{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002161136,0.002008615,0.001463797,0.0007042583,0.0004084348,0.001297165,0.001703222,0.001248468,0.003119378],"category_scores_gemma":[0.001866896,0.0009604526,0.0009418892,0.0007700774,0.000718129,0.001693693,0.001499285,0.002891244,0.003783313],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005731147,"about_ca_system_score_gemma":0.00070441,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003661716,"about_ca_topic_score_gemma":0.0009213357,"domain_scores_codex":[0.9980453,0.0003464146,0.0002106267,0.0005961819,0.0005275423,0.0002739547],"domain_scores_gemma":[0.9992018,0.0001729951,0.0002859889,0.0001279584,0.0001011796,0.0001101238],"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":[0.000233858,0.0004642425,0.0003151607,0.001036837,0.0001023354,0.0002755655,0.000319233,0.001709718,0.955958,0.004205306,0.001613544,0.03376614],"study_design_scores_gemma":[0.00005554521,0.0004911377,0.0002687809,0.0000345273,0.00004948705,0.0003187712,0.00005622483,0.004968705,0.9719721,0.0005480702,0.02116404,0.00007271108],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2588776,0.008313884,0.7133944,0.001052732,0.0005452199,0.001519066,0.000580796,0.005500104,0.01021623],"genre_scores_gemma":[0.5115734,0.005987705,0.4637088,0.0008793518,0.00009460895,0.00208736,0.001195629,0.0009304935,0.01354265],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003119378,"threshold_uncertainty_score":0.01142931,"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."}}