{"id":"W2893314280","doi":"10.1002/pep2.24095","title":"Amyloid self‐assembling peptides: Potential applications in nanovaccine engineering and biosensing","year":2018,"lang":"en","type":"article","venue":"Peptide Science","topic":"Supramolecular Self-Assembly in Materials","field":"Materials Science","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"PROTEO; Université du Québec à Montréal","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; International Development Research Centre","keywords":"Biomolecule; Biocompatibility; Nanotechnology; Amyloid fibril; Biosensor; Amyloid (mycology); Peptide; Amyloid disease; Chemistry; Materials science; Biophysics; Biochemistry; Biology; Amyloid β","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":[0.001733793,0.0002260461,0.0002500014,0.0004513011,0.0004130323,0.0005249988,0.0005721332,0.00006700463,0.00004049594],"category_scores_gemma":[0.000273428,0.0002184346,0.00002991171,0.001261206,0.0003365799,0.0007204604,0.0003501898,0.00009471404,0.0001350353],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001366059,"about_ca_system_score_gemma":0.0001541094,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001062573,"about_ca_topic_score_gemma":0.00002790277,"domain_scores_codex":[0.9975719,0.00004634845,0.000419994,0.0007717032,0.0004930045,0.0006970277],"domain_scores_gemma":[0.9988889,0.00008475881,0.0001275031,0.0004989199,0.0002260031,0.0001739917],"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":[0.000004797751,0.0000259245,0.000573891,0.00002008579,0.000001546922,0.00000698965,0.0003799264,0.0001361902,0.9982088,0.0004721344,0.0000113816,0.0001582859],"study_design_scores_gemma":[0.0002510708,0.00005310089,0.009170879,0.00006376632,0.00001130544,0.0000686198,0.0002275464,0.006560193,0.9827759,0.0001858718,0.0003300661,0.0003017409],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9946924,0.00005694231,0.003748944,0.00008987079,0.0005090678,0.0003408068,0.000003937778,0.0002503262,0.0003077465],"genre_scores_gemma":[0.9534981,0.00001140028,0.04609937,0.00007468812,0.0002484935,0.00002712042,0.000001487144,0.00002496971,0.00001435742],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04235043,"threshold_uncertainty_score":0.8907505,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006780844947514773,"score_gpt":0.2466027526657782,"score_spread":0.2398219077182634,"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."}}