{"id":"W4220747007","doi":"10.1021/acsnano.1c09564","title":"Nanoparticle Biomolecular Corona-Based Enrichment of Plasma Glycoproteins for N-Glycan Profiling and Application in Biomarker Discovery","year":2022,"lang":"en","type":"article","venue":"ACS Nano","topic":"Glycosylation and Glycoproteins Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"H2020 Marie Skłodowska-Curie Actions; Irish Research Council","keywords":"Glycan; Biomarker discovery; Nanomedicine; Nanotechnology; Fibrinogen; Glycoprotein; Glycosylation; Biomarker; Computational biology; Proteomics; Profiling (computer programming); Chemistry; Glycome; Nanoparticle; Materials science; Biology; Computer science; Biochemistry; Gene","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003291857,0.00009982524,0.0001134941,0.00009900794,0.0001096579,0.00001673142,0.0001384779,0.00005069398,0.000007262931],"category_scores_gemma":[0.00006059531,0.0001035191,0.00004360424,0.000227363,0.00007812535,0.000006720633,0.0001449238,0.00005009101,8.04244e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003774608,"about_ca_system_score_gemma":0.0001204426,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000869794,"about_ca_topic_score_gemma":0.00005081894,"domain_scores_codex":[0.9989744,0.00007284557,0.000221588,0.0003179696,0.000198293,0.0002149224],"domain_scores_gemma":[0.999541,0.00001895009,0.00008159039,0.000260586,0.00005323479,0.00004463608],"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":[0.0005020429,0.0001919522,0.004759139,0.00004390036,0.00001489017,0.000001464576,0.00001759848,0.000301437,0.9893625,0.0003815774,0.00004084808,0.00438259],"study_design_scores_gemma":[0.001556176,0.0005063926,0.001095161,0.000005243676,0.000005583493,0.000003392516,0.00007428503,0.00411268,0.9859708,0.00006946505,0.006481256,0.0001195624],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9896845,0.0002634478,0.008541306,0.000211855,0.00002961966,0.001140047,0.00007638634,0.000006468066,0.00004643666],"genre_scores_gemma":[0.9977016,0.00001065474,0.00088537,0.00008049371,0.00001264642,0.0009248688,0.0002280926,0.0000173106,0.0001389779],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008017152,"threshold_uncertainty_score":0.4221387,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01272653384955287,"score_gpt":0.2766380025125048,"score_spread":0.263911468662952,"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."}}