{"id":"W2481456694","doi":"10.1021/acs.analchem.6b01324","title":"Combination of Mechanical and Molecular Filtration for Enhanced Enrichment of Circulating Tumor Cells","year":2016,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Cancer Cells and Metastasis","field":"Medicine","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; McGill University; McGill University and Génome Québec Innovation Centre","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Lloyd Carr-Harris Foundation; Consejo Nacional de Ciencia y Tecnología; Canada Research Chairs","keywords":"Circulating tumor cell; Chemistry; Epithelial cell adhesion molecule; Filtration (mathematics); Size-exclusion chromatography; Receptor; Antibody; Flow cytometry; Epidermal growth factor receptor; Cancer cell; Cell; Cancer; Molecular biology; Chromatography; Cancer research; Metastasis; Biochemistry; Immunology; Biology","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":[0.000310493,0.000508049,0.0003596221,0.0004426944,0.0001580746,0.0005707946,0.0003705469,0.0007736281,0.0008787753],"category_scores_gemma":[0.0004319196,0.0002423708,0.0004494281,0.000199933,0.0002309652,0.0003357135,0.0003242627,0.0004185638,0.0005203938],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002830035,"about_ca_system_score_gemma":0.0002366125,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004944199,"about_ca_topic_score_gemma":0.0009072981,"domain_scores_codex":[0.9996085,0.00003541039,0.00002083024,0.0001053723,0.0001522598,0.00007768915],"domain_scores_gemma":[0.9998142,0.00006070398,0.00003892766,0.00001827351,0.00003919172,0.00002866745],"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":[0.00002423884,0.00001978688,0.0001648689,0.00004282105,0.00001108576,0.00003438331,0.00001139169,0.0001105327,0.9954951,0.00005720715,0.0001167735,0.003911924],"study_design_scores_gemma":[0.000008072025,0.000123221,0.001953041,0.000004938021,0.00002086329,0.0001674605,0.000007992239,0.001975716,0.9925795,0.00003220547,0.003114496,0.0000125559],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8649918,0.005115175,0.1224797,0.0004992385,0.0003147739,0.0002315814,0.0004708389,0.00144598,0.00445095],"genre_scores_gemma":[0.905947,0.002240074,0.08372025,0.0005871977,0.0001113357,0.0002118958,0.0005391554,0.0001536152,0.006489414],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008787753,"threshold_uncertainty_score":0.00293982,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01234247565645423,"score_gpt":0.2714463650655654,"score_spread":0.2591038894091112,"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."}}