{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000209131,0.0006448086,0.0003580802,0.0003076929,0.0001500067,0.0005593603,0.0008407792,0.0008749348,0.000711045],"category_scores_gemma":[0.00043815,0.0003319637,0.0003794066,0.0002280974,0.0001793731,0.0006093456,0.0003446534,0.0005995547,0.0007288432],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000317024,"about_ca_system_score_gemma":0.000180268,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001890405,"about_ca_topic_score_gemma":0.0003738368,"domain_scores_codex":[0.9996868,0.00002618242,0.00002831349,0.00009636069,0.0001356491,0.00002670675],"domain_scores_gemma":[0.9998419,0.00004041706,0.00004289485,0.00001783859,0.00003914787,0.00001779369],"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":[0.00001908777,0.00001464409,0.00007292951,0.00005527724,0.000006500297,0.00003294644,0.000009644012,0.0001084992,0.9941388,0.0001128415,0.000103851,0.005324977],"study_design_scores_gemma":[0.000006513848,0.0001433577,0.0004833144,0.000005472712,0.00001971894,0.0001409529,0.000008439561,0.003568733,0.9919963,0.00009351801,0.003519655,0.00001384191],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5257457,0.01768566,0.4429101,0.0009291302,0.001486956,0.0003452873,0.001350224,0.002465768,0.007081215],"genre_scores_gemma":[0.6548136,0.006914491,0.3279277,0.001162109,0.000202216,0.0003115562,0.001175724,0.00007466567,0.007417894],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008749348,"threshold_uncertainty_score":0.002378702,"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."}}