{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004449392,0.0004201326,0.0005244304,0.0003427127,0.0002658136,0.0004716842,0.0005173718,0.00060522,0.0005986503],"category_scores_gemma":[0.0004049549,0.0004314047,0.000315411,0.0002137573,0.000252716,0.0003470363,0.0004019643,0.0006202181,0.0004591236],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006848247,"about_ca_system_score_gemma":0.0003566521,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006691199,"about_ca_topic_score_gemma":0.001421797,"domain_scores_codex":[0.9995456,0.00004177444,0.00003190245,0.000131426,0.0001503419,0.00009885167],"domain_scores_gemma":[0.9998358,0.00004618092,0.00002956151,0.00002270523,0.00003588793,0.00002985463],"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":[0.00004937813,0.00001097402,0.00004949161,0.00001899558,0.00000224934,0.000008952295,0.000007621947,0.0000312093,0.9986443,0.00005980632,0.00003558358,0.001081338],"study_design_scores_gemma":[0.000006788506,0.00003028031,0.0003049653,0.000001810033,0.000003873748,0.00003625559,0.000003459257,0.0007255382,0.9983272,0.00002323194,0.0005335656,0.000003026124],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.896111,0.002140608,0.09769823,0.0003866238,0.00009189331,0.0001764649,0.0003256376,0.0006696431,0.002399934],"genre_scores_gemma":[0.9317172,0.0008240794,0.05991396,0.000252706,0.000040291,0.0001735247,0.0007477349,0.0001140301,0.006216289],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0006848247,"threshold_uncertainty_score":0.004968822,"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."}}