Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Begüm Bektaş A"'
Autor:
Hülya ÇAŞKURLU, Ahmet Naci EMECEN, Begüm BEKTAŞ, Ferda YILMAZ İNAL, Ravza GÜNDÜZ, Sevim GÜNGÖREN OCAK, Yasemin ÇAĞ
Publikováno v:
Mediterranean Journal of Infection, Microbes and Antimicrobials, Vol 13, Iss 1, Pp 17-17 (2024)
Introduction: During the severe acute respiratory syndrome-Coronavirus-2 pandemic, intensive care units (ICUs) were pivotal in treating severe cases. In the ICU, invasive procedures and the use of immunosuppressive drugs have been associated with an
Externí odkaz:
https://doaj.org/article/36dcf6dcd50545cfa516a759b1c69032
Publikováno v:
Ankara Medical Journal, Vol 22, Iss 3, Pp 434-443 (2022)
INTRODUCTION: In our study, we aimed to evaluate the causes of peripheral lymphadenopathy (LAP). METHODS: Patients older than 18 years old who were diagnosed with LAP and underwent peripheral lymph node biopsies between 01.11.2017 and 01.01.2020 were
Externí odkaz:
https://doaj.org/article/c7f6b95942af4d38ae64852d61bac6a9
Autor:
İrfan Şencan, Yasemin Çağ, Oğuz Karabay, Behice Kurtaran, Ertuğrul Güçlü, Aziz Öğütlü, Zehra Demirbaş, Dilek Bulut, Gülden Eser Karlıdağ, Merve Sefa Sayar, Ezgi Gizem Şibar, Oya Özlem Eren Kutsoylu, Gülnur Kul, Serpil Erol, Begüm Bektaş, Tülay Ünver Ulusoy, Semanur Kuzi, Meltem Tasbakan, Özge Yiğit, Nurgül Ceran, Ayşe Seza İnal, Pınar Ergen, Tansu Yamazhan, Hanife Uzar, Canan Ağalar
Publikováno v:
Balkan Medical Journal, Vol 39, Iss 3, Pp 209-217 (2022)
Background: Broad-spectrum empirical antimicrobials are frequently prescribed for patients with coronavirus disease 2019 (COVID-19) despite the lack of evidence for bacterial coinfection. Aims: We aimed to cross-sectionally determine the frequency o
Externí odkaz:
https://doaj.org/article/1402341b3ec74327b912f7d18baba1b8
Autor:
Elkrief, Arielle, Montesion, Meagan, Sivakumar, Smruthy, Hale, Caryn, Bowman, Anita S., Begüm Bektaş, Ayyüce, Bradic, Martina, Kang, Wenfei, Chan, Eric, Gogia, Pooja, Manova-Todorova, Katia, Mata, Douglas A., Egger, Jacklynn V., Rizvi, Hira, Socci, Nicolas D., Kelly, Daniel W., Rosiek, Eric, Meng, Fanli, Tam, Grittney, Fan, Ning
Publikováno v:
Journal of Clinical Oncology; 10/1/2024, Vol. 42 Issue 28, p3339-3349, 13p
Autor:
Ayyüce Begüm Bektaş, Mehmet Gönen
Publikováno v:
BMC Bioinformatics, Vol 22, Iss 1, Pp 1-15 (2021)
Abstract Background Identification of molecular mechanisms that determine tumour progression in cancer patients is a prerequisite for developing new disease treatment guidelines. Even though the predictive performance of current machine learning mode
Externí odkaz:
https://doaj.org/article/3bdf0d9723b14774a875cc2b5b3bfcca
Autor:
Cem Sulu, Ayyüce Begüm Bektaş, Serdar Şahin, Emre Durcan, Zehra Kara, Ahmet Numan Demir, Hande Mefkure Özkaya, Necmettin Tanrıöver, Nil Çomunoğlu, Osman Kızılkılıç, Nurperi Gazioğlu, Mehmet Gönen, Pınar Kadıoğlu
Publikováno v:
Pituitary. 25(3)
Objective To develop machine learning (ML) models that predict postoperative remission, remission at last visit, and resistance to somatostatin receptor ligands (SRL) in patients with acromegaly and to determine the clinical features associated with
BackgroundHerpes simplex encephalitis (HSE) is the most common form of sporadic encephalitis which is caused by herpes simplex virus type 1 (HSV-1). Current guidelines recommend intravenous Acyclovir for 14–21 days in cases of HSE.ObjectivesOptimiz
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4b937280289f653447d67266ea4772b9
https://doi.org/10.1101/2022.02.08.22270584
https://doi.org/10.1101/2022.02.08.22270584
Publikováno v:
Bioinformatics
Motivation: dataset sizes in computational biology have been increased drastically with the help of improved data collection tools and increasing size of patient cohorts. Previous kernel-based machine learning algorithms proposed for increased interp
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6dd8b93d6a73fe7296d4b6a9d23f3b44
http://cdm21054.contentdm.oclc.org/cdm/ref/collection/IR/id/10639
http://cdm21054.contentdm.oclc.org/cdm/ref/collection/IR/id/10639