{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001425541,0.00103317,0.0005379797,0.001199138,0.0002463237,0.001255832,0.001156696,0.001259409,0.001558513],"category_scores_gemma":[0.001672567,0.0005429415,0.0004097717,0.000618403,0.0007484587,0.0009550774,0.001002236,0.001130327,0.001389764],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005129671,"about_ca_system_score_gemma":0.0002843745,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002203136,"about_ca_topic_score_gemma":0.0003085792,"domain_scores_codex":[0.998904,0.0002657808,0.00005467542,0.0002193025,0.00048776,0.00006853831],"domain_scores_gemma":[0.9991978,0.0003108921,0.0001645655,0.0001081424,0.0001628319,0.00005579604],"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":[0.000119285,0.00004262191,0.0008979489,0.0001961986,0.0000261362,0.00007788635,0.00008204821,0.0002388731,0.9732631,0.001098239,0.0006143229,0.02334334],"study_design_scores_gemma":[0.000008851393,0.0002490268,0.001294888,0.00002044813,0.00004015258,0.0004605064,0.00002622847,0.003514216,0.9878651,0.0002532764,0.006241484,0.00002574767],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2054797,0.01521448,0.7624979,0.001306769,0.0004111051,0.0003439947,0.0005842202,0.004107722,0.01005405],"genre_scores_gemma":[0.4675505,0.009067741,0.507801,0.001130856,0.0002110296,0.0003779579,0.0007499799,0.0002066488,0.01290438],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001558513,"threshold_uncertainty_score":0.007539034,"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."}}