Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Levent ŞENSOY"'
Publikováno v:
Pamukkale Medical Journal.
Purpose: In this study, we evaluated healthcare-associated urinary tract infections caused by carbapenem-resistant Klebsiella pneumoniae. Materials and methods: The study included 134 patients, diagnosed with healthcare-associated urinary tract infec
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::63091ccb081fd473b901410f8cfd0f37
https://doi.org/10.31362/patd.1244480
https://doi.org/10.31362/patd.1244480
Publikováno v:
Tropical Journal of Pharmaceutical Research; Vol. 20 No. 8 (2021); 1691-1696
Purpose: To investigate the frequency of concurrent drug use and drug interactions in patients with human immunodeficiency virus (HIV) infection. Methods: The medical records of HIV-infected patients followed up at Ondokuz Mayis University Hospital i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::67661730fce30bda5c0c317e6b83b5e3
https://www.ajol.info/index.php/tjpr/article/view/222776
https://www.ajol.info/index.php/tjpr/article/view/222776
Publikováno v:
Journal of Health Sciences and Medicine. 3:324-329
Giriş: Hasta sayılarının artması, uygulanan invaziv girişimlerin çeşitliliği ve eşlik eden hastalıklardaki artış, hastaların yönetiminde multidisipliner yaklaşımı vazgeçilmez kılmaktadır. Özellikle immünsüpresyona yol açan te
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::90d84036dfb01b868c3c50ef8c86d37d
https://doi.org/10.32322/jhsm.738660
https://doi.org/10.32322/jhsm.738660
Publikováno v:
Pamukkale Medical Journal.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4a2a42713a52d2987d0b1aab129bdf17
https://doi.org/10.31362/patd.940929
https://doi.org/10.31362/patd.940929
Autor:
Wen Chen, Yimin Li, Nimu Yuan, Jinyi Qi, Brandon A. Dyer, Levent Sensoy, Stanley H. Benedict, Lu Shang, Shyam Rao, Yi Rong
Publikováno v:
Frontiers in Artificial Intelligence, Vol 3 (2021)
Purpose: To assess image quality and uncertainty in organ-at-risk segmentation on cone beam computed tomography (CBCT) enhanced by deep-learning convolutional neural network (DCNN) for head and neck cancer.Methods: An in-house DCNN was trained using
Externí odkaz:
https://doaj.org/article/8048f3dbae914053ba9fafadec0bd6ee