{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003381675,0.0002786814,0.0001970749,0.0002163008,0.0001473562,0.0002205454,0.0001235072,0.0003812578,0.0003599109],"category_scores_gemma":[0.0002750152,0.0001254322,0.0002819454,0.0001129857,0.0001941448,0.0001436745,0.0002177094,0.0002323611,0.0001579585],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001311638,"about_ca_system_score_gemma":0.0001875262,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005928006,"about_ca_topic_score_gemma":0.0009805752,"domain_scores_codex":[0.9998713,0.00003511173,0.000006597217,0.00003243639,0.0000345259,0.00002004971],"domain_scores_gemma":[0.9999229,0.00002172008,0.000009952842,0.000009904135,0.00002364969,0.00001196412],"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":[0.00002211075,0.000009687916,0.0001877535,0.00002064248,0.000005680992,0.00002508159,0.00001242586,0.00006732075,0.9976661,0.0000510859,0.00001989191,0.001912278],"study_design_scores_gemma":[0.000006381907,0.00020282,0.002841167,0.000006424519,0.00001903206,0.000228126,0.00002221567,0.001995026,0.9930503,0.00009020395,0.00152816,0.00001008954],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9468115,0.00264409,0.04720798,0.000270752,0.00007304385,0.0001573274,0.0001663015,0.0001430124,0.002526056],"genre_scores_gemma":[0.9591703,0.001182759,0.03746544,0.0002073076,0.00002229943,0.00005616462,0.0001532366,0.0000240633,0.001718392],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0005928006,"threshold_uncertainty_score":0.001788437,"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."}}