{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001390178,0.0002367866,0.0001764282,0.00008552502,0.000183326,0.0001111408,0.00007120168,0.0001695284,9.319217e-7],"category_scores_gemma":[0.00003706181,0.0002224215,0.00007982059,0.0001014481,0.00005909211,0.000005466347,0.00007541272,0.0001139693,5.547126e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002718576,"about_ca_system_score_gemma":0.00005658196,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005664069,"about_ca_topic_score_gemma":0.00007531858,"domain_scores_codex":[0.9987614,0.00001726624,0.000202313,0.0005072712,0.00006370428,0.0004481181],"domain_scores_gemma":[0.999638,0.00003675247,0.00003262595,0.0001666969,0.00003141629,0.00009452162],"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":[0.00004793047,0.000008258545,0.0001478633,0.0001343314,0.00006388492,0.000003389918,0.00005887408,0.0004393884,0.99582,0.00002770899,0.00003491973,0.003213421],"study_design_scores_gemma":[0.0004002767,0.0002709888,0.00005114761,0.00003991543,0.00005572556,0.0001313118,0.00003929041,0.134645,0.8595925,0.00006650561,0.004403189,0.0003041747],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9574431,0.02816933,0.01380621,0.00006664501,0.0001551721,0.0002690438,0.00004144901,0.00003831327,0.00001069352],"genre_scores_gemma":[0.9945819,0.001973417,0.00289142,0.00005103843,0.0003165166,0.00001255961,0.00004262187,0.00004933691,0.00008118132],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1362275,"threshold_uncertainty_score":0.9070085,"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."}}