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| http://dx.doi.org/10.1038/s41408-019-0244-2 | DOI Listing |
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Machine-learning derived identification of prognostic signature to forecast head and neck squamous cell carcinoma prognosis and drug response.
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Department of Otorhinolaryngology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Introduction: Head and neck squamous cell carcinoma (HNSCC), a highly heterogeneous malignancy is often associated with unfavorable prognosis. Due to its unique anatomical position and the absence of effective early inspection methods, surgical intervention alone is frequently inadequate for achieving complete remission. Therefore, the identification of reliable biomarker is crucial to enhance the accuracy of screening and treatment strategies for HNSCC.
View Article and Find Full Text PDFMicrodroplet Resuspension Off Surfaces.
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Department of Chemical and Biomolecular Engineering, Lehigh University, 19 Memorial Drive West, Bethlehem, Pennsylvania 18015, United States.
Understanding the resuspension of droplets from surfaces into air is important for elucidating a range of processes such as disease transmission of airborne pathogens and determining environmental contamination and the effectiveness of cleaning procedures. The resuspension condition is defined as the escape velocity of a droplet from a surface. This study investigated the dynamics of microliter-sized droplet resuspension off surfaces utilizing a novel free-fall device.
View Article and Find Full Text PDFCorrespondence on 'Triage in major incidents: development and external validation of novel machine learning-derived primary and secondary triage tools' by Xu .
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PANoptosis-Relevant Subgroups Predicts Prognosis and Characterizes the Tumour Microenvironment in Ovarian Cancer.
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Department of Gynecology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, Fujian Province, People's Republic of China.
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View Article and Find Full Text PDFPromoting LC-QToF based non-targeted fingerprinting and biomarker selection with machine learning for the discrimination of black tea geographical origin.
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National Measurement Laboratory: Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, 19 Chlorine Gardens, Belfast BT9 5DL, Northern Ireland, United Kingdom. Electronic address:
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