{"id":"W4382751588","doi":"10.1021/acs.analchem.2c05032","title":"Strategies to Improve Multi-enzyme Compatibility and Coordination in One-Pot SHERLOCK","year":2023,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Agency of Canada; University of Manitoba; Canadian Food Inspection Agency","funders":"Canadian Institutes of Health Research; Government of Canada; Research Manitoba","keywords":"Recombinase Polymerase Amplification; Chemistry; Computational biology; Polymerase chain reaction; Polymerase; Enzyme; Loop-mediated isothermal amplification; Recombinase; Nucleic acid; Nanotechnology; Combinatorial chemistry; DNA; Biochemical engineering; Biochemistry; Recombination; Gene; Biology; Materials science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001362808,0.0001093636,0.000125532,0.00002407345,0.00002255179,0.00003069776,0.00008593807,0.000113278,0.00001343934],"category_scores_gemma":[0.0001103324,0.0001186854,0.00003324543,0.0001691601,0.00003855939,0.000003319689,0.0001010441,0.00009003608,0.000008354962],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001518833,"about_ca_system_score_gemma":0.00002827785,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002837401,"about_ca_topic_score_gemma":0.00004368463,"domain_scores_codex":[0.999211,0.000008542933,0.0001635207,0.0003258892,0.00008307074,0.000208025],"domain_scores_gemma":[0.9996178,0.00001558757,0.00001434809,0.0002084352,0.00003674648,0.0001070459],"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":[0.00001941265,0.00004824745,0.004710505,0.00008833694,0.00001903841,0.000003393086,0.0000391102,0.001029822,0.9926703,0.00002054987,0.0009130476,0.0004382608],"study_design_scores_gemma":[0.0008093657,0.00009121616,0.05834288,0.00002563882,0.00002077719,0.000003390523,0.0005574945,0.04300218,0.893527,0.00009002353,0.003171367,0.0003587062],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9948106,0.0000677617,0.003712345,0.0003043428,0.00003189285,0.00009330078,0.00001063915,0.00002489973,0.0009442666],"genre_scores_gemma":[0.9982488,0.00001213471,0.0003080445,0.00003795148,0.0001003582,0.00001278665,0.0000580966,0.00001099324,0.001210799],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09914332,"threshold_uncertainty_score":0.4839849,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01885885875694418,"score_gpt":0.3250750960639283,"score_spread":0.3062162373069841,"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."}}