{"id":"W4386954916","doi":"10.1039/d3an01368e","title":"Light-up split aptamers: binding thermodynamics and kinetics for sensing","year":2023,"lang":"en","type":"article","venue":"The Analyst","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Institute for Nanotechnology","funders":"Waterloo Institute for Nanotechnology, University of Waterloo; Mitacs; University of Strathclyde","keywords":"Aptamer; Isothermal titration calorimetry; Dissociation constant; Chemistry; Thermostability; Fluorescence; Biosensor; Systematic evolution of ligands by exponential enrichment; Nanoparticle; Nanotechnology; Kinetics; Combinatorial chemistry; Biophysics; Materials science; RNA; Biochemistry; Biology; Molecular biology; Physics; Optics","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":[0.0006019784,0.0005035129,0.0003529877,0.0003452519,0.0001791482,0.0003727448,0.0004383678,0.0006450641,0.002054238],"category_scores_gemma":[0.001069591,0.0005060809,0.0003280906,0.0003358081,0.0002908167,0.000465597,0.0002225156,0.0008503671,0.0009224952],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006642513,"about_ca_system_score_gemma":0.0003582766,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008333907,"about_ca_topic_score_gemma":0.002010975,"domain_scores_codex":[0.9992917,0.000107924,0.00003496807,0.000185415,0.0003185715,0.0000613793],"domain_scores_gemma":[0.9996135,0.0001424905,0.00005176009,0.00004525566,0.000091016,0.00005609558],"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":[0.00009299475,0.00004416452,0.0002660124,0.00005519486,0.00001373833,0.00001801519,0.0000452799,0.0007533965,0.9935021,0.0003407692,0.0001506789,0.004717701],"study_design_scores_gemma":[0.00001034367,0.000107827,0.001005744,0.000004316646,0.00001145614,0.0001335541,0.00001276266,0.01429039,0.9829197,0.0002353758,0.001249489,0.00001901691],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8485358,0.004237649,0.1413303,0.0004488792,0.0001803346,0.0001840591,0.0007577376,0.0007504969,0.003574716],"genre_scores_gemma":[0.9634784,0.0007328295,0.02936426,0.0001789205,0.00001909071,0.0001520423,0.00067863,0.00008968827,0.005306092],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002054238,"threshold_uncertainty_score":0.006872118,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01492184985262925,"score_gpt":0.2821839246659315,"score_spread":0.2672620748133023,"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."}}