{"id":"W1964354687","doi":"10.1016/j.ab.2009.11.006","title":"Bacteria capture, lysate clearance, and plasmid DNA extraction using pH-sensitive multifunctional magnetic nanoparticles","year":2009,"lang":"en","type":"article","venue":"Analytical Biochemistry","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":52,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Sichuan Agricultural University; Sichuan University","keywords":"Lysis; Magnetic nanoparticles; Chromatography; Centrifugation; DNA extraction; DNA; Plasmid; Chemistry; Extraction (chemistry); Escherichia coli; Magnetic separation; Nanoparticle; genomic DNA; Alkaline lysis; Plasmid preparation; Bacteria; Nanotechnology; Materials science; Biochemistry; Biology; DNA vaccination; Polymerase chain reaction","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":[0.0001156187,0.0002404794,0.0002140672,0.00004261613,0.0001290734,0.00004935513,0.00007560993,0.0002615918,0.000008449731],"category_scores_gemma":[0.0001031505,0.0002225281,0.0001172193,0.0001472365,0.0002283489,0.00001036454,0.00004676655,0.0001464336,0.000003483792],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003042651,"about_ca_system_score_gemma":0.00003943354,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008031917,"about_ca_topic_score_gemma":0.000002828535,"domain_scores_codex":[0.9986869,0.00003492845,0.000264631,0.0005466525,0.0001653636,0.0003014957],"domain_scores_gemma":[0.9993434,0.00001727782,0.00009159748,0.0002689793,0.0001246803,0.0001540947],"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":[0.0001985213,0.00007824897,0.000163049,0.000008526119,0.00003967257,0.000011506,0.000004369667,0.000005793055,0.9968947,0.00001814675,0.0002587691,0.002318769],"study_design_scores_gemma":[0.0003006176,0.0001481473,0.002637885,0.00002738753,0.0001042101,0.0001259931,0.00005433348,0.002804477,0.9926376,0.00008404015,0.0007809628,0.0002943184],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9984093,0.0001829084,0.0005574054,0.0002472617,0.00003501731,0.000068921,0.00002355193,0.00004069914,0.0004349336],"genre_scores_gemma":[0.9944081,0.0001072184,0.004249364,0.0004827707,0.0002184114,0.000001442142,0.0001001805,0.00001477491,0.0004176781],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004257001,"threshold_uncertainty_score":0.9074435,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009740945496104438,"score_gpt":0.2693555508926933,"score_spread":0.2596146053965889,"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."}}