Last data update: May 13, 2024. (Total: 46773 publications since 2009)
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Query Trace: Putman A[original query] |
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The Human Phenotype Ontology in 2024: phenotypes around the world
Gargano MA , Matentzoglu N , Coleman B , Addo-Lartey EB , Anagnostopoulos AV , Anderton J , Avillach P , Bagley AM , Bakštein E , Balhoff JP , Baynam G , Bello SM , Berk M , Bertram H , Bishop S , Blau H , Bodenstein DF , Botas P , Boztug K , Čady J , Callahan TJ , Cameron R , Carbon SJ , Castellanos F , Caufield JH , Chan LE , Chute CG , Cruz-Rojo J , Dahan-Oliel N , Davids JR , de Dieuleveult M , de Souza V , de Vries BBA , de Vries E , DePaulo JR , Derfalvi B , Dhombres F , Diaz-Byrd C , Dingemans AJM , Donadille B , Duyzend M , Elfeky R , Essaid S , Fabrizzi C , Fico G , Firth HV , Freudenberg-Hua Y , Fullerton JM , Gabriel DL , Gilmour K , Giordano J , Goes FS , Moses RG , Green I , Griese M , Groza T , Gu W , Guthrie J , Gyori B , Hamosh A , Hanauer M , Hanušová K , He YO , Hegde H , Helbig I , Holasová K , Hoyt CT , Huang S , Hurwitz E , Jacobsen JOB , Jiang X , Joseph L , Keramatian K , King B , Knoflach K , Koolen DA , Kraus ML , Kroll C , Kusters M , Ladewig MS , Lagorce D , Lai MC , Lapunzina P , Laraway B , Lewis-Smith D , Li X , Lucano C , Majd M , Marazita ML , Martinez-Glez V , McHenry TH , McInnis MG , McMurry JA , Mihulová M , Millett CE , Mitchell PB , Moslerová V , Narutomi K , Nematollahi S , Nevado J , Nierenberg AA , Čajbiková NN , Nurnberger JI Jr , Ogishima S , Olson D , Ortiz A , Pachajoa H , Perez de Nanclares G , Peters A , Putman T , Rapp CK , Rath A , Reese J , Rekerle L , Roberts AM , Roy S , Sanders SJ , Schuetz C , Schulte EC , Schulze TG , Schwarz M , Scott K , Seelow D , Seitz B , Shen Y , Similuk MN , Simon ES , Singh B , Smedley D , Smith CL , Smolinsky JT , Sperry S , Stafford E , Stefancsik R , Steinhaus R , Strawbridge R , Sundaramurthi JC , Talapova P , Tenorio Castano JA , Tesner P , Thomas RH , Thurm A , Turnovec M , van Gijn ME , Vasilevsky NA , Vlčková M , Walden A , Wang K , Wapner R , Ware JS , Wiafe AA , Wiafe SA , Wiggins LD , Williams AE , Wu C , Wyrwoll MJ , Xiong H , Yalin N , Yamamoto Y , Yatham LN , Yocum AK , Young AH , Yüksel Z , Zandi PP , Zankl A , Zarante I , Zvolský M , Toro S , Carmody LC , Harris NL , Munoz-Torres MC , Danis D , Mungall CJ , Köhler S , Haendel MA , Robinson PN . Nucleic Acids Res 2023 52 D1333-D1346 The Human Phenotype Ontology (HPO) is a widely used resource that comprehensively organizes and defines the phenotypic features of human disease, enabling computational inference and supporting genomic and phenotypic analyses through semantic similarity and machine learning algorithms. The HPO has widespread applications in clinical diagnostics and translational research, including genomic diagnostics, gene-disease discovery, and cohort analytics. In recent years, groups around the world have developed translations of the HPO from English to other languages, and the HPO browser has been internationalized, allowing users to view HPO term labels and in many cases synonyms and definitions in ten languages in addition to English. Since our last report, a total of 2239 new HPO terms and 49235 new HPO annotations were developed, many in collaboration with external groups in the fields of psychiatry, arthrogryposis, immunology and cardiology. The Medical Action Ontology (MAxO) is a new effort to model treatments and other measures taken for clinical management. Finally, the HPO consortium is contributing to efforts to integrate the HPO and the GA4GH Phenopacket Schema into electronic health records (EHRs) with the goal of more standardized and computable integration of rare disease data in EHRs. |
COVID-19 vaccine uptake among patients with systemic lupus erythematosus in the American Midwest: The Lupus Midwest Network (LUMEN).
Chevet B , Figueroa-Parra G , Yang JX , Hulshizer CA , Gunderson TM , Duong SQ , Putman MS , Barbour KE , Crowson CS , Duarte-Garca A . J Rheumatol 2022 49 (11) 1276-1282 OBJECTIVE: Systemic lupus erythematosus (SLE) patients are at higher risk of poor outcomes from coronavirus disease 2019 (COVID-19). The vaccination rate among such patients is unknown. We aimed to assess COVID-19 vaccine uptake among SLE patients. METHODS: We included 342 SLE patients from the Lupus Midwest Network and 350 age, sex, race, and county matched comparators. Vaccination uptake for influenza, pneumococcal, and zoster vaccines before pandemic restrictions began (up to February 29, 2020) was assessed. First-dose COVID-19 vaccine uptake was electronically retrieved and manually ascertained (December 15, 2020, to July 31, 2021). Time to COVID-19 vaccination, demographics, lupus manifestations, medications, comorbidity index, area deprivation index, and rurality measures were compared. RESULTS: On July 31, 2021, 83.3% of SLE patients and 85.5% of comparators were vaccinated against COVID-19. The COVID-19 vaccination rates were similar among SLE and comparators (hazard ratio: 0.93; 95% CI: 0.79-1.10). Non-vaccinated SLE patients were more likely to be men (27.3% versus 14.1% vaccinated), younger (mean 54.1 versus 58.8 years in vaccinated), have a shorter SLE duration (median 7.3 versus 10.7 years in vaccinated), and be less frequently vaccinated with influenza and pneumococcal vaccine. CONCLUSION: SLE patients in the Lupus Midwest Network had similar COVID-19 vaccination uptake as matched comparators, most of whom were vaccinated early when the vaccine became available. One in six remain unvaccinated. |
Online work force analyzes social media to identify consequences of an unplanned school closure - using technology to prepare for the next pandemic
Rainey JJ , Kenney J , Wilburn B , Putman A , Zheteyeva Y , O'Sullivan M . PLoS One 2016 11 (9) e0163207 BACKGROUND: During an influenza pandemic, the United States Centers for Disease Control and Prevention (CDC) may recommend school closures. These closures could have unintended consequences for students and their families. Publicly available social media could be analyzed to identify the consequences of an unplanned school closure. METHODS: As a proxy for an unplanned, pandemic-related school closure, we used the district-wide school closure due to the September 10-18, 2012 teachers' strike in Chicago, Illinois. We captured social media posts about the school closure using the Radian6 social media-monitoring platform. An online workforce from Amazon Mechanical Turk categorized each post into one of two groups. The first group included relevant posts that described the impact of the closure on students and their families. The second group included irrelevant posts that described the political aspects of the strike or topics unrelated to the school closure. All relevant posts were further categorized as expressing a positive, negative, or neutral sentiment. We analyzed patterns of relevant posts and sentiment over time and compared our findings to household surveys conducted after other unplanned school closures. RESULTS: We captured 4,546 social media posts about the district-wide school closure using our search criteria. Of these, 930 (20%) were categorized as relevant by the online workforce. Of the relevant posts, 619 (67%) expressed a negative sentiment, 51 (5%) expressed a positive sentiment, and 260 (28%) were neutral. The number of relevant posts, and especially those with a negative sentiment, peaked on day 1 of the strike. Negative sentiment expressed concerns about childcare, missed school lunches, and the lack of class time for students. This was consistent with findings from previously conducted household surveys. CONCLUSION: Social media are publicly available and can readily provide information on the impact of an unplanned school closure on students and their families. Using social media to assess the impact of an unplanned school closure due to a public health event would be informative. An online workforce can effectively assist with the review process. |
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