{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005431001,0.00008525619,0.0001416197,0.00009202158,0.00005389046,0.00001756172,0.0003744234,0.00003048986,0.00002776808],"category_scores_gemma":[0.000260288,0.00006640357,0.00006091581,0.0003372573,0.0001976765,0.00004581841,0.00005949616,0.0001627859,0.00003052328],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006645428,"about_ca_system_score_gemma":0.00001779611,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002210073,"about_ca_topic_score_gemma":0.00001820257,"domain_scores_codex":[0.9991477,0.00002874073,0.0001993092,0.0001267217,0.0001792179,0.0003183451],"domain_scores_gemma":[0.9993392,0.0002679685,0.00002129098,0.000258145,0.00006518469,0.00004817248],"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":[0.00002068386,0.00001779985,0.1255202,0.00006340482,0.00004185966,0.000002264876,0.001086642,0.00007662809,0.8646759,0.0000388931,0.0004771035,0.007978517],"study_design_scores_gemma":[0.000516304,0.00004348849,0.07638271,0.0001787332,0.00002435532,0.000003096858,0.001254751,0.07842354,0.8420755,0.0002568873,0.0006695298,0.0001710898],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9954216,0.001266849,0.002528789,0.000119652,0.000157282,0.0001460979,0.000003700721,0.0000515934,0.0003044698],"genre_scores_gemma":[0.9952562,0.0001394026,0.004229631,0.000004400467,0.0003047886,0.00001275588,0.000001146105,0.00001905212,0.0000326621],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07834692,"threshold_uncertainty_score":0.2707859,"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."}}