{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003258292,0.0003909876,0.0002905434,0.0001935211,0.0001306201,0.0002277187,0.0002113398,0.0002898221,0.0002871127],"category_scores_gemma":[0.0003388588,0.0001479573,0.0002470828,0.0002153065,0.0001136829,0.0001150665,0.0001938485,0.0002857118,0.0002867674],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002462366,"about_ca_system_score_gemma":0.0001159437,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001218732,"about_ca_topic_score_gemma":0.00133039,"domain_scores_codex":[0.9996196,0.00008762314,0.00004778986,0.00006470804,0.0001212764,0.0000589677],"domain_scores_gemma":[0.9998387,0.00004549922,0.00003331894,0.00002527291,0.00003494766,0.00002214169],"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":[0.000003468572,0.000004375275,0.00004641031,0.000006198299,8.705202e-7,0.000007975071,0.000003555744,0.00002358129,0.9997399,0.000004616951,0.000002476577,0.0001565259],"study_design_scores_gemma":[0.000002083829,0.00007675261,0.001238346,0.000001377994,0.00000599217,0.00007743799,0.000007802008,0.0005999121,0.9974484,0.00000726333,0.0005320683,0.000002471648],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9746187,0.001809411,0.02158313,0.00008113847,0.00002956164,0.0001247096,0.000310715,0.00008708341,0.001355655],"genre_scores_gemma":[0.9792256,0.00117381,0.01634035,0.00007844943,0.00001242337,0.00007477542,0.0009521417,0.00002847112,0.002113987],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001218732,"threshold_uncertainty_score":0.002423286,"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."}}