{"id":"W4294867329","doi":"10.1021/acs.analchem.2c03013","title":"Single-Nanoparticle Differential Immunoassay for Multiplexed Gastric Cancer Biomarker Monitoring","year":2022,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Department of Science and Technology of Sichuan Province; Central University Basic Research Fund of China; National Natural Science Foundation of China","keywords":"Immunoassay; Analyte; Chemistry; Biomarker; Detection limit; Cancer biomarkers; Chromatography; Sample preparation; Multiplexing; Cancer; Computer science; Antibody; Immunology; Medicine","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001200498,0.0009464631,0.0006527409,0.0008801479,0.0002464868,0.0006535299,0.0008559924,0.001169969,0.0006158028],"category_scores_gemma":[0.001438479,0.000465532,0.0004515933,0.0005210876,0.0004886313,0.0007057092,0.0007414931,0.0009933803,0.0004086391],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007020219,"about_ca_system_score_gemma":0.0004487667,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004698596,"about_ca_topic_score_gemma":0.0008759508,"domain_scores_codex":[0.998676,0.0002088251,0.00008022321,0.0004834829,0.0004514524,0.00009998403],"domain_scores_gemma":[0.9995183,0.000142774,0.00007900417,0.00005902125,0.0001534812,0.00004747206],"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":[0.000136903,0.00006053629,0.001115991,0.0003179501,0.00003461675,0.0001361184,0.00009871508,0.0008125027,0.9609164,0.001221208,0.0005809708,0.03456816],"study_design_scores_gemma":[0.00001830733,0.0002421349,0.001015229,0.0000173503,0.00006754146,0.0003220635,0.00003696646,0.02333383,0.9700767,0.0005499994,0.004276988,0.00004303333],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3136247,0.01368625,0.6625321,0.0008791242,0.0005564848,0.0004900136,0.0006402252,0.002248027,0.005343151],"genre_scores_gemma":[0.6912531,0.003429641,0.3014789,0.0005134137,0.0001331481,0.000422726,0.0003157101,0.00005227469,0.002401044],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001200498,"threshold_uncertainty_score":0.006348908,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02491015088965848,"score_gpt":0.3004384250346708,"score_spread":0.2755282741450124,"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."}}