{"id":"W2826154191","doi":"10.1039/c8an00536b","title":"Fluorescence hyperspectral imaging for live monitoring of multiple spheroids in microfluidic chips","year":2018,"lang":"en","type":"article","venue":"The Analyst","topic":"3D Printing in Biomedical Research","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre Hospitalier de l’Université de Montréal; Polytechnique Montréal","funders":"Fonds de recherche du Québec – Nature et technologies; Université de Montréal; Canada Foundation for Innovation; Ovarian Cancer Canada; Cancer Research Society; Natural Sciences and Engineering Research Council of Canada; CMC Microsystems","keywords":"Hyperspectral imaging; Spheroid; Microfluidics; Microfluidic chip; Fluorescence; Fluorescence-lifetime imaging microscopy; Live cell imaging; Chemistry; Materials science; Nanotechnology; Remote sensing; Cell; Optics; Physics; Geology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005125194,0.0004161143,0.0002947002,0.0004327276,0.0002782375,0.0003187821,0.000391852,0.0004164814,0.001112724],"category_scores_gemma":[0.0003954378,0.0002411587,0.0002453456,0.0002682101,0.0002879874,0.0004867312,0.0004178181,0.000383054,0.0003027955],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005993319,"about_ca_system_score_gemma":0.0004389185,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008135441,"about_ca_topic_score_gemma":0.001456019,"domain_scores_codex":[0.9997066,0.00004404688,0.00001693581,0.00009922955,0.0001061856,0.00002696412],"domain_scores_gemma":[0.9997359,0.0001225382,0.00003734785,0.00003492095,0.00004615988,0.00002316708],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003432273,0.00002464311,0.0002122538,0.00003002064,0.00000431684,0.00001615987,0.00002749342,0.001321024,0.9921824,0.0002758857,0.0001758084,0.005695768],"study_design_scores_gemma":[0.000007331185,0.00008654817,0.001301086,0.000004098788,0.000006480036,0.00007056564,0.00001495958,0.03604782,0.9607835,0.0001714798,0.001489536,0.00001646782],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5046023,0.001221171,0.4881915,0.0002751344,0.000102623,0.0002277419,0.0008995014,0.001896296,0.002583785],"genre_scores_gemma":[0.5556891,0.0009361526,0.4401186,0.0001367371,0.00003397704,0.0003887973,0.0004169389,0.0001044383,0.00217536],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001112724,"threshold_uncertainty_score":0.004348397,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0223261995797972,"score_gpt":0.2844820182361817,"score_spread":0.2621558186563845,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}