{"id":"W4377094532","doi":"10.1002/advs.202300656","title":"Repurposing CRISPR/Cas to Discover SARS‐CoV‐2 Detecting and Neutralizing Aptamers","year":2023,"lang":"en","type":"article","venue":"Advanced Science","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Beijing Municipal Natural Science Foundation; Shenzhen Municipal Science and Technology Innovation Council; Guangdong Science and Technology Department; University of Toronto; Shenzhen University; National Natural Science Foundation of China","keywords":"Aptamer; CRISPR; Systematic evolution of ligands by exponential enrichment; Computational biology; RNA; Biology; Repurposing; Context (archaeology); Virology; Chemistry; Molecular biology; Gene; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002466616,0.0001042662,0.00007889503,0.00009639759,0.0002160764,0.00006063464,0.0001710802,0.00002903533,5.343024e-7],"category_scores_gemma":[0.0002167987,0.0001057066,0.00002521559,0.0005699624,0.0001116083,0.00001662205,0.0002121097,0.00005631968,0.000006758734],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001453675,"about_ca_system_score_gemma":0.00003181961,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001298617,"about_ca_topic_score_gemma":0.00002828971,"domain_scores_codex":[0.9989265,0.00000736495,0.0001122838,0.000446317,0.0001366547,0.0003709031],"domain_scores_gemma":[0.9995911,0.00001270027,0.00002419221,0.0002449649,0.00003501206,0.00009200355],"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":[0.000006170926,0.000001788523,0.0003783238,0.000006444373,0.000001923503,0.000002646226,0.0001647983,0.004441944,0.9737628,0.00002227783,0.0000325468,0.02117833],"study_design_scores_gemma":[0.0001086588,0.00007438914,0.003846969,0.00002052565,0.000003160585,0.00001338593,0.0002947259,0.001023351,0.9887016,0.00004042775,0.005711669,0.0001611053],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9757456,0.0002277945,0.02325448,0.0001088633,0.0002280915,0.0001152462,0.000001904325,0.00004057768,0.0002774909],"genre_scores_gemma":[0.9939529,0.0001084806,0.005553474,0.0002089011,0.00005128438,0.00001053024,0.000001892047,0.00001416869,0.00009831087],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02101723,"threshold_uncertainty_score":0.4310592,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01378875731499864,"score_gpt":0.3595580548453265,"score_spread":0.3457692975303279,"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."}}