{"id":"W2951001292","doi":"10.7150/ntno.20758","title":"Amplified visual immunosensor integrated with nanozyme for ultrasensitive detection of avian influenza virus","year":2017,"lang":"en","type":"article","venue":"Nanotheranostics","topic":"Advanced Nanomaterials in Catalysis","field":"Materials Science","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Ministry of Agriculture, Food and Rural Affairs; Natural Sciences and Engineering Research Council of Canada; Ontario Ministry of Agriculture, Food and Rural Affairs","keywords":"Detection limit; Avian influenza virus; Immunoassay; Influenza A virus subtype H5N1; Virus; Chemistry; Analyte; Colloidal gold; Bioanalysis; Naked eye; Influenza A virus; Combinatorial chemistry; Nanotechnology; Virology; Nanoparticle; Chromatography; Materials science; Antibody; Biology","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":[0.0002558778,0.0004367368,0.0002413729,0.0002362622,0.00009247482,0.0002616447,0.0005164645,0.0005185157,0.0005003881],"category_scores_gemma":[0.0002975891,0.0002425196,0.0002346914,0.000152359,0.0001800392,0.0002654172,0.0002692657,0.000339215,0.0002416939],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003330393,"about_ca_system_score_gemma":0.0001918192,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003870521,"about_ca_topic_score_gemma":0.0006476235,"domain_scores_codex":[0.9996548,0.00004517301,0.00002596172,0.00008502328,0.0001575136,0.00003150341],"domain_scores_gemma":[0.9998956,0.00002156482,0.00003155909,0.000007756178,0.00002999151,0.00001347191],"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":[0.00002496071,0.0000134502,0.0001260019,0.00004560018,0.000003542336,0.00003552772,0.00001076476,0.0001052477,0.9964226,0.00008082351,0.00003829787,0.003093138],"study_design_scores_gemma":[0.000006769553,0.000108955,0.0006636545,0.000003071433,0.00001033725,0.0001420111,0.000007257588,0.004476602,0.9936277,0.00003269138,0.0009151582,0.000005872819],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8471803,0.006018112,0.1411507,0.0003079156,0.0002771332,0.00009679549,0.0001673436,0.000837758,0.003964031],"genre_scores_gemma":[0.9420715,0.001165459,0.05229279,0.0001468242,0.00002870246,0.00006762525,0.0001412677,0.0000193407,0.004066385],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0005185157,"threshold_uncertainty_score":0.002416372,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0190740259869833,"score_gpt":0.2996929596307992,"score_spread":0.280618933643816,"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."}}