{"id":"W4313397293","doi":"10.1038/s41378-022-00460-5","title":"Label-free impedimetric immunosensor for point-of-care detection of COVID-19 antibodies","year":2023,"lang":"en","type":"article","venue":"Microsystems & Nanoengineering","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":57,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba; Canadian Food Inspection Agency; Provincial Laboratory of Public Health; University of Calgary; University of Alberta","funders":"Alberta Innovates; Mitacs","keywords":"Biosensor; Capacitance; Miniaturization; Microelectrode; Materials science; Antibody; Nanotechnology; Point-of-care testing; Detection limit; Coronavirus disease 2019 (COVID-19); Capacitive sensing; Electrical impedance; Point of care; Chromatography; Chemistry; Computer science; Electrode; Biology; Medicine; Immunology; Physics; Infectious disease (medical specialty)","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":[0.0009639665,0.001000777,0.0006204864,0.0006343745,0.0002288139,0.00053178,0.001096629,0.001153931,0.0009519876],"category_scores_gemma":[0.001167421,0.0004896865,0.0004449204,0.0003906095,0.0003122884,0.0005793798,0.0006576019,0.001288401,0.0009669615],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006928024,"about_ca_system_score_gemma":0.000312849,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003418304,"about_ca_topic_score_gemma":0.0006240653,"domain_scores_codex":[0.9984171,0.0002871348,0.0001104649,0.0002671747,0.0008297879,0.00008823376],"domain_scores_gemma":[0.9996387,0.0001085745,0.00006310066,0.00003913088,0.000119562,0.00003093129],"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":[0.00003084627,0.00003057961,0.0001085781,0.0001056702,0.000008806365,0.00003534081,0.00001780263,0.0001787403,0.9952912,0.0002035281,0.0002217917,0.003767231],"study_design_scores_gemma":[0.00001151158,0.0001454376,0.0004375263,0.00001170543,0.000016124,0.0001471302,0.00001010621,0.00494907,0.9899094,0.0000885132,0.004253904,0.0000195232],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3308819,0.01315862,0.6383547,0.001586047,0.001187209,0.0007262235,0.001073653,0.00356994,0.009461733],"genre_scores_gemma":[0.5282811,0.005681127,0.4548813,0.001192456,0.0001972646,0.0006760894,0.001143833,0.000111838,0.007834959],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001153931,"threshold_uncertainty_score":0.005098045,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01048741084388277,"score_gpt":0.2766302480233325,"score_spread":0.2661428371794498,"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."}}