Medicine Comprehensive Cancer Center Chicago IL United States

Population models of cancer reflect the overall US population by drawing on numerous existing data resources for parameter inputs and calibration targets. Models require data inputs that are appropriately representative, collected in a harmonized manner, have minimal missing or inaccurate values, and reflect adequate sample sizes. data resource priorities for population modeling to support cancer health equity include increasing the availability of data that 1) arise from uninsured and underinsured individuals and those traditionally not included in health-care delivery studies, 2) reflect relevant exposures for groups historically and intentionally excluded across the full cancer control continuum, 3) disaggregate categories (race, ethnicity, socioeconomic status, gender, sexual orientation, etc.) and their intersections that conceal important variation in health outcomes, 4) identify specific populations of interest in clinical databases whose health outcomes have been understudied, 5) enhance health records through expanded data elements and linkage with other data types (eg, patient surveys, provider and/or facility level information, neighborhood data), 6) decrease missing and misclassified data from historically underrecognized populations, and 7) capture potential measures or effects of systemic racism and corresponding intervenable targets for change. © The Author(s) 2023. Published by Oxford University Press. All rights reserved. For permissions, please email: ***@***.

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Can OpenEHR, ISO 13606 and HL7 FHIR work together? An agnostic perspective for the selection and application of EHR standards from Spain

TechRxiv

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TechRxiv 2022年

作者： Pedrera-Jiménez, Miguel Kalra, Dipak Beale, Thomas Carrero, Adolfo Muñoz Balazote, Pablo Serrano Barrio, Noelia García Cano, David Moner Frid, Santiago Tomás, Diego Boscá Segura, Jose Alberto Maldonado Rubí, Raimundo Lozano Data Science Unit Hospital Universitario 12 de Octubre Av. de Córdoba s/n Madrid28041 Spain Data Science Unit Hospital Universitario 12 de Octubre Madrid Spain Veratech for Health Valencia Spain Unit of Medical Informatics Hospital Clinic de Barcelona Barcelona Spain Ars Semantica UK. Specifications Program OpenEHR International United Kingdom Madrid Spain

Due to the heterogeneity of Electronic health Record (EHR) standards, the decision-making teams, who are not experts in health information, express confusion for selecting and applying these resources in their data platforms. For this reason, a group of experts has analyzed strengths and weaknesses about design, modeling capabilities, flexibility and resources implemented of three relevant standards based on Detailed Clinical Models: OpenEHR, ISO 13606 and HL7 FHIR. Thus, it was concluded that: (1) they are useful for the purposes for which they have been designed and show shortcomings in those for which they have not;(2) they are functionally compatible in health data platforms and methodologies developed in a standards-agnostic perspective;and (3) they are conceptually and technically compatible with each other, so the choice of one or the other does not have a high impact as long as one starts from the one richer in modeling capabilities and flexibility. © 2022, CC BY-NC-SA.

关键词： Decision making

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Mobile health Monitoring of Cardiac Status

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Biomedical data Science 1000年第1期3卷 243-263页

作者： Jeffrey W. Christle Steven G. Hershman Jessica Torres Soto Euan A. Ashley 2Stanford Center for Inherited Cardiovascular Disease Stanford University Stanford California 94305 USA 1Division of Cardiovascular Medicine Department of Medicine Stanford University Stanford California 94305 USA email: christle@stanford.edu 3Biomedical Informatics Program Department of Biomedical Data Science Stanford University Stanford California 94305 USA 4Stanford Center for Digital Health Stanford University Stanford California 94305 USA

Cardiovascular diseases (CVDs) are responsible for more deaths than any other cause, with coronary heart disease and stroke accounting for two-thirds of those deaths. Morbidity and mortality due to CVD are largely preventable, through either primary prevention of disease or secondary prevention of cardiac events. Monitoring cardiac status in healthy and diseased cardiovascular systems has the potential to dramatically reduce cardiac illness and injury. Smart technology in concert with mobile health platforms is creating an environment where timely prevention of and response to cardiac events are becoming a reality.

关键词： cardiovascular disease mhealth wearables arrhythmia LVAD physical activity

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The Genomics Research and Innovation Network: creating an interoperable, federated, genomics learning system (vol 74, pg 879, 2020)

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GENETICS IN MEDICINE 2020年第2期22卷 449-449页

作者： Mandl, Kenneth D. Glauser, Tracy Krantz, Ian D. Avillach, Paul Bartels, Anna Beggs, Alan H. Biswas, Sawona Bourgeois, Florence T. Corsmo, Jeremy Dauber, Andrew Devkota, Batsal Fleisher, Gary R. Heath, Allison P. Helbig, Ingo Hirschhorn, Joel N. Kilbourn, Judson Kong, Sek Won Kornetsky, Susan Majzoub, Joseph A. Marsolo, Keith Martin, Lisa J. Nix, Jeremy Schwarzhoff, Amy Stedman, Jason Strauss, Arnold Sund, Kristen L. Taylor, Deanne M. White, Peter S. Marsh, Eric Grimberg, Adda Hawkes, Colin Computational Health Informatics Program Boston Children’s Hospital Boston MA USA Department of Biomedical Informatics Harvard Medical School Boston MA USA Department of Pediatrics Harvard Medical School Boston MA USA Division of Neurology Cincinnati Children’s Hospital Medical Center Cincinnati OH USA Department of Pediatrics University of Cincinnati College of Medicine Cincinnati OH USA Division of Human Genetics at the Children’s Hospital of Philadelphia Philadelphia PA USA Department of Pediatrics Perelman School of Medicine at the University of Pennsylvania Philadelphia PA USA Cincinnati Children’s Hospital Medical Center Cincinnati OH USA The Manton Center for Orphan Disease Research Division of Genetics and Genomics Boston Children’s Hospital Boston MA USA Department of Pediatrics Boston Children’s Hospital Boston MA USA Office of Research Compliance and Regulatory Affairs Cincinnati Children’s Hospital Medical Center Cincinnati OH USA Division of Endocrinology Children’s National Health System Washington DC USA Center for Data-Driven Discovery in Biomedicine The Children’s Hospital of Philadelphia Philadelphia PA USA Department of Biomedical and Health Informatics The Children’s Hospital of Philadelphia Philadelphia PA USA Division of Neurology Children’s Hospital of Philadelphia Philadelphia PA USA Division of Endocrinology Boston Children’s Hospital Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA Research Administration Boston Children’s Hospital Boston MA USA Department of Population Health Sciences Duke University School of Medicine Durham NC USA Division of Human Genetics Cincinnati Children’s Hospital Medical Center Cincinnati OH USA Division of Biomedical Informatics Cincinnati Children’s Hospital Medical Center Cincinnati OH USA Department of Biomedical Informatics University of Cincinnati College of Medicine Cincinnati OH USA Children’s Hospital of Philadelphia Philadelphia PA USA Division o

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

关键词： genomic medicine federated networks electronic health records biobanking information technology

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