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Al-Sawaff ZH, Dalgic SS, Kandemirli F, Monajjemi M, Mollaamin F. DFT Study Adsorption of Hydroxychloroquine for Treatment COVID-19 by SiC Nanotube and Al, Si Doping on Carbon Nanotube Surface: A Drug Delivery Simulation. RUSSIAN JOURNAL OF PHYSICAL CHEMISTRY A 2022. [PMCID: PMC9801348 DOI: 10.1134/s003602442213026x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Affiliation(s)
- Zaid H. Al-Sawaff
- Material Science and Engineering Department, Faculty of Engineering and Architecture, Kastamonu University, Kastamonu, Turkey
- Medical Instrumentation Technology, Technical Engineering College, Northern Technical University, Mosul, Iraq
| | - Serap Senturk Dalgic
- Department of Physics, Faculty of Science, Trakya University, 22030 Edirne, Turkey
| | - Fatma Kandemirli
- Biomedical Engineering Department, Faculty of Engineering and Architecture, Kastamonu University, Kastamonu, Turkey
| | - Majid Monajjemi
- Biomedical Engineering Department, Faculty of Engineering and Architecture, Kastamonu University, Kastamonu, Turkey
| | - Fatemeh Mollaamin
- Biomedical Engineering Department, Faculty of Engineering and Architecture, Kastamonu University, Kastamonu, Turkey
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Kozdra S, Jacquet M, Kargul J, Hęclik K, Wójcik A, Piotr Michałowski P. Insight into structure-property relationship of organometallic terpyridine wires: Combined theoretical and experimental study. Polyhedron 2022. [DOI: 10.1016/j.poly.2021.115628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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van der Lubbe MFJA, Vaidyanathan A, de Wit M, van den Burg EL, Postma AA, Bruintjes TD, Bilderbeek-Beckers MAL, Dammeijer PFM, Bossche SV, Van Rompaey V, Lambin P, van Hoof M, van de Berg R. A non-invasive, automated diagnosis of Menière's disease using radiomics and machine learning on conventional magnetic resonance imaging: A multicentric, case-controlled feasibility study. Radiol Med 2021; 127:72-82. [PMID: 34822101 PMCID: PMC8795017 DOI: 10.1007/s11547-021-01425-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 10/26/2021] [Indexed: 12/02/2022]
Abstract
Purpose This study investigated the feasibility of a new image analysis technique (radiomics) on conventional MRI for the computer-aided diagnosis of Menière’s disease. Materials and methods A retrospective, multicentric diagnostic case–control study was performed. This study included 120 patients with unilateral or bilateral Menière’s disease and 140 controls from four centers in the Netherlands and Belgium. Multiple radiomic features were extracted from conventional MRI scans and used to train a machine learning-based, multi-layer perceptron classification model to distinguish patients with Menière’s disease from controls. The primary outcomes were accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the classification model. Results The classification accuracy of the machine learning model on the test set was 82%, with a sensitivity of 83%, and a specificity of 82%. The positive and negative predictive values were 71%, and 90%, respectively. Conclusion The multi-layer perceptron classification model yielded a precise, high-diagnostic performance in identifying patients with Menière’s disease based on radiomic features extracted from conventional T2-weighted MRI scans. In the future, radiomics might serve as a fast and noninvasive decision support system, next to clinical evaluation in the diagnosis of Menière’s disease. Supplementary Information The online version contains supplementary material available at 10.1007/s11547-021-01425-w.
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Affiliation(s)
- Marly F J A van der Lubbe
- Department of Otolaryngology and Head and Neck Surgery, Maastricht University Medical Center +, Maastricht, The Netherlands.
| | - Akshayaa Vaidyanathan
- The D-Lab, Department of Precision Medicine, GROW Research Institute for Oncology, Maastricht University, Maastricht, The Netherlands.,Research and Development, Oncoradiomics SA, Liege, Belgium
| | - Marjolein de Wit
- Department of Otolaryngology and Head and Neck Surgery, Maastricht University Medical Center +, Maastricht, The Netherlands
| | - Elske L van den Burg
- Department of Otolaryngology and Head and Neck Surgery, Maastricht University Medical Center +, Maastricht, The Netherlands
| | - Alida A Postma
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, The Netherlands.,School for Mental Health and Sciences, Maastricht University, Maastricht, The Netherlands
| | - Tjasse D Bruintjes
- Department of Otorhinolaryngology, Gelre Hospital, Apeldoorn, The Netherlands.,Department of Otorhinolaryngology, Leiden University Medical Center, Leiden, The Netherlands
| | | | | | - Stephanie Vanden Bossche
- Department of Radiology, Antwerp University Hospital, Antwerp, Belgium.,Department of Radiology, AZ St-Jan Brugge-Oostende, Bruges, Belgium
| | - Vincent Van Rompaey
- Department of Otorhinolaryngology and Head & Neck Surgery, Antwerp University Hospital, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
| | - Philippe Lambin
- The D-Lab, Department of Precision Medicine, GROW Research Institute for Oncology, Maastricht University, Maastricht, The Netherlands
| | - Marc van Hoof
- Department of Otolaryngology and Head and Neck Surgery, Maastricht University Medical Center +, Maastricht, The Netherlands
| | - Raymond van de Berg
- Department of Otolaryngology and Head and Neck Surgery, Maastricht University Medical Center +, Maastricht, The Netherlands
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