Zobrazeno 1 - 10
of 76
pro vyhledávání: '"K. Farahat"'
Autor:
Michael R. Emami, Mark A. Brimble, Alejandro Espinoza, Jane Owens, Laurence O. Whiteley, Sandra Casinghino, Thomas A. Lanz, Philip K. Farahat, Matteo Pellegrini, Courtney S. Young, Paul G. Thomas, Elizabeth M. McNally, S. Armando Villalta, Stefan A. Schattgen, Melissa J. Spencer
Publikováno v:
Molecular Therapy: Methods & Clinical Development, Vol 32, Iss 4, Pp 101349- (2024)
Clinical trials for Duchenne muscular dystrophy (DMD) are assessing the therapeutic efficacy of systemically delivered adeno-associated virus (AAV) carrying a modified DMD transgene. High vector doses (>1E14 vg/kg) are needed to globally transduce sk
Externí odkaz:
https://doaj.org/article/fee20f3ef621479e9e7090e49aade946
Autor:
Michael R. Emami, Alejandro Espinoza, Courtney S. Young, Feiyang Ma, Philip K. Farahat, Philip L. Felgner, Jeffrey S. Chamberlain, Xiangmin Xu, April D. Pyle, Matteo Pellegrini, S. Armando Villalta, Melissa J. Spencer
Publikováno v:
Molecular Therapy: Methods & Clinical Development, Vol 30, Iss , Pp 90-102 (2023)
High systemic doses of adeno-associated viruses (AAVs) have been associated with immune-related serious adverse events (SAEs). Although AAV was well tolerated in preclinical models, SAEs were observed in clinical trials, indicating the need for impro
Externí odkaz:
https://doaj.org/article/5f866c643097449da902b95f356bd37b
Autor:
Jenna M. Kastenschmidt, Gerald Coulis, Philip K. Farahat, Phillip Pham, Rodolfo Rios, Therese T. Cristal, Ali H. Mannaa, Rachel E. Ayer, Rayan Yahia, Archis A. Deshpande, Brandon S. Hughes, Adam K. Savage, Carlee R. Giesige, Scott Q. Harper, Richard M. Locksley, Tahseen Mozaffar, S. Armando Villalta
Publikováno v:
Cell Reports, Vol 35, Iss 2, Pp 108997- (2021)
Summary: Despite the well-accepted view that chronic inflammation contributes to the pathogenesis of Duchenne muscular dystrophy (DMD), the function and regulation of eosinophils remain an unclear facet of type II innate immunity in dystrophic muscle
Externí odkaz:
https://doaj.org/article/415bf515be5348feade1d4581741b4f9
Autor:
Gerald Coulis, Diego Jaime, Christian Guerrero-Juarez, Jenna M. Kastenschmidt, Philip K. Farahat, Quy Nguyen, Nicholas Pervolarakis, Katherine McLinden, Lauren Thurlow, Saba Movahedi, Jorge Duarte, Andrew Sorn, Elizabeth Montoya, Izza Mozaffar, Morgan Dragan, Shivashankar Othy, Trupti Joshi, Chetan P. Hans, Virginia Kimonis, Adam L. MacLean, Qing Nie, Lindsay M. Wallace, Scott Q. Harper, Tahseen Mozaffar, Marshall W. Hogarth, Surajit Bhattacharya, Jyoti K. Jaiswal, David R. Golann, Qi Su, Kai Kessenbrock, Michael Stec, Melissa J. Spencer, Jesse R. Zamudio, S. Armando Villalta
Publikováno v:
bioRxiv
The monocytic/macrophage system is essential for skeletal muscle homeostasis, but its dysregulation contributes to the pathogenesis of muscle degenerative disorders. Despite our increasing knowledge of the role of macrophages in degenerative disease,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4e5353e2ada6cb20a8b3fd8cc1d6f1fe
https://doi.org/10.1101/2023.04.18.537253
https://doi.org/10.1101/2023.04.18.537253
Autor:
Namita A. Goyal, Gérald Coulis, Jorge Duarte, Philip K. Farahat, Ali H. Mannaa, Jonathan Cauchii, Tyler Irani, Nadia Araujo, Leo Wang, Marie Wencel, Vivian Li, Lishi Zhang, Steven A. Greenberg, Tahseen Mozaffar, S. Armando Villalta
Publikováno v:
Neurology, vol 98, iss 13
Background and ObjectivesTo evaluate the therapeutic potential of targeting highly differentiated T cells in patients with inclusion body myositis (IBM) by establishing high-resolution mapping of killer cell lectin-like receptor subfamily G member 1
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a6435547538b4a8c2596204dc5bda4c2
https://escholarship.org/uc/item/7px358zf
https://escholarship.org/uc/item/7px358zf
Publikováno v:
Egyptian Journal of Pediatrics. 36:419-431
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::62f08f4b8d2d341a8e49210c78387122
https://doi.org/10.12816/0054712
https://doi.org/10.12816/0054712
Autor:
Therese T. Cristal, Rodolfo Rios, Gerald Coulis, Adam K. Savage, Tahseen Mozaffar, Brandon S. Hughes, Scott Q. Harper, Jenna M. Kastenschmidt, Rachel E. Ayer, Archis A. Deshpande, S. Armando Villalta, Ali H. Mannaa, Richard M. Locksley, Rayan Yahia, Philip K. Farahat, Carlee R. Giesige, Phillip Pham
Publikováno v:
Cell reports, vol 35, iss 2
Cell reports
Cell Reports, Vol 35, Iss 2, Pp 108997-(2021)
Cell reports
Cell Reports, Vol 35, Iss 2, Pp 108997-(2021)
SUMMARY Despite the well-accepted view that chronic inflammation contributes to the pathogenesis of Duchenne muscular dystrophy (DMD), the function and regulation of eosinophils remain an unclear facet of type II innate immunity in dystrophic muscle.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::93634d2ee96482c107503c688e1e7242
https://escholarship.org/uc/item/57k699x2
https://escholarship.org/uc/item/57k699x2
Time series data have grown at an explosive rate in numerous domains and have stimulated a surge of time series modeling research. A comprehensive comparison of different time series models, for a considered data analytics task, provides useful guida
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c3a0c96941ddb5ddec81fa9e9cac3208
Autor:
Chetan Gupta, Ahmed K. Farahat
Publikováno v:
KDD
Industrial AI is concerned with the application of Artificial Intelligence (AI), Machine Learning (ML) and related technologies towards addressing real-world use cases in industrial and societal domains. These uses cases can be broadly categorized in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e2f13634d3e500544f4b54b7f6295fe7
https://doi.org/10.1145/3394486.3406482
https://doi.org/10.1145/3394486.3406482
Publikováno v:
ICPHM
Prognostics is concerned with predicting the future health of the equipment and any potential failures. With the advances in the Internet of Things (IoT), data-driven approaches for prognostics that leverage the power of machine learning models are g
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::540cf7ea5457d12f2f4bdd8cf21b106c
http://arxiv.org/abs/2006.03729
http://arxiv.org/abs/2006.03729