Results 51 to 60 of about 600,653 (291)

An easy to control all-metal in-line-series ohmic RF MEMS switch

, 2010
Copyright @ 2010 Springer-VerlagThe analysis, design and simulation of a novel easy to control all-metal in-line-series ohmic RF MEMS switch is presented, for applications where the operating frequency ranges from DC to 4 GHz. The proposed switch, due to
K. Tsiakmakis, Nilavalan, R, Charalampidis, N, N. Charalampidis, Spasos, M, Tsiakmakis, K, R. Nilavalan, M. Spasos   +7 more
core   +1 more source

Loss of proton‐sensing TDAG8 increases tumor progression in mouse models of colon cancer

Molecular Oncology, EarlyView.
Loss of the pH‐sensing receptor TDAG8 accelerates colorectal cancer progression in mice. Animals lacking TDAG8 expression had increased tumor growth, DNA damage, and recruitment of tumor‐associated immune cells, including macrophages, neutrophils, and monocytes.
Ermanno Malagola, Yasmine Illi, Anna Bircher, Marijn Wilmink, Rachele F. Malagutti, Solenn Ottié, Cordelia Schuler, Pedro A. Ruiz, Cheryl de Vallière, Klaus Seuwen, Gerhard Rogler, Martin Hausmann   +11 more
wiley   +1 more source

In vitro and in silico modelling of ROS1‐positive non‐small cell lung cancer reveals fusion‐dependent tyrosine kinase inhibitor responses

Molecular Oncology, EarlyView.
Drug resistance limits treatment success in a subset of lung cancers driven by ROS1 gene alterations. Using patient‐derived cells and computer simulations, we studied three key mutations and how they affect five targeted drugs. The mutations reduced drug effectiveness in different ways by altering protein structure and behavior.
Farhan Ul Haq, Marc Terrones, Felicia Rodrigues Fortes, Anne Schepers, Alessia Denis, Christophe Deben, Ken Op de Beeck, Guy Van Camp, Geert Vandeweyer   +8 more
wiley   +1 more source

CureIt – A Multidisease Predictive System using Machine Learning [PDF]

ITM Web of Conferences
Many contemporary machine learning approaches perform well in predictive analytics on large datasets from many sectors. While predictive analytics in healthcare remains an ambitious goal, it has the potential to enable healthcare practitioners to make ...
Mehta Apoorva S., Raval Jinit S., Chaudhari Rupal R.   +2 more
doaj   +1 more source

Identifying gene expression signatures for risk stratification of postoperative adjuvant chemotherapy in colorectal cancer

FEBS Open Bio, EarlyView.
A novel signature integrating genome‐wide analysis with clinical factors predicts recurrence in stage II colorectal cancer and enables a new risk stratification to guide postoperative adjuvant chemotherapy. Clinical risk stratification for postoperative recurrence in patients with pathological stage II (pStage II) colorectal cancer (CRC) is essential ...
Mayuko Otomo, Keisuke Okuno, Shuichi Watanabe, Sakiko Oba, Hiroyasu Kagawa, Masanori Tokunaga, Daisuke Ban, Yusuke Kinugasa   +7 more
wiley   +1 more source

Application of machine learning methods on investigation the effect of interrupted deflector's height and downstream depth changes on energy dissipation at flip bucket spillways [PDF]

Journal of Hydraulic Structures
Flip bucket is a type of energy dissipator structure. Flip buckets can sometimes be improved by adding wedge-shaped deflectors. This research introduced the best height proportion of used deflector on the flip buckets to increase energy dissipation.
Arash Adib, Danial Ebrahimzadeh, Mahmood S. Bejestan, Morteza Lotfirad, Masoud Oulapour   +4 more
doaj   +1 more source

Risk Prediction Models for Recurrence After Curative Treatment of Early‐Stage or Locally Advanced Lung Cancer: A Systematic Review

Aging and Cancer, EarlyView.
This systematic review synthesizes prognostic models for survival and recurrence in resected non‐small cell lung cancer. While many models demonstrate moderate to good discrimination, few are externally validated and reporting quality is variable, limiting clinical applicability and highlighting the need for robust, transparent model development ...
Evangeline Samuel, S. Tissera, E. Paul, J. Zalcberg, R. G. Stirling   +4 more
wiley   +1 more source

Geo-spatial dynamics and machine learning insights: SVM and RF-driven land use and cover change detection in São Paulo, Brazil

Journal of Degraded and Mining Lands Management
A quantitative evaluation of land-use/land-cover (LULC) changes is required due to the rapid acceleration of LULC change in recent decades, which has been influenced by population growth, economic expansion, and industrial development, particularly in ...
G Lavanya, J Rajamurugadoss, V Sujatha, Muhammed Shameem Kachancheeri, SPM Kannan, Rupesh Gupta, Vivek Sivakumar   +6 more
doaj   +1 more source

Machine learning algorithm for improving performance on 3 AQ-screening classification

Communications in Science and Technology, 2019
Autism Spectrum Disorder (ASD) classification using machine learning can help parents, caregivers, psychiatrists, and patients to obtain the results of early detection of ASD.
Taftazani Ghazi Pratama, Rudy Hartanto, Noor Akhmad Setiawan   +2 more
doaj   +1 more source

Onasemnogene Abeparvovec in Type I Spinal Muscular Atrophy: 24‐Month Follow‐Up From the Italian Registry

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Onasemnogene abeparvovec (OA) is an AAV9‐based gene therapy for spinal muscular atrophy type I (SMA I). Real‐world outcomes show increased response variability compared to clinical trials, and follow‐up data beyond 12–18 months are limited.
Marika Pane, Giorgia Coratti, Chiara Cutrì, Antonio Varone, Riccardo Masson, Adele D'Amico, Valeria Sansone, Sonia Messina, Federica Ricci, Chiara Ticci, Claudio Bruno, Caterina Agosto, Francesca Benedetti, Antonella Pini, Sabrina Siliquini, Massimiliano Filosto, Alberto Zambon, Ilaria Bitetti, Maria Rosaria Manna, Claudia Dosi, Riccardo Zanin, Stefano Parravicini, Roberto De Sanctis, Giulia Stanca, Michela Catteruccia, Michele Tosi, Irene Mizzoni, Emilio Albamonte, Valentina Franchino, Maria Sframeli, Ilaria Cavallina, Elena Procopio, Michele Sacchini, Simone Morando, Noemi Brolatti, Federica Trucco, Gaia Scarpini, Elena Briganti, Beatrice Berti, Concetta Palermo, Daniela Leone, Stefano C. Previtali, Eugenio Mercuri, the ITASMAC working group   +43 more
wiley   +1 more source