{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001906117,0.000464215,0.0002856579,0.0002361509,0.0001596901,0.0004725,0.0002606403,0.0003435149,0.001241514],"category_scores_gemma":[0.0001551722,0.0001662617,0.0002730778,0.0001671906,0.000255963,0.0001865387,0.0002524663,0.000490504,0.0005315543],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003767968,"about_ca_system_score_gemma":0.0003273633,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004670437,"about_ca_topic_score_gemma":0.000790265,"domain_scores_codex":[0.9997801,0.00002526088,0.00001584844,0.00006068149,0.00009424462,0.00002380481],"domain_scores_gemma":[0.9999362,0.00001336495,0.000016303,0.00001161617,0.00001106275,0.00001133493],"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":[0.00002967906,0.00002007845,0.0001132744,0.00003166683,0.000006341627,0.0000539082,0.000009293033,0.0004589789,0.9910954,0.0005415936,0.0001361574,0.007503502],"study_design_scores_gemma":[0.00001262633,0.0001760336,0.0004363784,0.00000356075,0.00001357587,0.0002616095,0.00001161095,0.004133863,0.9881061,0.0002598604,0.006575356,0.00000944583],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7772065,0.004187779,0.1989799,0.0006534148,0.0002297038,0.0003293067,0.0007404657,0.002616622,0.01505625],"genre_scores_gemma":[0.8848805,0.00242161,0.09937092,0.0003417919,0.00004028542,0.0001002592,0.0007485189,0.0001060351,0.01198993],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001241514,"threshold_uncertainty_score":0.004153252,"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."}}