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Natural killer cell detection, quantification, and subpopulation identification on paper microfluidic cell chromatography using smartphone-based machine learning classification.
Zenhausern, Ryan; Day, Alexander S; Safavinia, Babak; Han, Seungmin; Rudy, Paige E; Won, Young-Wook; Yoon, Jeong-Yeol.
Affiliation
  • Zenhausern R; Department of Biomedical Engineering, The University of Arizona, Tucson, AZ, 85721, United States.
  • Day AS; Department of Biomedical Engineering, The University of Arizona, Tucson, AZ, 85721, United States.
  • Safavinia B; Department of Biomedical Engineering, The University of Arizona, Tucson, AZ, 85721, United States.
  • Han S; Department of Surgery, The University of Arizona College of Medicine, Tucson, AZ, 85721, United States.
  • Rudy PE; Department of Biomedical Engineering, The University of Arizona, Tucson, AZ, 85721, United States.
  • Won YW; Department of Surgery, The University of Arizona College of Medicine, Tucson, AZ, 85721, United States.
  • Yoon JY; Department of Biomedical Engineering, The University of Arizona, Tucson, AZ, 85721, United States. Electronic address: jyyoon@arizona.edu.
Biosens Bioelectron ; 200: 113916, 2022 Mar 15.
Article in En | MEDLINE | ID: mdl-34974261
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Full text: 1 Database: MEDLINE Main subject: Biosensing Techniques / Microfluidics Type of study: Diagnostic_studies / Prognostic_studies Language: En Journal: Biosens Bioelectron Journal subject: BIOTECNOLOGIA Year: 2022 Type: Article Affiliation country: United States
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Full text: 1 Database: MEDLINE Main subject: Biosensing Techniques / Microfluidics Type of study: Diagnostic_studies / Prognostic_studies Language: En Journal: Biosens Bioelectron Journal subject: BIOTECNOLOGIA Year: 2022 Type: Article Affiliation country: United States