{"id":"W4312210229","doi":"10.3390/pharmaceutics14122824","title":"Engineering of a Fully Human Anti-MUC-16 Antibody and Evaluation as a PET Imaging Agent","year":2022,"lang":"en","type":"article","venue":"Pharmaceutics","topic":"Glycosylation and Glycoproteins Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal University Hospital; University of Saskatchewan","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Saskatchewan Health Research Foundation","keywords":"Biodistribution; Monoclonal antibody; In vivo; Pancreatic cancer; Cancer research; Antibody; Imaging agent; Flow cytometry; Ex vivo; Ovarian cancer; Preclinical imaging; Distribution (mathematics); Chemistry; Cancer; Medicine; Immunology; Biology; Internal medicine","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005692052,0.00008320301,0.00008318981,0.00008108324,0.0001270653,0.00001653381,0.0001057912,0.00001376745,0.0003452447],"category_scores_gemma":[0.00006740527,0.00009411215,0.00003645047,0.0001053869,0.00002963003,0.000003724141,0.0002261583,0.0001206728,0.000002883269],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003132637,"about_ca_system_score_gemma":0.00006641353,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002151797,"about_ca_topic_score_gemma":0.000001641866,"domain_scores_codex":[0.9990867,0.00008931931,0.0001469113,0.0001891335,0.00032543,0.0001624697],"domain_scores_gemma":[0.9996243,0.000008356708,0.00005364755,0.000148204,0.000101657,0.00006390767],"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":[0.00002398853,0.00005395972,0.003555224,0.00003834042,0.00002905251,0.000006973866,0.00005727564,0.001427411,0.9915954,0.00006705063,0.0001933279,0.002951975],"study_design_scores_gemma":[0.001708905,0.0001450604,0.004135161,0.00001178234,0.00006665619,0.0001452667,0.0002243016,0.06323133,0.8248544,0.00005905759,0.1051665,0.0002515243],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.997488,0.000869275,0.0002617095,0.0001640088,0.00007592302,0.0002891512,0.00002100574,0.000008801736,0.0008220942],"genre_scores_gemma":[0.9992683,0.0001151361,0.0001308954,0.0001006397,0.00004830418,0.00005061706,0.0001389276,0.00001512724,0.0001320804],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.166741,"threshold_uncertainty_score":0.3837782,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03420917442412916,"score_gpt":0.3971544058658853,"score_spread":0.3629452314417562,"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."}}