Browsing by Author "Blau, Hannah"

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    Diagnosis of Cystic Fibrosis in Screened Populations
    (Elsevier, 2017-02) Farrell, Philip M.; White, Terry B.; Howenstine, Michelle S.; Munck, Anne; Parad, Richard B.; Rosenfeld, Margaret; Sommerburg, Olaf; Accurso, Frank J.; Davies, Jane C.; Rock, Michael J.; Sanders, Don B.; Wilschanski, Michael; Sermet-Gaudelus, Isabelle; Blau, Hannah; Gartner, Silvia; McColley, Susanna A.; Pediatrics, School of Medicine
    Objective Cystic fibrosis (CF) can be difficult to diagnose, even when newborn screening (NBS) tests yield positive results. This challenge is exacerbated by the multitude of NBS protocols, misunderstandings about screening vs diagnostic tests, and the lack of guidelines for presumptive diagnoses. There is also confusion regarding the designation of age at diagnosis. Study design To improve diagnosis and achieve standardization in definitions worldwide, the CF Foundation convened a committee of 32 experts with a mission to develop clear and actionable consensus guidelines on diagnosis of CF with an emphasis on screened populations, especially the newborn population. A comprehensive literature review was performed with emphasis on relevant articles published during the past decade. Results After reviewing the common screening protocols and outcome scenarios, 14 of 27 consensus statements were drafted that apply to screened populations. These were approved by 80% or more of the participants. Conclusions It is recommended that all diagnoses be established by demonstrating dysfunction of the CF transmembrane conductance regulator (CFTR) channel, initially with a sweat chloride test and, when needed, potentially with newer methods assessing membrane transport directly, such as intestinal current measurements. Even in babies with 2 CF-causing mutations detected via NBS, diagnosis must be confirmed by demonstrating CFTR dysfunction. The committee also recommends that the latest classifications identified in the Clinical and Functional Translation of CFTR project [http://www.cftr2.org/index.php] should be used to aid with CF diagnosis. Finally, to avoid delays in treatment, we provide guidelines for presumptive diagnoses and recommend how to determine the age of diagnosis.
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    The Human Phenotype Ontology in 2024: phenotypes around the world
    (Oxford University Press, 2024) Gargano, Michael A.; Matentzoglu, Nicolas; Coleman, Ben; Addo-Lartey, Eunice B.; Anagnostopoulos, Anna V.; Anderton, Joel; Avillach, Paul; Bagley, Anita M.; Bakštein, Eduard; Balhoff, James P.; Baynam, Gareth; Bello, Susan M.; Berk, Michael; Bertram, Holli; Bishop, Somer; Blau, Hannah; Bodenstein, David F.; Botas, Pablo; Boztug, Kaan; Čady, Jolana; Callahan, Tiffany J.; Cameron, Rhiannon; Carbon, Seth J.; Castellanos, Francisco; Caufield, J. Harry; Chan, Lauren E.; Chute, Christopher G.; Cruz-Rojo, Jaime; Dahan-Oliel, Noémi; Davids, Jon R.; de Dieuleveult, Maud; de Souza, Vinicius; de Vries, Bert B. A.; de Vries, Esther; DePaulo, J. Raymond; Derfalvi, Beata; Dhombres, Ferdinand; Diaz-Byrd, Claudia; Dingemans, Alexander J. M.; Donadille, Bruno; Duyzend, Michael; Elfeky, Reem; Essaid, Shahim; Fabrizzi, Carolina; Fico, Giovanna; Firth, Helen V.; Freudenberg-Hua, Yun; Fullerton, Janice M.; Gabriel, Davera L.; Gilmour, Kimberly; Giordano, Jessica; Goes, Fernando S.; Gore Moses, Rachel; Green, Ian; Griese, Matthias; Groza, Tudor; Gu, Weihong; Guthrie, Julia; Gyori, Benjamin; Hamosh, Ada; Hanauer, Marc; Hanušová, Kateřina; He, Yongqun Oliver; Hegde, Harshad; Helbig, Ingo; Holasová, Kateřina; Hoyt, Charles Tapley; Huang, Shangzhi; Hurwitz, Eric; Jacobsen, Julius O. B.; Jiang, Xiaofeng; Joseph, Lisa; Keramatian, Kamyar; King, Bryan; Knoflach, Katrin; Koolen, David A.; Kraus, Megan L.; Kroll, Carlo; Kusters, Maaike; Ladewig, Markus S.; Lagorce, David; Lai, Meng-Chuan; Lapunzina, Pablo; Laraway, Bryan; Lewis-Smith, David; Li, Xiarong; Lucano, Caterina; Majd, Marzieh; Marazita, Mary L.; Martinez-Glez, Victor; McHenry, Toby H.; McInnis, Melvin G.; McMurry, Julie A.; Mihulová, Michaela; Millett, Caitlin E.; Mitchell, Philip B.; Moslerová, Veronika; Narutomi, Kenji; Nematollahi, Shahrzad; Nevado, Julian; Nierenberg, Andrew A.; Novák Čajbiková, Nikola; Nurnberger, John I., Jr.; Ogishima, Soichi; Olson, Daniel; Ortiz, Abigail; Pachajoa, Harry; Perez de Nanclares, Guiomar; Peters, Amy; Putman, Tim; Rapp, Christina K.; Rath, Ana; Reese, Justin; Rekerle, Lauren; Roberts, Angharad M.; Roy, Suzy; Sanders, Stephan J.; Schuetz, Catharina; Schulte, Eva C.; Schulze, Thomas G.; Schwarz, Martin; Scott, Katie; Seelow, Dominik; Seitz, Berthold; Shen, Yiping; Similuk, Morgan N.; Simon, Eric S.; Singh, Balwinder; Smedley, Damian; Smith, Cynthia L.; Smolinsky, Jake T.; Sperry, Sarah; Stafford, Elizabeth; Stefancsik, Ray; Steinhaus, Robin; Strawbridge, Rebecca; Sundaramurthi, Jagadish Chandrabose; Talapova, Polina; Tenorio Castano, Jair A.; Tesner, Pavel; Thomas, Rhys H.; Thurm, Audrey; Turnovec, Marek; van Gijn, Marielle E.; Vasilevsky, Nicole A.; Vlčková, Markéta; Walden, Anita; Wang, Kai; Wapner, Ron; Ware, James S.; Wiafe, Addo A.; Wiafe, Samuel A.; Wiggins, Lisa D.; Williams, Andrew E.; Wu, Chen; Wyrwoll, Margot J.; Xiong, Hui; Yalin, Nefize; Yamamoto, Yasunori; Yatham, Lakshmi N.; Yocum, Anastasia K.; Young, Allan H.; Yüksel, Zafer; Zandi, Peter P.; Zankl, Andreas; Zarante, Ignacio; Zvolský, Miroslav; Toro, Sabrina; Carmody, Leigh C.; Harris, Nomi L.; Munoz-Torres, Monica C.; Danis, Daniel; Mungall, Christopher J.; Köhler, Sebastian; Haendel, Melissa A.; Robinson, Peter N.; Psychiatry, School of Medicine
    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.