{"id":"W2318344459","doi":"10.1021/ac502157g","title":"In Vitro Selection, Characterization, and Biosensing Application of High-Affinity Cylindrospermopsin-Targeting Aptamers","year":2014,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal; Institut National de la Recherche Scientifique","funders":"","keywords":"Aptamer; Chemistry; Systematic evolution of ligands by exponential enrichment; Cylindrospermopsin; Biosensor; SELEX Aptamer Technique; Dissociation constant; Colloidal gold; Detection limit; Biophysics; Combinatorial chemistry; Nanotechnology; Chromatography; Biochemistry; Nanoparticle; Receptor; Molecular biology; Cyanobacteria; Biology; RNA","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001932081,0.0001231632,0.0001966847,0.000032158,0.00004399728,0.00001121801,0.00006102938,0.0001612959,0.000001507514],"category_scores_gemma":[0.0002092784,0.0001221872,0.00004779372,0.0002076235,0.0001304892,0.000005860844,0.00004455372,0.00009519023,4.521317e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000014588,"about_ca_system_score_gemma":0.00001628213,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002238225,"about_ca_topic_score_gemma":0.00000306349,"domain_scores_codex":[0.9991238,0.00002653957,0.0002780675,0.0003277898,0.00009085072,0.0001529185],"domain_scores_gemma":[0.9995229,0.00001698874,0.000125,0.0001640369,0.0001152359,0.00005582286],"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":[0.00003859394,0.00003541357,0.001844521,0.00002497329,0.00001612114,2.914054e-7,0.00000408241,0.000003436049,0.9935501,0.00002296255,0.00002093939,0.004438523],"study_design_scores_gemma":[0.0001572538,0.00001941497,0.00228871,0.000008065648,0.00002442532,0.000007576974,0.000009280605,0.004958782,0.9914123,0.000111186,0.0008658143,0.0001372271],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9755124,0.000009643056,0.02399796,0.0002234662,0.00000857657,0.00004721836,0.000007451438,0.00001725554,0.0001759881],"genre_scores_gemma":[0.995968,0.00003332481,0.003359275,0.0001208339,0.0001293823,0.000001967234,0.0002459383,0.0000118096,0.0001294104],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02063869,"threshold_uncertainty_score":0.4982651,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003725105099420929,"score_gpt":0.234666651093935,"score_spread":0.2309415459945141,"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."}}