{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002340371,0.0001642681,0.0003010032,0.0003119937,0.00006807879,0.0000101078,0.000178857,0.0001505552,2.002771e-7],"category_scores_gemma":[0.000425325,0.000156735,0.0001755238,0.0005558591,0.00004254859,0.000004584186,0.0001015608,0.00004579069,6.77246e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003502417,"about_ca_system_score_gemma":0.00003469685,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004074619,"about_ca_topic_score_gemma":0.00002320608,"domain_scores_codex":[0.999021,0.00002151413,0.0003688941,0.0002589294,0.00009964612,0.0002300399],"domain_scores_gemma":[0.9991322,0.0000425658,0.0001726247,0.000409502,0.0001926991,0.00005041724],"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":[0.00005038743,0.00001011086,0.0001585522,0.0007636427,0.00008238976,0.000001068872,0.00006661817,0.0001599002,0.9973305,0.000006568173,0.0001902345,0.001180005],"study_design_scores_gemma":[0.0004681927,0.0002363176,0.00009105828,0.00006201072,0.00003973722,0.00001214515,0.0003352843,0.000331103,0.9916583,0.000008242153,0.006591049,0.0001665199],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.964543,0.003559219,0.030925,0.00002522448,0.0002198854,0.0003551018,0.0002180235,0.0001419644,0.00001259501],"genre_scores_gemma":[0.9958582,0.0003442731,0.003392679,0.000009424042,0.00009877533,0.00001880157,0.000130032,0.00003234383,0.0001154986],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03131519,"threshold_uncertainty_score":0.6391466,"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."}}