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Accuracy of a new rapid diagnostic test for urinary antigen detection and assessment of drug treatment in opisthorchiasis

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Infectious Diseases of Poverty 2023年第6期12卷 79-90页

作者： Chanika Worasith Jiraporn Sithithaworn Phattharaphon Wongphutorn Chutima Homwong Kanoknan Khongsukwiwat Anchalee Techasen Kulthida Y.Kopolrat Watcharin Loilome Nisana Namwat Bandit Thinkamrop Chaiwat Tawarungruang Attapol Titapun Thewarach Laha Ross H.Andrews Simon D.Taylor‑Robinson Paiboon Sithithaworn Department of Adult Nursing Faculty of NursingKhon Kaen UniversityKhon KaenThailand Cholangiocarcinoma Research Institute Khon Kaen UniversityKhon KaenThailand Faculty of Associated Medical Sciences Khon Kaen UniversityKhon KaenThailand Biomedical Science Program Graduate SchoolKhon Kaen UniversityKhon KaenThailand Faculty of Public Health Kasetsart University Chalermphrakiat Sakon Nakhon Province CampusSakon NakhonThailand Department of Systems Biosciences and Computational Medicine Faculty of MedicineKhon Kaen UniversityKhon KaenThailand Khon Kaen University Phenome Centre Khon Kaen UniversityKhon KaenThailand Department of Epidemiology and Biostatistics Faculty of Public HealthKhon Kaen UniversityKhon KaenThailand Department of Surgery Faculty of MedicineKhon Kaen UniversityKhon KaenThailand Depart‑ment of Parasitology Faculty of MedicineKhon Kaen UniversityKhon KaenThailand Faculty of Medicine St Mary’s CampusImperial College LondonLondonUK Division of Digestive Health Department of Surgery and Can‑cerImperial College LondonLondonUK

Background Screening for opisthorchiasis,a parasitic worm infection affecting many millions of people in Southeast Asia,has traditionally relied on faecal egg examination such as the formalin-ethyl acetate concentration technique(FECT)and Kato-Katz *** the urinary enzyme-linked immunosorbent assay(ELISA)has been used more recently,we developed a urinary antigen-based rapid diagnostic test(RDT)to simplify diagnosis and as a point-of-care testing(POCT)and field applications for surveillance and control of *** A urinaryOpisthorchis viverrini(OV)-RDT was developed using immunochromatographic methodology with a specific monoclonal antibody against *** diagnostic performance of the urinary OV-RDT was compared to that of quantitative faecal FECT and urinary antigen ELISA(n=493).Cross-reactivities of urinary OV-RDT with other helminthiases coexisted *** were determined(n=96).A field trial in the application of urinary OV-RDT was compared with urinary antigen ELISA at baseline screening and assessment of drug treatment outcomes in opisthorchiasis(n=1629).The McNemar chi-square,Kruskal-Wallis and Cohen’s kappa coefficient(κ-value)tests were used for statistical *** Urinary OV-RDT had sensitivity of 94.2%and specificity of 93.2%,compared to faecal *** OV-RDT had high diagnostic agreement(Kappa=0.842-0.874,P<0.001)and quantitative correlation with urinary antigen ELISA(Kruskal-Wallis tests=316.2,P<0.0001)and faecal FECT(Kruskal-Wallis tests=362.3,P<0.0001).The positive rates by OV-RDT,ELISA and FECT were 48.9%,52.5%and 49.3%,***-reactions of urinary OV-RDT with other helminthiases were few(2%).Field trials of urinary OV-RDT yielded comparable prevalence *** between urinary OV-RDT(53.2%)and urinary antigen ELISA(54.0%).OV screening showed high diagnostic agreement(kappa>0.8,P<0.0001)between urinary OV-RDT and urinary antigen *** cure rates of opisthorchiasis at 1 month post-praziq

关键词： Liver fluke Opisthorchis viverrini Urinary antigen detection Urinary Opisthorchis viverrini rapid diagnosis test Enzyme-linked immunosorbent assay Quantitative formalin-ethyl acetate concentration technique

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Revisiting the Transit Timing and Atmosphere Characterization of the Neptune-mass Planet HAT-P-26 b

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Astronomical Journal 2023年第6期166卷 223-223页

作者： A-Thano, Napaporn Awiphan, Supachai Jiang, Ing-Guey Kerins, Eamonn Priyadarshi, Akshay McDonald, Iain Joshi, Yogesh C. Chulikorn, Thansuda Hayes, Joshua J. C. Charles, Stephen Huang, Chung-Kai Rattanamala, Ronnakrit Yeh, Li-Chin Dhillon, Vik S Department of Physics Institute of Astronomy National Tsing-Hua University Hsinchu 30013 Taiwan National Astronomical Research Institute of Thail 260 Moo 4 Donkaew Mae Rim Chiang Mai 50180 Thailand Jodrell Bank Centre for Astrophysics University of Manchester Oxford Road Manchester M13 9PL United Kingdom Department of Physical Sciences The Open University Walton Hall Milton Keynes MK7 6AA United Kingdom Aryabhatta Research Institute of Observational Sciences (ARIES) Manora Peak Nainital 263001 India Department of Physics Faculty of Science Chulalongkorn University 254 Phayathai Road Pathumwan Bangkok 10330 Thailand Institute of Astronomy and Astrophysics Academia Sinica No.1 Sec. 4 Roosevelt Road Taipei 10617 Taiwan PhD Program in Astronomy Department of Physics and Materials Science Faculty of Science Chiang Mai University Chiang Mai 50200 Thailand Department of Physics and General Science Faculty of Science and Technology Nakhon Ratchasima Rajabhat University Nakhon Ratchasima 30000 Thailand Institute of Computational and Modeling Science National Tsing-Hua University Hsinchu 30013 Taiwan Department of Physics and Astronomy University of Sheffield Sheffield S3 7RH United Kingdom Instituto de Astrofísica de Canarias La Laguna Tenerife E-38205 Spain

We present a transit-timing variation (TTV) and planetary atmosphere analysis of the Neptune-mass planet HAT-P-26 b. We present a new set of 13 transit light curves from optical ground-based observations and combine them with light curves from the Wide Field Camera 3 on the Hubble Space Telescope, the Transiting Exoplanet Survey Satellite, and previously published ground-based data. We refine the planetary parameters of HAT-P-26 b and undertake a TTV analysis using 33 transits obtained over seven years. The TTV analysis shows an amplitude signal of 1.98 ± 0.05 minutes, which could result from the presence of an additional ∼0.02 M _Jup planet at a 1:2 mean-motion resonance orbit. Using a combination of transit depths spanning optical to near-infrared wavelengths, we find that the atmosphere of HAT-P-26 b contains 2.4 − 1.6 + 2.9 % H₂O with a derived temperature of 590 − 50 + 60 K. © 2023. The Author(s). Published by the American Astronomical Society.

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Edge states of Floquet-Dirac semimetal in a laser-driven semiconductor quantum-well

arXiv

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arXiv 2020年

作者： Zhang, Boyuan Maeshima, Nobuya Hino, Ken-Ichi Doctoral Program in Materials Science Graduate School of Pure and Applied Sciences University of Tsukuba Tsukuba Ibaraki305-8573 Japan Center for Computational Sciences University of Tsukuba Tsukuba305-8577 Japan Division of Materials Science Faculty of Pure and Applied Sciences University of Tsukuba Tsukuba305-8573 Japan

Band crossings observed in a wide range of condensed matter systems are recognized as a key to understand low-energy fermionic excitations that behave as massless Dirac particles. Despite rapid progress in this field, the exploration of non-equilibrium topological states remains scarce and it has potential ability of providing a new platform to create unexpected massless Dirac states. Here we show that in a cw-laser driven semiconductor quantum-well, the optical Stark effect conducts bulk-band crossing, and the resulting Floquet-Dirac semimetallic phase supports an unconventional edge state in the projected one-dimensional Brillouin zone. Further, we reveal that this edge state mediates a transition between topological and non-topological edge states that is caused by tuning the laser intensity. The existence of the respective edge states and the related topological numbers are understood in a unified manner in terms of the laser-induced polarization reflecting parity hybridization in the bulk Brillouin zone. Copyright © 2020, The Authors. All rights reserved.

关键词： Semiconductor quantum wells

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Protect transgender scientists

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science 2025年第6753期388卷 1283-1284页

作者： Nasa Sinnott-Armstrong Desiree Forsythe Jane M Benoit Callie R. Chappell Lisa S. Y. Coe Bruno Francesco Rodrigues de Oliveira Natalya Evans Anna C. Fagre Jonathan M. Gilligan Maria Hamilton Colin Moran Henneberry Suzanne L. Ishaq Juliet Johnston Erin Krichilsky Jamie Alcira Lopez Kay McMonigal Melanie Ortiz Alvarez de la Campa Rahmeen Rahman Nicole E. Schwartz Lauren Talluto Eric J. Taylor José M. Vargas-Muñiz J. L. Weissman Herbold Computational Biology Program Public Health Sciences Division Fred Hutchinson Cancer Center Seattle WA USA. Department of Genome Sciences University of Washington Seattle WA USA. Department of Biology Santa Clara University Santa Clara CA USA. Department of Biological Science Florida State University Tallahassee FL USA. Department of Biology Stanford University Stanford CA USA. Department of Biology and Genetics Institute University of Florida Gainesville FL USA. School of Teaching and Learning College of Education University of Florida Gainesville FL USA. Department of Microbiology and Parasitology Biomedical Institute Fluminense Federal University Niterói RJ Brazil. Marine Science Institute University of California Santa Barbara Santa Barbara CA USA. Department of Microbiology Immunology and Pathology Colorado State University Fort Collins CO USA. Department of Earth and Environmental Sciences Vanderbilt University Nashville TN USA. Department Civil and Environmental Engineering Vanderbilt University Nashville TN USA. Department of Marine Sciences University of Georgia Athens GA USA. Department of Ecology and Evolution Stony Brook University Stony Brook NY USA. Department of Animal and Veterinary Sciences University of Maine Orono ME USA. Microbes and Social Equity working group Orono ME USA. School of Science and Math Hampshire College Amherst MA USA. American Museum of Natural History Richard Gilder Graduate School New York NY USA. Department of Ecology Evolution and Environmental Biology Columbia University New York NY USA. Department of Bioengineering Stanford University Stanford CA USA. Department of Applied Physics Stanford University Stanford CA USA. College of Fisheries and Ocean Sciences University of Alaska Fairbanks Fairbanks AK USA. Department of Molecular Microbiology and Immunology Brown University Providence RI USA. Department of Infectious Disease Imperial College London London UK. Depart

Transgender and gender nonconforming (TGnC) people are a primary target of the Trump administration. Multiple executive orders seek to erase TGnC protections; mandate denial of gender identity; and ban diversity, equity, and inclusion (DEI) (1). Changes to federal funding policies related to TGnC individuals and DEI threaten to curtail academic diversity and freedom (2). Institutions have been hesitant to resist, but for many TGnC scientists—especially TGnC individuals of color, immigrants, and those with disabilities—invisibility is not an option. Anticipatory obedience (3, 4), and even passive allyship, is insufficient; institutions must abandon neutrality and defend targeted communities to minimize further harm (5, 6). As queer and TGnC scientists, we call on researchers at all levels—from undergraduates to university presidents—to take direct, consistent, and rapid action.

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Using digital traces to build prospective and real-time county-level early warning systems to anticipate COVID-19 outbreaks in the United States

arXiv

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arXiv 2022年

作者： Stolerman, Lucas M. Clemente, Leonardo Poirier, Canelle Parag, Kris V. Majumder, Atreyee Masyn, Serge Resch, Bernd Santillana, Mauricio Computational Health Informatics Program Boston Children’s Hospital BostonMA United States Department of Pediatrics Harvard Medical School BostonMA United States Department of Mathematics Oklahoma State University StillwaterOK United States NIHR Health Protection Research Unit Behavioural Science and Evaluation University of Bristol Bristol United Kingdom Harvard University T.H. Chan School of Public Health BostonMA United States Department of Geoinformatics - Z-GIS University of Salzburg Salzburg Austria Center for Geographic Analysis Harvard University CambridgeMA United States

The ongoing COVID-19 pandemic continues to affect communities around the world. To date, almost 6 million people have died as a consequence of COVID-19, and more than one-quarter of a billion people are estimated to have been infected worldwide. The design of appropriate and timely mitigation strategies to curb the effects of this and future disease outbreaks requires close monitoring of their spatio-temporal trajectories. We present machine learning methods to anticipate sharp increases in COVID-19 activity in US counties in real-time. Our methods leverage Internet-based digital traces – e.g., disease-related Internet search activity from the general population and clinicians, disease-relevant Twitter micro-blogs, and outbreak trajectories from neighboring locations– to monitor potential changes in population-level health trends. Motivated by the need for finer spatial-resolution epidemiological insights to improve local decision-making, we build upon previous retrospective research efforts originally conceived at the state level and in the early months of the pandemic. Our methods –tested in real-time and in an out-of-sample manner on a subset of 97 counties distributed across the US– frequently anticipated sharp increases in COVID-19 activity 1-6 weeks before the onset of local outbreaks (defined as the time when the effective reproduction number Rt becomes larger than 1 consistently). Given the continued emergence of COVID-19 variants of concern –such as the most recent one, Omicron– and the fact that multiple countries have not had full access to vaccines, the framework we present, while conceived for the county-level in the US, could be helpful in countries where similar data sources are available. Copyright © 2022, The Authors. All rights reserved.

关键词： Search engines

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APPROXIMATE BAYESIAN INFERENCE OF DIRECTED ACYCLIC GRAPHS IN BIOLOGY WITH FLEXIBLE PRIORS ON EDGE STATES

arXiv

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arXiv 2019年

作者： Martin, Evan A. Fu, Audrey Qiuyan The Bioinformatics and Computational Biology Program University of Idaho United States The Bioinformatics and Computational Biology Program Department of Mathematics and Statistical Science Institute for Modeling Collaboration and Innovation Institute for Interdisciplinary Data Sciences University of Idaho United States

Graphical models or networks describe the statistical dependence among multiple variables and are widely used in biology (e.g., gene regulatory networks). Under appropriate assumptions, directed edges may represent causal relationships. A key feature of a biological network is sparsity, defined by how likely an edge is present, of which we often have some knowledge. However, most existing Bayesian methods use priors for the entire graph, making it difficult to specify the level of sparsity. The few methods that use priors on edges estimate the two directions independently;the sum of the two probabilities can exceed 1. Here, we present baycn (BAYesian Causal Network), a novel approximate Bayesian method that represents a graph in terms of three states of edges: the two directions and edge absence, and specifies priors on these edge states. We design a pseudo Bayesian sampling algorithm for efficient inference. We apply baycn to two genomic problems: i) distinguishing direct and indirect target genes of genetic variants, using these variants as instrumental variables, and ii) inferring combinatorial binding of highly-correlated transcription factors in Drosophila. In both cases and in extensive simulations, our method demonstrates much improved accuracy over existing methods for the whole graph and for individual edges. Copyright © 2019, The Authors. All rights reserved.

关键词： Inference engines

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Enriching and Characterizing T-Cell Repertoires from 3' Barcoded Single-Cell Whole Transcriptome Amplification Products

arXiv

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arXiv 2022年

作者： Jivanjee, Tasneem Ibrahim, Samira Nyquist, Sarah K. James Gatter, G. Bromley, Joshua D. Jaiswal, Swati Berger, Bonnie Behar, Samuel M. Christopher Love, J. Shalek, Alex K. Institute for Medical Engineering & Science Department of Chemistry Massachusetts Institute of Technology CambridgeMA United States Koch Institute for Integrative Cancer Research Massachusetts Institute of Technology CambridgeMA United States Ragon Institute of MGH MIT and Harvard CambridgeMA United States Broad Institute of MIT and Harvard CambridgeMA United States Massachusetts Institute of Technology CambridgeMA United States Program in Computational and Systems Biology Massachusetts Institute of Technology CambridgeMA United States Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology CambridgeMA United States Microbiology Graduate Program Massachusetts Institute of Technology CambridgeMA02139 United States Department of Microbiology and Physiological Systems University of Massachusetts Medical School WorcesterMA United States Department of Mathematics Massachusetts Institute of Technology CambridgeMA United States Department of Chemical Engineering Massachusetts Institute of Technology CambridgeMA United States

Antigen-specific T cells play an essential role in immunoregulation and many diseases such as cancer. Characterizing the T cell receptor (TCR) sequences that encode T cell specificity is critical for elucidating the antigenic determinants of immunological diseases and designing therapeutic remedies. However, methods of obtaining single-cell TCR sequencing data are labor and cost intensive, typically requiring both cell sorting and full length single-cell RNA-sequencing (scRNA-seq). New high-throughput 3' cell-barcoding scRNA-seq methods can simplify and scale this process;however, they do not routinely capture TCR sequences during library preparation and sequencing. While 5' cell-barcoding scRNA-seq methods can be used to examine TCR repertoire at single-cell resolution, doing so requires specialized reagents which cannot be applied to samples previously processed using 3' cell-barcoding methods. Here, we outline a method for sequencing TCRα and TCRβ transcripts from samples already processed using 3' cell-barcoding scRNA-seq platforms, ensuring TCR recovery at a single-cell resolution. In short, a fraction of the 3' barcoded whole transcriptome amplification (WTA) product typically used to generate a massively parallel 3' scRNA-seq library is enriched for TCR transcripts using biotinylated probes, and further amplified using the same universal primer sequence from WTA. Primer extension using TCR V-region primers and targeted PCR amplification using a second universal primer results in a 3' barcoded single-cell CDR3-enriched library that can be sequenced with custom sequencing primers. Coupled with 3' scRNA-seq of the same WTA, this method enables simultaneous analysis of single-cell transcriptomes and TCR sequences which can help interpret inherent heterogeneity among antigen-specific T cells and salient disease biology. The method presented here can also be adapted readily to enrich and sequence other transcripts of interest from both 3' and 5' barcoded scRNA-seq

关键词： RNA

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When data disappear: public health pays as US policy strays

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The Lancet Digital Health 2025年 100874页

作者： McAndrew, Thomas Lover, Andrew A Hoyt, Garrik Majumder, Maimuna S Department of Biostatistics and Health Data Science College of Health Lehigh University BethlehemPA United States Department of Biostatistics and Epidemiology School of Public Health and Health Sciences University of Massachusetts Amherst AmherstMA United States Department of Computer Science and Engineering PC Rossin College of Engineering and Applied Sciences Lehigh University BethlehemPA United States Department of Pediatrics Harvard Medical School BostonMA United States Computational Health Informatics Program Boston Children's Hospital BostonMA United States

Presidential actions on Jan 20, 2025, by President Donald Trump, including executive orders, have delayed access to or led to the removal of crucial public health data sources in the USA. The continuous collection and maintenance of health data support public health, safety, and security associated with diseases such as seasonal influenza. To show how public health data surveillance enhances public health practice, we analysed data from seven US Government-maintained sources associated with seasonal influenza. We fit two models that forecast the number of national incident influenza hospitalisations in the USA: (1) a data-rich model incorporating data from all seven Government data sources;and (2) a data-poor model built using a single Government hospitalisation data source, representing the minimal required information to produce a forecast of influenza hospitalisations. The data-rich model generated reliable forecasts useful for public health decision making, whereas the predictions using the data-poor model were highly uncertain, rendering them impractical. Thus, health data can serve as a transparent and standardised foundation to improve domestic and global health. Therefore, a plan should be developed to safeguard public health data as a public good. © 2025 The Author(s)

关键词： Health insurance

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Universal mechanical response of metallic glasses during strain-rate-dependent uniaxial compression

arXiv

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arXiv 2022年

作者： Jin, Weiwei Datye, Amit Schwarz, Udo D. Shattuck, Mark D. O'Hern, Corey S. Department of Mechanical Engineering and Materials Science Yale University New HavenCT06520 United States Department of Chemical and Environmental Engineering Yale University New HavenCT06520 United States Benjamin Levich Institute Physics Department The City College of New York New YorkNY10031 United States Department of Physics Yale University New HavenCT06520 United States Department of Applied Physics Yale University New HavenCT06520 United States Graduate Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States

Experimental data on the compressive strength σmax versus strain rate Ε eng for metallic glasses undergoing uniaxial compression shows significantly different behavior for different alloys. For some metallic glasses, σmax decreases with increasing Ε eng, for others, σmax increases with increasing Ε eng, and for others σmax versus Ε eng is nonmonotonic. Using numerical simulations of metallic glasses undergoing uniaxial compression at nonzero strain rate and temperature, we show that they obey a universal relation for the compressive strength versus temperature, which determines their mechanical response. At low Ε eng, increasing strain rate leads to increases in temperature and decreases in σmax∗, whereas at high Ε eng, increasing strain rate leads to decreases in temperature and increases in σmax∗. This non-monotonic behavior of σmax∗ versus temperature causes the nonmonotonic behavior of σmax∗ versus Ε eng. Variations in the internal dissipation change the characteristic strain rate at which the nonmonotonic behavior occurs. These results are general for a wide range of metallic glasses with different atomic interactions, damping coefficients, and chemical compositions. © 2022, CC BY.

关键词： Strain rate

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From Challenges and Pitfalls to Recommendations and Opportunities: Implementing Federated Learning in Healthcare

arXiv

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arXiv 2024年

作者： Li, Ming Xu, Pengcheng Hu, Junjie Tang, Zeyu Yang, Guang Bioengineering Department Imperial-X Imperial College London LondonW12 7SL United Kingdom National Heart and Lung Institute Imperial College London LondonSW7 2AZ United Kingdom Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital BostonMA United States State Key Laboratory of Extreme Photonics and Instrumentation College of Optical Science and Engineering Zhejiang University Hangzhou China Tri-Institutional Computational Biology & Medicine Program Weill Cornell Medicine of Cornell University New York United States Cardiovascular Research Centre Royal Brompton Hospital LondonSW3 6NP United Kingdom School of Biomedical Engineering & Imaging Sciences King’s College London LondonWC2R 2LS United Kingdom

Federated learning holds great potential for enabling large-scale healthcare research and collaboration across multiple centres while ensuring data privacy and security are not compromised. Although numerous recent studies suggest or utilize federated learning based methods in healthcare, it remains unclear which ones have potential clinical utility. This review paper considers and analyzes the most recent studies up to May 2024 that describe federated learning based methods in healthcare. After a thorough review, we find that the vast majority are not appropriate for clinical use due to their methodological flaws and/or underlying biases which include but are not limited to privacy concerns, generalization issues, and communication costs. As a result, the effectiveness of federated learning in healthcare is significantly compromised. To overcome these challenges, we provide recommendations and promising opportunities that might be implemented to resolve these problems and improve the quality of model development in federated learning with healthcare. © 2024, CC BY-NC-ND.

关键词： Federated learning

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