{"id":"W2335017003","doi":"10.1021/acs.biomac.5b00219","title":"Development of a Polyester Coating Combining Antithrombogenic and Cell Adhesive Properties: Influence of Sequence and Surface Density of Adhesion Peptides","year":2015,"lang":"en","type":"article","venue":"Biomacromolecules","topic":"Nanofabrication and Lithography Techniques","field":"Engineering","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Montreal Heart Institute; Polytechnique Montréal","funders":"Institut de Cardiologie de Montréal; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Polyester; Adhesive; Adhesion; Coating; Sequence (biology); Cell adhesion; Materials science; Polymer chemistry; Polymer science; Chemistry; Chemical engineering; Nanotechnology; Composite material; Biochemistry; Layer (electronics)","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":[0.0002333756,0.0006797726,0.0003294164,0.0002278683,0.00009012445,0.0003301858,0.0002367562,0.0004316451,0.000427754],"category_scores_gemma":[0.0002704162,0.0001946258,0.0002383035,0.0001620324,0.000163288,0.0003704675,0.0002365328,0.0004025247,0.0002603544],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001546132,"about_ca_system_score_gemma":0.0001112478,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001631732,"about_ca_topic_score_gemma":0.000269152,"domain_scores_codex":[0.9998425,0.00002492809,0.00001569496,0.00004512199,0.00004750489,0.00002414094],"domain_scores_gemma":[0.9997926,0.00004938111,0.00006810681,0.00002140484,0.00003307074,0.00003551825],"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":[0.00001027024,0.000006604717,0.00002908801,0.00002334525,0.000002882213,0.00001199892,0.000002446546,0.0000300633,0.9989843,0.000009178823,0.000004198662,0.0008856431],"study_design_scores_gemma":[0.000004184162,0.00008422657,0.0005181781,0.00000266308,0.00001114172,0.00009969002,0.000002921509,0.0003647165,0.9984675,0.000004983793,0.0004372374,0.000002518724],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9745018,0.00404227,0.02003663,0.00005485646,0.00005461164,0.0001011349,0.0001180882,0.0001198203,0.0009708584],"genre_scores_gemma":[0.9593769,0.003096488,0.03532549,0.0001033551,0.00002930089,0.00006888484,0.0002560994,0.00004511576,0.00169836],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0006797726,"threshold_uncertainty_score":0.001430988,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03066840837393358,"score_gpt":0.236951528579054,"score_spread":0.2062831202051204,"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."}}