{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001349251,0.00008862474,0.00008370616,0.00006052282,0.0002120612,0.00002035718,0.0001431273,0.00002798482,0.00002228533],"category_scores_gemma":[0.000007407222,0.00009592114,0.00002236722,0.0001589406,0.00002371149,0.00004735804,0.00004620321,0.00008634864,0.00001515923],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007341679,"about_ca_system_score_gemma":0.000005182322,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005883595,"about_ca_topic_score_gemma":2.507858e-7,"domain_scores_codex":[0.9993397,0.000008598539,0.0001675617,0.0001388901,0.00009558118,0.0002496704],"domain_scores_gemma":[0.99974,0.0000264484,0.00003094723,0.0001759667,0.00001123643,0.00001540393],"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":[0.000004418114,0.000008088819,0.0002694771,0.00003939336,0.000002606638,7.535572e-7,0.00006072532,0.004305983,0.9785965,0.00006012442,0.007385998,0.009265916],"study_design_scores_gemma":[0.0002550523,0.00005331246,0.000178926,0.000006104882,0.000010469,0.000003217174,0.00009074304,0.004462824,0.9829349,0.00003472287,0.0118212,0.0001485301],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9935403,0.0005578884,0.003943344,0.0008351378,0.0003094892,0.0001639693,0.000007166501,0.0006073424,0.00003533887],"genre_scores_gemma":[0.9939583,0.00003945491,0.005397554,0.0004227583,0.00003511578,0.00005736034,0.00001157239,0.00002446271,0.00005339877],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009117386,"threshold_uncertainty_score":0.391155,"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."}}