{"id":"W3126571486","doi":"10.1021/acsbiomaterials.0c01552","title":"Fast Thermoresponsive Poly(oligoethylene glycol methacrylate) (POEGMA)-Based Nanostructured Hydrogels for Reversible Tuning of Cell Interactions","year":2021,"lang":"en","type":"article","venue":"ACS Biomaterials Science & Engineering","topic":"Hydrogels: synthesis, properties, applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Methacrylate; Self-healing hydrogels; Materials science; Ethylene glycol; Thermoresponsive polymers in chromatography; Lower critical solution temperature; Polymer chemistry; PEG ratio; Chemical engineering; Biophysics; Polymer; Chemistry; Organic chemistry; Copolymer; Composite material","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":[0.0001195412,0.0003187876,0.0001119596,0.0001279449,0.0000641927,0.0001617301,0.0001706855,0.0002439603,0.0005537816],"category_scores_gemma":[0.0001161,0.0001595246,0.0001341177,0.00007245885,0.000147808,0.0002954992,0.0001870025,0.0003208805,0.0002193908],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001356031,"about_ca_system_score_gemma":0.0001202164,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001249246,"about_ca_topic_score_gemma":0.0003967384,"domain_scores_codex":[0.9999493,0.000004614929,0.000003411186,0.0000142021,0.00001694472,0.00001145126],"domain_scores_gemma":[0.9999149,0.00002451025,0.00003050673,0.000008373117,0.000007067863,0.00001471563],"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":[0.000003967457,0.000003073838,0.000009472702,0.000006622814,4.550428e-7,0.00001082415,0.000002277392,0.00003642301,0.9992827,0.00003432319,0.000007958609,0.0006020791],"study_design_scores_gemma":[0.000004194709,0.00004842146,0.0003511183,0.000001727682,0.000002283827,0.00008380141,0.000002882186,0.0010323,0.9974631,0.00003589387,0.000970865,0.000003484294],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9461426,0.002567094,0.04717236,0.0001573756,0.0001086842,0.00009019874,0.000268605,0.0003737426,0.003119316],"genre_scores_gemma":[0.9734558,0.0009613204,0.02275798,0.00008311262,0.0000182108,0.00005913007,0.0001054516,0.0000355961,0.002523265],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0005537816,"threshold_uncertainty_score":0.001852572,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01253501229172552,"score_gpt":0.2469236516002107,"score_spread":0.2343886393084852,"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."}}