{"id":"W4401304889","doi":"10.1016/j.bios.2024.116631","title":"Hydrophilic/hydrophobic modified microchip for detecting multiple gene doping candidates using CRISPR-Cas12a and RPA","year":2024,"lang":"en","type":"article","venue":"Biosensors and Bioelectronics","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Key Research and Development Program of China; Natural Science Foundation of Hubei Province; China Postdoctoral Science Foundation; World Anti-Doping Agency; National Natural Science Foundation of China","keywords":"CRISPR; Context (archaeology); Software portability; Multiplexing; Nanotechnology; Multiplex; Computational biology; Cas9; Microfluidics; Lab-on-a-chip; Plasmid; Computer science; Gene; Materials science; Bioinformatics; Biology; Genetics; Telecommunications","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":[0.000318492,0.0005936844,0.0005013472,0.0003547242,0.0002969234,0.0004525796,0.000947763,0.0007774904,0.001528386],"category_scores_gemma":[0.0004288696,0.0004185321,0.0003559506,0.0002428929,0.0003685273,0.0004269811,0.0005881047,0.0006740176,0.0008753001],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000420571,"about_ca_system_score_gemma":0.0003865746,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006334776,"about_ca_topic_score_gemma":0.001930429,"domain_scores_codex":[0.9993277,0.00004955623,0.00002462428,0.0002100254,0.0003058126,0.00008233471],"domain_scores_gemma":[0.9996287,0.00009231712,0.0001006647,0.00005457583,0.00007176526,0.00005202981],"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":[0.00002540181,0.00001517739,0.00008814087,0.00002186184,0.00000492329,0.00003629364,0.00001099843,0.00005601431,0.9977646,0.00008113601,0.00007243406,0.001823059],"study_design_scores_gemma":[0.000003553667,0.00006019062,0.0004122796,0.00000160034,0.000007896768,0.00006850079,0.000008398837,0.001139026,0.9972152,0.00002145487,0.001054296,0.000007571706],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8618292,0.001342716,0.1268032,0.0005075288,0.0002678831,0.0002305906,0.0006310355,0.002845734,0.005542098],"genre_scores_gemma":[0.8435868,0.0006506115,0.1406976,0.0004343223,0.00005017786,0.0002607935,0.0003428217,0.0001576743,0.01381914],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001528386,"threshold_uncertainty_score":0.005112946,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01223175100285435,"score_gpt":0.2909522979646239,"score_spread":0.2787205469617695,"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."}}