{"id":"W4281666421","doi":"10.1021/acs.nanolett.2c01018","title":"Nanoparticle Amplification Labeling for High-Performance Magnetic Cell Sorting","year":2022,"lang":"en","type":"article","venue":"Nano Letters","topic":"Microfluidic and Bio-sensing Technologies","field":"Engineering","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Canada First Research Excellence Fund","keywords":"Magnetic nanoparticles; Cell sorting; Immunomagnetic separation; Magnetic separation; Cell; Sorting; Nanoparticle; Intracellular; Nanotechnology; Chemistry; Biophysics; Materials science; Cell biology; Computer science; Biology; Biochemistry; Chromatography","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004252718,0.0006189729,0.0003062288,0.0004562113,0.0003190585,0.0004300934,0.0004171662,0.0006301951,0.001872662],"category_scores_gemma":[0.000544595,0.0002902322,0.0002988042,0.0002219672,0.0003806035,0.0003457285,0.0004023239,0.0007233939,0.001304576],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006416549,"about_ca_system_score_gemma":0.000427423,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005554778,"about_ca_topic_score_gemma":0.0010141,"domain_scores_codex":[0.9994081,0.0001096204,0.00003382254,0.0001339132,0.0002478723,0.00006658393],"domain_scores_gemma":[0.9997814,0.00008335945,0.00003572538,0.00002220879,0.00006001728,0.00001713787],"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":[0.00002717268,0.00002312692,0.00005538127,0.00006488511,0.000004935514,0.00003773747,0.00002885854,0.0003252251,0.9893281,0.001200612,0.0004242309,0.008479757],"study_design_scores_gemma":[0.000008633592,0.00005230003,0.0001354998,0.000005611111,0.000006457413,0.00009922872,0.000004987237,0.006302299,0.983807,0.0003019077,0.009265732,0.00001030051],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1860366,0.00356291,0.7938174,0.001022923,0.0003862773,0.0005167359,0.0002621915,0.0020114,0.01238351],"genre_scores_gemma":[0.5942509,0.002708077,0.3868113,0.0006759829,0.0001669357,0.001026311,0.0005995337,0.0002034268,0.01355749],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001872662,"threshold_uncertainty_score":0.006264687,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00895974039822623,"score_gpt":0.1777766684092081,"score_spread":0.1688169280109819,"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."}}