{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002625416,0.0001271435,0.0001415717,0.00007341064,0.0001439895,0.00003941872,0.0001102089,0.00008674523,3.249422e-7],"category_scores_gemma":[0.00004946935,0.00008612133,0.0001116084,0.0002670189,0.00006848283,0.000002193305,0.00009496267,0.00005428507,0.00000232797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008985709,"about_ca_system_score_gemma":0.000007889153,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007270228,"about_ca_topic_score_gemma":0.00004259889,"domain_scores_codex":[0.9992679,0.00003323986,0.000148677,0.0002551173,0.00008142473,0.000213646],"domain_scores_gemma":[0.9994674,0.00002878398,0.00007858925,0.0003224504,0.00006617625,0.00003661572],"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":[0.00002418032,0.000005130134,0.00005370324,0.000008646736,0.0001069357,0.000001230385,0.00003555587,0.000014329,0.9885271,0.0001050405,0.0006922334,0.01042591],"study_design_scores_gemma":[0.0003391591,0.0002292336,0.0002487772,0.0000315752,0.0003504716,0.00002563916,0.0009011381,0.02926563,0.9307873,0.001162152,0.03625214,0.0004068128],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9876639,0.0001061904,0.0104955,0.00113469,0.00005791689,0.0001514788,0.00001897986,0.00007417078,0.0002971907],"genre_scores_gemma":[0.9957063,0.0002822054,0.002576238,0.0001745465,0.0001675177,0.000002765796,0.0001140951,0.00002208799,0.000954289],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05773983,"threshold_uncertainty_score":0.3511926,"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."}}