{"id":"W4235531293","doi":"10.1002/ange.201407982","title":"Highly Specific Electrochemical Analysis of Cancer Cells using Multi‐Nanoparticle Labeling","year":2014,"lang":"en","type":"article","venue":"Angewandte Chemie","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Cancer cell; Circulating tumor cell; Cancer; Nanoparticle; Chemistry; Nanotechnology; Electrochemistry; Cancer research; Materials science; Biology; Medicine; Internal medicine; Metastasis; Electrode","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0002988655,0.000341385,0.0002640532,0.0003392986,0.0001299312,0.0004860972,0.0005137491,0.0007243652,0.0006639105],"category_scores_gemma":[0.0003472323,0.000212166,0.0001653971,0.0002622928,0.0002135605,0.0003234877,0.0004044541,0.0005356758,0.0003817536],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003677801,"about_ca_system_score_gemma":0.0001587701,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003205992,"about_ca_topic_score_gemma":0.0005111217,"domain_scores_codex":[0.9996821,0.00004243306,0.00001687802,0.0001084143,0.0001132358,0.00003700097],"domain_scores_gemma":[0.9998417,0.00004966014,0.0000251028,0.0000178829,0.00004641667,0.00001917055],"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":[0.00001539197,0.00001119694,0.00009964826,0.00002687796,0.000003511966,0.00001941832,0.000009406132,0.0001261923,0.9963117,0.0001691481,0.00009299655,0.003114421],"study_design_scores_gemma":[0.000004987106,0.00002974539,0.0003297641,0.000002584589,0.000004212834,0.00007411662,0.000004692748,0.004947894,0.9932972,0.00007582123,0.00122411,0.000004916068],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6562371,0.005050947,0.3297863,0.0007945576,0.0002763579,0.0001413229,0.0006050625,0.001174696,0.005933743],"genre_scores_gemma":[0.8709592,0.00141175,0.1225336,0.0002868145,0.00004184817,0.0001330739,0.0002628115,0.00004695058,0.00432381],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0007243652,"threshold_uncertainty_score":0.00266844,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01677871427495717,"score_gpt":0.2865161413762216,"score_spread":0.2697374271012644,"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."}}