{"id":"W4387149224","doi":"10.1016/j.mtchem.2023.101722","title":"Tissue adhesive hydrogel based on upcycled proteins and plant polyphenols for enhanced wound healing","year":2023,"lang":"en","type":"article","venue":"Materials Today Chemistry","topic":"Silk-based biomaterials and applications","field":"Materials Science","cited_by":26,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal; Centre Hospitalier Universitaire Sainte-Justine","funders":"Institut TransMedTech; Natural Sciences and Engineering Research Council of Canada; Wallonie-Bruxelles International; Université de Montréal; Fonds de Recherche du Québec - Santé; Innoviris; Fonds De La Recherche Scientifique - FNRS","keywords":"Wound healing; Adhesive; Pyrogallol; Biocompatibility; Keratin; Wool; Tannic acid; Bioadhesive; Materials science; Polyphenol; Self-healing hydrogels; SILK; Chemistry; Polymer chemistry; Antioxidant; Drug delivery; Composite material; Nanotechnology; Biochemistry; Organic chemistry; Surgery","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":[0.00008884331,0.0002839399,0.0001543843,0.0001407711,0.00008394287,0.0001869889,0.0001344403,0.0002372291,0.0008634672],"category_scores_gemma":[0.0000597079,0.0001076147,0.0001844116,0.0001184676,0.00009204382,0.0002035189,0.0001595421,0.0003242572,0.0001488393],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001672805,"about_ca_system_score_gemma":0.0001052729,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002561937,"about_ca_topic_score_gemma":0.0006836825,"domain_scores_codex":[0.9999472,0.00000604717,0.000002854101,0.00001051344,0.00001325856,0.00002010225],"domain_scores_gemma":[0.9999585,0.000008650992,0.00001151463,0.000002973266,0.000005086146,0.0000131109],"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":[0.00002002328,0.00000821724,0.00001393556,0.00003053078,0.00000210911,0.00001901722,0.000004635741,0.00002881818,0.9989335,0.00004683023,0.00001987706,0.0008724497],"study_design_scores_gemma":[0.000004749336,0.00006279993,0.0004418064,0.000002812085,0.000008611689,0.0000291017,0.000004569291,0.0004054291,0.998256,0.00001137596,0.0007701096,0.000002713825],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9859349,0.004768341,0.006842653,0.00008082851,0.00008299711,0.00003294635,0.0001204005,0.00009681327,0.002040096],"genre_scores_gemma":[0.9919422,0.001552585,0.003995361,0.00006657869,0.00001382061,0.00002741059,0.0000729605,0.00002111781,0.002308007],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008634672,"threshold_uncertainty_score":0.00288856,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01554972526325721,"score_gpt":0.2638994517761549,"score_spread":0.2483497265128977,"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."}}