{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002760629,0.0002605933,0.0002127717,0.0001224748,0.00007799804,0.0003110034,0.0002628488,0.0003290194,0.0006363164],"category_scores_gemma":[0.0001636252,0.0001598622,0.0002044588,0.0001363085,0.0001057647,0.0001317836,0.0002183676,0.0003449395,0.0003840244],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004093869,"about_ca_system_score_gemma":0.0002451373,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001008872,"about_ca_topic_score_gemma":0.0008252155,"domain_scores_codex":[0.9998522,0.00002236156,0.00001523093,0.00002545637,0.00004331294,0.00004142918],"domain_scores_gemma":[0.9999208,0.000007033211,0.00001847361,0.000008797249,0.00002344956,0.00002141885],"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":[0.00004894789,0.0000296364,0.0001198087,0.00002205945,0.000005245863,0.00003962756,0.00001115404,0.0003514623,0.9980991,0.00008556445,0.00002972783,0.001157595],"study_design_scores_gemma":[0.00003020829,0.0004898053,0.0013735,0.000007350801,0.00002465811,0.000334314,0.00001327307,0.002776616,0.9868563,0.00003202292,0.008054862,0.000007092121],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9772862,0.001137448,0.01920287,0.0001065022,0.00004179806,0.0001675765,0.000409549,0.00008627578,0.001561749],"genre_scores_gemma":[0.9748288,0.000823527,0.01957637,0.00007056132,0.000009060939,0.00008195488,0.000968508,0.00003800534,0.003603148],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001008872,"threshold_uncertainty_score":0.002970278,"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."}}