{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006161272,0.0004721138,0.0003901171,0.0001531971,0.0001410098,0.0004093742,0.0002817935,0.0004246099,0.0003537806],"category_scores_gemma":[0.0007227486,0.0001744342,0.0003368903,0.000165366,0.0002794514,0.0003383734,0.0002294956,0.0006250261,0.0001877256],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002433177,"about_ca_system_score_gemma":0.0001654607,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005149973,"about_ca_topic_score_gemma":0.0005713595,"domain_scores_codex":[0.9994588,0.0001465115,0.00004180836,0.00009994681,0.0001700448,0.00008279864],"domain_scores_gemma":[0.9996964,0.0001242021,0.00006993075,0.00003140276,0.00006019951,0.00001794046],"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":[0.0000423507,0.00001710244,0.0002001558,0.00003690197,0.000007105335,0.00001970379,0.00002518306,0.0001270648,0.998428,0.00003007245,0.00001142136,0.001054999],"study_design_scores_gemma":[0.000001972259,0.0001186338,0.001398423,0.000002373614,0.00001312794,0.00007310791,0.00001679445,0.002155744,0.9958932,0.000020534,0.0002999672,0.000006074337],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9785663,0.001383549,0.01915928,0.00008329667,0.0000301104,0.00003671457,0.00008563105,0.00007293902,0.0005822245],"genre_scores_gemma":[0.9859778,0.001082236,0.01184178,0.0001008357,0.00001293815,0.00003295397,0.0001441541,0.00001925955,0.0007880681],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0006161272,"threshold_uncertainty_score":0.003258467,"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."}}