{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004152062,0.0001724547,0.0002148905,0.00005935859,0.0002531472,0.00001908961,0.0001028215,0.0001156339,6.180064e-7],"category_scores_gemma":[0.00008169132,0.0001624763,0.00007159565,0.00008657181,0.00009483998,0.000004001924,0.0001052274,0.00006352322,0.000002163413],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005300299,"about_ca_system_score_gemma":0.00003554141,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001348011,"about_ca_topic_score_gemma":0.00001880097,"domain_scores_codex":[0.9987331,0.00003381184,0.0002246864,0.0004734505,0.0001126894,0.0004222744],"domain_scores_gemma":[0.9993607,0.00003770137,0.0001311971,0.0003409276,0.00001431264,0.0001151792],"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":[0.0001277966,0.00004903832,0.0003853122,0.0001177987,0.00006440827,0.000001131143,0.00006959851,0.00001309676,0.9491878,0.00005376584,0.008095864,0.04183437],"study_design_scores_gemma":[0.003616138,0.001841019,0.001185341,0.0001273114,0.0001024502,0.00001644529,0.0005454893,0.007435183,0.821881,0.0006815104,0.1615768,0.0009912992],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.961503,0.0005910493,0.03017966,0.004943354,0.0001396919,0.002079098,0.0003487583,0.0001902272,0.00002520167],"genre_scores_gemma":[0.8756548,0.002462408,0.1189031,0.001004456,0.0002224389,0.00005953878,0.0006788747,0.00006034051,0.000954036],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1534809,"threshold_uncertainty_score":0.6625592,"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."}}