{"id":"W4381571297","doi":"10.1021/envhealth.3c00017","title":"Capture-SELEX for Chloramphenicol Binding Aptamers for Labeled and Label-Free Fluorescence Sensing","year":2023,"lang":"en","type":"article","venue":"Environment & Health","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Aptamer; Chemistry; Fluorescence; Detection limit; Dissociation constant; Systematic evolution of ligands by exponential enrichment; Biosensor; Isothermal titration calorimetry; DNA; Chromatography; Combinatorial chemistry; RNA; Analytical Chemistry (journal); Biophysics; Nanotechnology; Molecular biology; Biology; Biochemistry; Materials science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0007202633,0.0009432673,0.0008383808,0.0004693177,0.0003282301,0.0008441037,0.000833324,0.0005639956,0.00369923],"category_scores_gemma":[0.0007474828,0.0004003448,0.0005174034,0.0004289547,0.0002656851,0.0003634861,0.0007856076,0.0007863393,0.002832639],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003973189,"about_ca_system_score_gemma":0.0002771111,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004349551,"about_ca_topic_score_gemma":0.0008935662,"domain_scores_codex":[0.9995308,0.00006487845,0.00003866329,0.0001103954,0.0001934219,0.00006194128],"domain_scores_gemma":[0.9997885,0.0000863283,0.00002931687,0.00003066869,0.00003706124,0.00002821975],"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":[0.0001442096,0.00009677623,0.0003587822,0.0001678878,0.00004328125,0.0003194613,0.00009888733,0.002261138,0.9678825,0.0004820641,0.001021171,0.02712387],"study_design_scores_gemma":[0.00002217232,0.0001525571,0.0005797765,0.00001218793,0.00002599854,0.0003710632,0.00003039157,0.008492068,0.9807549,0.0001503963,0.009386018,0.00002243976],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5153702,0.001542874,0.4556437,0.0004406527,0.0001684381,0.001364564,0.004748568,0.006811351,0.0139097],"genre_scores_gemma":[0.626112,0.002116295,0.3145327,0.0005697126,0.00003319017,0.002082249,0.01024169,0.001779599,0.0425325],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00369923,"threshold_uncertainty_score":0.01237518,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01967348016105042,"score_gpt":0.2919636359481484,"score_spread":0.272290155787098,"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."}}