{"id":"W3188527509","doi":"10.1038/s41467-021-23117-9","title":"Ultra-strong bio-glue from genetically engineered polypeptides","year":2021,"lang":"en","type":"article","venue":"Nature Communications","topic":"Surgical Sutures and Adhesives","field":"Medicine","cited_by":197,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Canada Research Chairs; Russian Foundation for Basic Research; Lomonosov Moscow State University; Volkswagen Foundation; K. C. Wong Education Foundation; Chinese Academy of Sciences; Salt Science Research Foundation","keywords":"Adhesion; Adhesive; Bioadhesive; Wound healing; Covalent bond; Nanotechnology; GLUE; Biocompatible material; Cationic polymerization; Materials science; Chemistry; Biophysics; Biomedical engineering; Polymer chemistry; Composite material; Drug delivery; Organic chemistry","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":[0.00005080958,0.0001204017,0.0002093737,0.00003775535,0.0001303441,0.0000314078,0.0003570513,0.0002782236,0.0004495154],"category_scores_gemma":[0.0004605244,0.0001023047,0.0001553094,0.0002632896,0.0001021522,0.00003379477,0.000118715,0.0008800675,0.00005803071],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002977632,"about_ca_system_score_gemma":0.00009803872,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002658176,"about_ca_topic_score_gemma":0.00009198953,"domain_scores_codex":[0.999195,0.00007544409,0.0001919262,0.0001937391,0.0001686037,0.0001752316],"domain_scores_gemma":[0.9975733,0.0005137486,0.00003432807,0.001538054,0.0001800592,0.0001605197],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001418553,0.0017855,0.06501466,0.00005166,0.001034423,0.000183385,0.0007124908,0.00003403991,0.7512295,0.1239087,0.02489726,0.03100658],"study_design_scores_gemma":[0.001137167,0.00005623988,0.325171,0.0001478989,0.000254856,0.00006885272,0.0004583774,0.0003903984,0.06673223,0.0006630523,0.6046128,0.0003071676],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6197352,0.2036246,0.0004861398,0.09470938,0.0006365392,0.0005434634,0.0003338872,0.0004536015,0.07947715],"genre_scores_gemma":[0.9751871,0.002083294,0.01967975,0.001642348,0.0001898912,0.00001407934,0.0005553971,0.00001899367,0.0006291886],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6844972,"threshold_uncertainty_score":0.4921878,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02062334701473887,"score_gpt":0.2976408644542306,"score_spread":0.2770175174394917,"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."}}