{"id":"W2592883798","doi":"10.1021/acsbiomaterials.7b00037","title":"Genetically Encoded Toolbox for Glycocalyx Engineering: Tunable Control of Cell Adhesion, Survival, and Cancer Cell Behaviors","year":2017,"lang":"en","type":"article","venue":"ACS Biomaterials Science & Engineering","topic":"Glycosylation and Glycoproteins Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":58,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of General Medical Sciences; National Institutes of Health; National Cancer Institute; Canadian Institutes of Health Research; Division of Graduate Education; John S. and James L. Knight Foundation","keywords":"Glycocalyx; Cell biology; Circulating tumor cell; Cell adhesion; Cancer cell; Glycobiology; Cell; Biology; Chemistry; Cancer; Glycan; Glycoprotein; Biochemistry; Metastasis; Genetics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005531887,0.0001788064,0.0002280681,0.00007462015,0.000215519,0.0001929677,0.0005389376,0.0001170193,0.00002248152],"category_scores_gemma":[0.0002570527,0.000169107,0.00004833074,0.00007003888,0.0001876606,0.00003434949,0.0001628295,0.00002534688,0.000001522565],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002418872,"about_ca_system_score_gemma":0.0001173359,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001515014,"about_ca_topic_score_gemma":0.000009475893,"domain_scores_codex":[0.9986116,0.00001099387,0.000274072,0.0004033167,0.0002550266,0.0004449934],"domain_scores_gemma":[0.9989746,0.00001938029,0.0001156329,0.0004985562,0.0002197568,0.0001720972],"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":[0.00003657112,0.00002661362,0.0003803946,0.00007022338,0.000005813743,9.847347e-7,0.00001808302,0.0008056801,0.9984295,0.00008648777,0.00001433378,0.0001253004],"study_design_scores_gemma":[0.0008013631,0.0001864083,0.003346594,0.00002071231,0.00001480544,0.000001783115,0.00001349422,0.0023812,0.9913111,0.000002318554,0.001720468,0.0001997456],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.99107,0.0004362099,0.007487229,0.00003971379,0.0003846107,0.0004304014,0.00007914108,0.00001752602,0.00005513676],"genre_scores_gemma":[0.9962243,0.0001950917,0.003242298,0.000008700475,0.0001031773,0.0001042213,0.00001028695,0.00002531721,0.00008655782],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007118406,"threshold_uncertainty_score":0.6895984,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01354346729437225,"score_gpt":0.2780951644066366,"score_spread":0.2645516971122643,"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."}}