{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001910721,0.0005197483,0.0002346973,0.0002590461,0.0001286039,0.0004129499,0.0003411895,0.0004834899,0.0007040241],"category_scores_gemma":[0.0002686547,0.0002038757,0.0002327665,0.0001894446,0.0003549402,0.0004116154,0.0004778543,0.0009336785,0.000259933],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003812103,"about_ca_system_score_gemma":0.0002334039,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003231274,"about_ca_topic_score_gemma":0.0004585137,"domain_scores_codex":[0.999882,0.00001500065,0.000009902115,0.000025497,0.0000480417,0.00001953678],"domain_scores_gemma":[0.9998553,0.00002733678,0.00005601596,0.00001801454,0.00001242954,0.00003094307],"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":[0.00001095821,0.000009768581,0.0000276353,0.00002285874,0.000002573,0.00002669979,0.000007967965,0.0002414898,0.997251,0.0004962016,0.00004934372,0.001853408],"study_design_scores_gemma":[0.00001561546,0.0001246846,0.000398444,0.000009934201,0.00001221049,0.0001265011,0.00001189053,0.003275666,0.9884763,0.0002292523,0.007305525,0.00001394345],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8285308,0.003581509,0.1581887,0.0008499119,0.0002758829,0.0002052789,0.001088707,0.001713433,0.005565745],"genre_scores_gemma":[0.9036062,0.003430451,0.08732219,0.0002127761,0.00003637913,0.0001968968,0.0006067713,0.0002793362,0.004308938],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0007040241,"threshold_uncertainty_score":0.002765954,"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."}}