{"id":"W2153205039","doi":"10.3390/s150922291","title":"An Apta-Biosensor for Colon Cancer Diagnostics","year":2015,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; Ministry of Science Research and Technology","keywords":"Aptamer; Detection limit; Biosensor; Nanotechnology; Colorectal cancer; Cyclic voltammetry; Flow cytometry; Cancer; Chemistry; Materials science; Electrode; Computer science; Chromatography; Electrochemistry; Molecular biology; Medicine; Biology; Internal medicine","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":[0.0003881299,0.000778162,0.0004053243,0.0004668943,0.0002443052,0.0003969648,0.0007538685,0.001196975,0.001059799],"category_scores_gemma":[0.0003599769,0.0003831429,0.0003919085,0.0002927567,0.0002589118,0.0004453636,0.0003287153,0.0009430728,0.001653638],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006500875,"about_ca_system_score_gemma":0.0005133692,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005950456,"about_ca_topic_score_gemma":0.001097658,"domain_scores_codex":[0.9993576,0.0001157059,0.00003372447,0.0001411109,0.0002918102,0.00006004301],"domain_scores_gemma":[0.9998084,0.00004042612,0.00003311434,0.00001385892,0.00006522003,0.00003900241],"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":[0.0000227134,0.00001471845,0.0001319966,0.00009915404,0.000006562353,0.00005182427,0.0000103522,0.000109312,0.993499,0.0001988151,0.000210096,0.005645419],"study_design_scores_gemma":[0.000008183858,0.0001972605,0.0006371523,0.00001245827,0.00002016264,0.000850985,0.00001187459,0.002854611,0.9842277,0.0001030799,0.01106246,0.00001405809],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3327594,0.05760622,0.5788317,0.00332611,0.002204485,0.0007986157,0.001372362,0.005867023,0.01723418],"genre_scores_gemma":[0.63967,0.01441848,0.3185761,0.00124238,0.0002344191,0.000287365,0.001076383,0.0001001364,0.02439473],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001196975,"threshold_uncertainty_score":0.004716754,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02795962285682694,"score_gpt":0.3409626718800634,"score_spread":0.3130030490232364,"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."}}