{"id":"W2522746526","doi":"10.1007/s12274-016-1262-z","title":"NanoHDA: A nanoparticle-assisted isothermal amplification technique for genotyping assays","year":2016,"lang":"en","type":"article","venue":"Nano Research","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"QIMR Berghofer Medical Research Institute","keywords":"Recombinase Polymerase Amplification; Loop-mediated isothermal amplification; Amplicon; Colloidal gold; DNA; Denaturation (fissile materials); Helicase; Molecular biology; Polymerase chain reaction; Genotyping; Multiple displacement amplification; Chemistry; Primer (cosmetics); Nanoparticle; Biophysics; Nanotechnology; Materials science; Genotype; Gene; Biology; Biochemistry; DNA extraction; RNA; Nuclear chemistry","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":[0.001427064,0.0001325568,0.0001404441,0.0001413523,0.0002584775,0.0000339997,0.0002607192,0.0002310481,0.000005779233],"category_scores_gemma":[0.0005015603,0.00009081245,0.0001124258,0.0002864124,0.0002132972,0.000007440637,0.000112997,0.00008419099,0.00001268737],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006707974,"about_ca_system_score_gemma":0.0001056781,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001081882,"about_ca_topic_score_gemma":0.00002208189,"domain_scores_codex":[0.9983102,0.0001881966,0.0002344092,0.0004972906,0.000272682,0.0004972143],"domain_scores_gemma":[0.9986784,0.00008665859,0.00006831432,0.0005325517,0.0005465706,0.00008749657],"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":[0.0001490213,0.00006153669,0.0002118204,0.00001249345,0.00002601995,7.987016e-7,0.000005433418,1.288951e-7,0.9631014,0.0003412589,0.00158627,0.03450378],"study_design_scores_gemma":[0.0003165925,0.0002951833,0.0003409829,0.00004145844,0.000007936527,0.00001042736,0.00001596698,0.000008664839,0.9607517,0.000595262,0.03746269,0.0001531417],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6711183,0.0004399005,0.3220312,0.002775226,0.0000815123,0.00197656,0.00009287484,0.0001801725,0.001304255],"genre_scores_gemma":[0.9810085,0.0001272881,0.01600347,0.00006226098,0.0001698475,0.000338154,0.00003126671,0.00003376096,0.002225509],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3098901,"threshold_uncertainty_score":0.3703224,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06720974743587926,"score_gpt":0.3883331430425865,"score_spread":0.3211233956067072,"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."}}