{"id":"W4386726428","doi":"10.1021/acssensors.3c01221","title":"Paper-Based All-in-One Origami Nanobiosensor for Point-of-Care Detection of Cardiac Protein Markers in Whole Blood","year":2023,"lang":"en","type":"article","venue":"ACS Sensors","topic":"Biosensors and Analytical Detection","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Major Basic Research Project of the Natural Science Foundation of the Jiangsu Higher Education Institutions; Xi’an Jiaotong-Liverpool University; University of Toronto; Canada Foundation for Innovation; McGill University","keywords":"Point-of-care testing; Troponin I; Biomedical engineering; Immunoassay; Whole blood; Dielectric spectroscopy; Point of care; Electrode; Detection limit; Microfluidics; Nanotechnology; Materials science; Medicine; Chromatography; Electrochemistry; Chemistry; Internal medicine; Pathology","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":[0.000215941,0.0001685003,0.0003623064,0.0003399363,0.00002098398,0.000008554352,0.0000722619,0.0001922318,0.00000586748],"category_scores_gemma":[0.0001182561,0.0001797769,0.0001583326,0.0007387237,0.00004170432,0.00007225743,0.00001287572,0.000159541,0.00001226506],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006372602,"about_ca_system_score_gemma":0.00001292775,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007909079,"about_ca_topic_score_gemma":0.0001293118,"domain_scores_codex":[0.9988212,0.0000517886,0.0004102786,0.000226558,0.0001673859,0.0003227616],"domain_scores_gemma":[0.9994899,0.0001197206,0.00005591884,0.0002146383,0.00006964388,0.00005015115],"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":[0.00008036603,0.00003522951,0.0002178339,0.00058222,0.00005505048,0.000003936519,0.000130217,0.009394132,0.9852626,0.00001648354,0.00001900909,0.004202936],"study_design_scores_gemma":[0.0007997813,0.0002265946,0.006176111,0.0002018453,0.00005076893,4.9236e-7,0.0005015752,0.01675252,0.9741871,0.00006829831,0.0008234571,0.0002113882],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9986027,0.0001063672,0.00003918765,0.0001045389,0.0001627943,0.0006376651,0.00005985951,0.0001453501,0.0001415424],"genre_scores_gemma":[0.9993659,0.00003125981,0.0003238,0.000008371572,0.00004290256,0.00005654091,0.00001946882,0.00004737397,0.0001044447],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01107542,"threshold_uncertainty_score":0.7331088,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01375557467163464,"score_gpt":0.2210774445876881,"score_spread":0.2073218699160534,"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."}}