{"id":"W2086899725","doi":"10.3390/toxins6082435","title":"Selection and Characterization of a Novel DNA Aptamer for Label-Free Fluorescence Biosensing of Ochratoxin A","year":2014,"lang":"en","type":"article","venue":"Toxins","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":145,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Aptamer; Systematic evolution of ligands by exponential enrichment; Biosensor; Ochratoxin A; Nucleic acid; Ochratoxin; DNA; SYBR Green I; Computational biology; Detection limit; SELEX Aptamer Technique; Biology; Mycotoxin; Chemistry; Molecular biology; Biotechnology; Biochemistry; Chromatography; Polymerase chain reaction; Gene; RNA","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":[0.0003411199,0.0003289478,0.0002776723,0.0002404225,0.0001285357,0.0002992484,0.0002905517,0.0004705397,0.0004009284],"category_scores_gemma":[0.0005405456,0.000185078,0.0002053732,0.0001467605,0.0002270312,0.0001873342,0.000197139,0.0003393846,0.0003564551],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000304944,"about_ca_system_score_gemma":0.0002649674,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000382567,"about_ca_topic_score_gemma":0.0006290541,"domain_scores_codex":[0.9995899,0.00005264685,0.00003545649,0.0001046383,0.0001699808,0.00004748514],"domain_scores_gemma":[0.9996144,0.00009275723,0.0001017983,0.00003932378,0.00008255753,0.00006912717],"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":[0.000006622609,0.000006901486,0.00004684407,0.000005207618,6.906184e-7,0.000009760057,0.000004546941,0.00005445073,0.9994472,0.00001152524,0.000002566164,0.0004036056],"study_design_scores_gemma":[0.000005637708,0.0001339326,0.0008077575,0.000001997593,0.00000399382,0.00009572535,0.000005360243,0.001186246,0.9972554,0.00001553129,0.0004848623,0.000003645297],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9516042,0.0004512478,0.0465285,0.0001053997,0.00002567918,0.000181879,0.0002589137,0.000139342,0.0007048569],"genre_scores_gemma":[0.944649,0.0003289192,0.05162573,0.0001039322,0.00001345405,0.0001339529,0.000726497,0.00004833701,0.002370081],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0004705397,"threshold_uncertainty_score":0.002212465,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01173500889986767,"score_gpt":0.2594944724695134,"score_spread":0.2477594635696457,"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."}}