{"id":"W4409980852","doi":"10.1007/s40820-025-01730-3","title":"Universal Amplification-Free RNA Detection by Integrating CRISPR-Cas10 with Aptameric Graphene Field-Effect Transistor","year":2025,"lang":"en","type":"article","venue":"Nano-Micro Letters","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Natural Science Foundation of Qingdao; State Key Laboratory of Mycology; National Natural Science Foundation of China; Shandong University","keywords":"CRISPR; Nucleic acid; Biosensor; Locked nucleic acid; RNA; Effector; Biology; Computational biology; DNA; Nanotechnology; Materials science; Genetics; Gene; Cell biology","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":[0.0001102638,0.0002101864,0.0001831845,0.0001142567,0.0001559592,0.000032746,0.0002148132,0.0001429062,0.000001484988],"category_scores_gemma":[0.00006025447,0.0001739321,0.0001267409,0.0003263094,0.0001127615,0.000007636907,0.00003256207,0.0001497685,7.862268e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005104382,"about_ca_system_score_gemma":0.00002330166,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001238016,"about_ca_topic_score_gemma":0.0001262074,"domain_scores_codex":[0.9990093,0.00007902676,0.0001782527,0.0004241524,0.00009747891,0.0002118315],"domain_scores_gemma":[0.9993237,0.00003681911,0.00009950981,0.0004368688,0.00006488572,0.00003819207],"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":[0.0001660869,0.00002096984,0.0002786366,0.00001528726,0.00009839923,0.000001860963,0.0000162811,0.000002186351,0.9739385,0.000004536375,0.009259104,0.01619821],"study_design_scores_gemma":[0.0004399498,0.0002761336,0.00005811871,0.00003095667,0.00009183675,0.000006052516,0.00004897542,0.00001684556,0.9797935,0.00000809572,0.01902545,0.0002040602],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6683787,0.0001933256,0.3291427,0.001728123,0.00008222462,0.0001805456,0.00001531059,0.00007472523,0.0002043845],"genre_scores_gemma":[0.9912429,0.00005848359,0.005833304,0.0024899,0.00005303824,0.00001466746,0.00007733182,0.00001737054,0.0002130018],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3233094,"threshold_uncertainty_score":0.7092745,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002365233120547279,"score_gpt":0.2200073734214637,"score_spread":0.2176421403009164,"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."}}