{"id":"W2606004721","doi":"10.1038/nnano.2017.56","title":"Peptide–MHC-based nanomedicines for autoimmunity function as T-cell receptor microclustering devices","year":2017,"lang":"en","type":"article","venue":"Nature Nanotechnology","topic":"Immunotherapy and Immune Responses","field":"Immunology and Microbiology","cited_by":165,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Centre for Phenogenomics; University of Toronto; McGill University; Hotchkiss Brain Institute; University of Calgary","funders":"","keywords":"In vivo; Major histocompatibility complex; Cell biology; Receptor; Ligand (biochemistry); Zebrafish; Autoimmunity; Antigen; In vitro; T-cell receptor; Peptide; Biology; Chemistry; Computational biology; Nanotechnology; Immune system; Immunology; T cell; Materials science; Biochemistry; Genetics","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":[0.0001792087,0.0002247104,0.0001558126,0.0001270052,0.0001499849,0.0003200309,0.0003221311,0.0003217904,0.0008977499],"category_scores_gemma":[0.0001929955,0.0001145775,0.0001263724,0.00006519445,0.0001948754,0.0004065064,0.0002926454,0.0002940116,0.0003943671],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002282014,"about_ca_system_score_gemma":0.00008978861,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007478616,"about_ca_topic_score_gemma":0.0001053867,"domain_scores_codex":[0.9999137,0.00001780781,0.000005013906,0.00002204409,0.00002460303,0.00001693367],"domain_scores_gemma":[0.9999225,0.00002878941,0.0000162525,0.000008842054,0.00001411422,0.000009594682],"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":[0.00009855946,0.00003214935,0.0001130335,0.00008957789,0.00001039497,0.00006042277,0.00003858797,0.0006921786,0.984862,0.001967937,0.0005379546,0.01149735],"study_design_scores_gemma":[0.00001543667,0.0001940415,0.0002382765,0.000006077289,0.00001254839,0.0001351554,0.00001400816,0.005024938,0.9881076,0.0004148733,0.005829884,0.000007057251],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9117328,0.01178099,0.06356084,0.0008117888,0.0002933671,0.0001117895,0.0001787295,0.0007428316,0.01078688],"genre_scores_gemma":[0.9802742,0.001358269,0.01508695,0.0002437777,0.00004310929,0.00004742443,0.00006277201,0.00005198114,0.002831594],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008977499,"threshold_uncertainty_score":0.00300324,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009120611547057967,"score_gpt":0.2617590500358851,"score_spread":0.2526384384888272,"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."}}