{"id":"W2411047436","doi":"10.1021/acs.analchem.6b01604","title":"Blu-ray Technology-Based Quantitative Assays for Cardiac Markers: From Disc Activation to Multiplex Detection","year":2016,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Advanced Biosensing Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"eSenso (Canada); Simon Fraser University","funders":"China Scholarship Council; Division of Electrical, Communications and Cyber Systems; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Multiplex; Chemistry; Myoglobin; Aptamer; Point-of-care testing; Cardiac marker; Troponin complex; Biomarker; Point of care; Analyte; Protein detection; Detection limit; Myocardial infarction; Troponin; Molecular biology; Chromatography; Nanotechnology; Pathology; Biochemistry; Bioinformatics; Medicine; Cardiology; Biology","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":[0.00007505269,0.0001463595,0.0001402841,0.00002807841,0.00008058586,0.00001272654,0.0001274418,0.0002470098,0.00001061189],"category_scores_gemma":[0.0005094911,0.000115181,0.000122347,0.0001471075,0.00009701719,0.000004605148,0.00005181247,0.00006185089,0.000006664087],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000492094,"about_ca_system_score_gemma":0.00003152608,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007176113,"about_ca_topic_score_gemma":0.000003490411,"domain_scores_codex":[0.9990951,0.000009429272,0.0001639031,0.0004468104,0.00008280807,0.000201987],"domain_scores_gemma":[0.9992451,0.00007869527,0.00006525387,0.0003854822,0.0001404387,0.00008500061],"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":[0.0001453454,0.00003391858,0.0001645713,0.000006997179,0.00003842806,1.528843e-7,0.000001354525,0.00001169985,0.9920727,0.00008673793,0.0007573062,0.006680758],"study_design_scores_gemma":[0.0002802331,0.00009562664,0.0002568815,0.00003312798,0.00002898492,2.154055e-7,0.00002924632,0.0007329427,0.9686629,0.001232582,0.02845742,0.0001898054],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3678406,0.00001469753,0.6292201,0.002198143,0.00001752271,0.0002594412,0.0001729693,0.00007268053,0.0002037495],"genre_scores_gemma":[0.9578577,0.000007060957,0.041229,0.0001240999,0.0001312055,0.0001414168,0.0001626031,0.00002201541,0.0003248916],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.590017,"threshold_uncertainty_score":0.4696946,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01406807650477211,"score_gpt":0.2965707430036004,"score_spread":0.2825026664988283,"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."}}