{"id":"W3160863507","doi":"10.1016/j.bioactmat.2021.04.022","title":"Multiplexed detection and differentiation of bacterial enzymes and bacteria by color-encoded sensor hydrogels","year":2021,"lang":"en","type":"article","venue":"Bioactive Materials","topic":"Biosensors and Analytical Detection","field":"Engineering","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Infection and Immunity","funders":"H2020 European Research Council; Ministère de l’Europe et des Affaires étrangères; Bundesministerium für Bildung und Frauen; Ministère de l'Europe et des Affaires Étrangères; Bundesministerium für Bildung und Forschung; Deutscher Akademischer Austauschdienst; Max-Buchner-Forschungsstiftung; European Research Council; Ministère de l'Enseignement supérieur, de la Recherche et de l'Innovation","keywords":"Self-healing hydrogels; Bacteria; Enzyme; Staphylococcus aureus; Escherichia coli; Strain (injury); Chemistry; Microbiology; Biochemistry; Biology; Gene; Polymer chemistry","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006806019,0.0001434372,0.0002674381,0.00004347532,0.00004478464,0.00007706315,0.00002006628,0.0001321233,0.0001634597],"category_scores_gemma":[0.00004228881,0.0001361682,0.00002540443,0.00006653174,0.00005500399,0.0001156442,0.00002515166,0.00003946455,0.000002688998],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000261483,"about_ca_system_score_gemma":0.000004332348,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002338623,"about_ca_topic_score_gemma":0.00001253303,"domain_scores_codex":[0.9992926,0.00007882732,0.0002348009,0.0001903054,0.00007140489,0.0001320692],"domain_scores_gemma":[0.9997052,0.00004599648,0.00006138017,0.00008327819,0.00005039895,0.00005377188],"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":[0.00007524541,0.00001758475,0.00003729457,0.00007911262,0.00005752318,9.666951e-7,0.00006536724,0.000001686706,0.9982116,0.00002373116,0.00001373165,0.001416122],"study_design_scores_gemma":[0.0004830326,0.00005171535,0.007959252,0.00001942018,0.00004112302,0.000008704926,0.00007831737,0.0007116444,0.9902,0.00004094252,0.0002637928,0.0001420412],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9985546,0.00009320058,0.0003827117,0.00001527718,0.0004832529,0.0001214062,0.000256563,0.00006722635,0.00002576294],"genre_scores_gemma":[0.999304,0.0001390128,0.0003460773,0.000004959624,0.00009750927,0.000009192311,0.00004934779,0.00001907784,0.00003082115],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00801162,"threshold_uncertainty_score":0.5552779,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007441762287408023,"score_gpt":0.192892990389011,"score_spread":0.1854512281016029,"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."}}