{"id":"W2919517942","doi":"10.1080/19420862.2019.1581017","title":"Impact of N-glycosylation on Fcγ receptor / IgG interactions: unravelling differences with an enhanced surface plasmon resonance biosensor assay based on coiled-coil interactions","year":2019,"lang":"en","type":"article","venue":"mAbs","topic":"Monoclonal and Polyclonal Antibodies Research","field":"Medicine","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Polytechnique Montréal; National Research Council Canada","funders":"National Research Council Canada; Servier; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Surface plasmon resonance; Glycosylation; Biosensor; Chemistry; Receptor–ligand kinetics; Immunoglobulin G; Fc receptor; Biophysics; Receptor; Fragment crystallizable region; Biochemistry; Antibody; Biology; Nanotechnology; Materials science; Immunology","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002103104,0.0002400901,0.0003821013,0.0002111258,0.0001064004,0.00003299836,0.0001168211,0.00007159761,0.001402147],"category_scores_gemma":[0.0001179767,0.0001595603,0.0001394927,0.0003058034,0.00007679786,0.0001660748,0.00001779695,0.0004686388,0.000342424],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001810773,"about_ca_system_score_gemma":0.0001427226,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006525628,"about_ca_topic_score_gemma":0.0001768243,"domain_scores_codex":[0.9982694,0.0001589069,0.0003213465,0.0004047457,0.0005526567,0.0002929745],"domain_scores_gemma":[0.9981853,0.0008226028,0.0001862984,0.0003773645,0.0002729604,0.000155488],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.05169889,0.002942341,0.2750685,0.000387231,0.0003560794,0.0000290167,0.001048034,0.01050573,0.6247153,0.0004646889,0.001014955,0.03176922],"study_design_scores_gemma":[0.003717985,0.01592844,0.5257127,0.003252035,0.00006990604,0.00002438977,0.0003935502,0.1140069,0.3309042,0.00004707152,0.005342708,0.0006000362],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9932916,0.00002864423,0.0002143788,0.0006094287,0.0002506181,0.0004367764,0.0001018626,0.00004553674,0.005021141],"genre_scores_gemma":[0.9928516,0.00006392095,0.0007892911,0.00009644507,0.0001038647,0.0000141961,0.0001602622,0.00002910131,0.005891303],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2938111,"threshold_uncertainty_score":0.9995107,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02898577292644913,"score_gpt":0.3346505426039931,"score_spread":0.305664769677544,"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."}}