{"id":"W4318755528","doi":"10.1039/d2lc00693f","title":"A microfluidic plasma separation device combined with a surface plasmon resonance biosensor for biomarker detection in whole blood","year":2023,"lang":"en","type":"article","venue":"Lab on a Chip","topic":"Biosensors and Analytical Detection","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Whole blood; Surface plasmon resonance; Biomarker; Microfluidics; Point of care; Biosensor; Chromatography; Lab-on-a-chip; Detection limit; Analyte; Blood plasma; Chemistry; Nanotechnology; Materials science; Medicine; Immunology; Pathology; Nanoparticle; Biochemistry","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":[0.0001445563,0.0001728331,0.0001771595,0.0001563942,0.00006957978,0.00003960547,0.00005649516,0.0001332837,0.000002878509],"category_scores_gemma":[0.00003040461,0.000155119,0.00004673574,0.0008576574,0.00002131511,0.00007436211,0.000008022655,0.0001412111,0.00009744568],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000516842,"about_ca_system_score_gemma":0.000008427934,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002280761,"about_ca_topic_score_gemma":0.0003454404,"domain_scores_codex":[0.9991044,0.00002901639,0.0001973066,0.0002564083,0.0001250517,0.0002878191],"domain_scores_gemma":[0.9996278,0.0001120042,0.00002850017,0.000148203,0.00003039115,0.00005314483],"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":[0.0009326093,0.0000916237,0.0004599231,0.0001661366,0.000054984,0.00001927792,0.0001401632,0.003127653,0.9875977,0.00009717506,0.001290757,0.006022022],"study_design_scores_gemma":[0.002572814,0.0005670003,0.01391982,0.0001466094,0.00003246412,0.000008681465,0.00004690057,0.2879119,0.6832498,0.00006675003,0.01117825,0.0002990051],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9983357,0.0001272908,0.0001911135,0.0002062521,0.0001403405,0.00037504,0.00003605371,0.0004081872,0.0001800515],"genre_scores_gemma":[0.9991075,0.00009520049,0.0001256127,0.00004065509,0.00002970833,0.00004299128,0.00002460045,0.00004376283,0.000489988],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3043478,"threshold_uncertainty_score":0.632557,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01565950406307934,"score_gpt":0.2373055788584089,"score_spread":0.2216460747953295,"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."}}