{"id":"W4409802708","doi":"10.1002/advs.202412185","title":"A Bead‐Based Quantum Dot Immunoassay Integrated with Multi‐Module Microfluidics Enables Real‐Time Multiplexed Detection of Blood Insulin and Glucagon","year":2025,"lang":"en","type":"article","venue":"Advanced Science","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Population Health Research Institute; McMaster University; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; CMC Microsystems","keywords":"Microfluidics; Immunoassay; Multiplexing; Quantum dot; Insulin; Bead; Insulin delivery; Nanotechnology; Materials science; Chromatography; Chemistry; Computer science; Diabetes mellitus; Medicine; Type 1 diabetes; Telecommunications; Internal medicine","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":[0.0005490753,0.0004536936,0.0004434063,0.0004238476,0.0001846081,0.0004612324,0.000673853,0.0007305131,0.0005433029],"category_scores_gemma":[0.0003899864,0.0003794038,0.0004226533,0.0002855416,0.0002745866,0.0004829337,0.0005370086,0.0004604362,0.0003795282],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004088051,"about_ca_system_score_gemma":0.0003162936,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003025196,"about_ca_topic_score_gemma":0.0004933051,"domain_scores_codex":[0.9994788,0.00007356189,0.0000293343,0.0001783504,0.0001840935,0.00005599978],"domain_scores_gemma":[0.9997649,0.00006246768,0.00006953983,0.00002491508,0.00004819244,0.00003011498],"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":[0.00004599861,0.00002795416,0.0003147292,0.00005855017,0.00001242601,0.00003456879,0.00001459806,0.0001988728,0.9906682,0.0004054743,0.0001203292,0.008098265],"study_design_scores_gemma":[0.0000133408,0.000233318,0.001414435,0.000005307191,0.00002579647,0.0002511034,0.000006598824,0.01127887,0.9824953,0.0001199752,0.004128527,0.00002741352],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4656632,0.005886143,0.5204121,0.0004859603,0.0004954762,0.0002537524,0.0005822899,0.002347057,0.003874057],"genre_scores_gemma":[0.7138234,0.001655919,0.2787814,0.0003798768,0.00009718047,0.0001869626,0.0003452389,0.00005651613,0.004673427],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0007305131,"threshold_uncertainty_score":0.002966046,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005033950947425008,"score_gpt":0.25003850537505,"score_spread":0.245004554427625,"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."}}