{"id":"W3202185491","doi":"10.1101/2021.10.06.463387","title":"Tracking the fates of iron-labeled tumor cells in vivo using Magnetic Particle Imaging","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Characterization and Applications of Magnetic Nanoparticles","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Basque Center for Applied Mathematics","keywords":"Magnetic resonance imaging; In vivo; Metastasis; Cell; Preclinical imaging; Pathology; Cancer cell; Magnetic particle imaging; Contrast (vision); Cancer; Cancer research; Chemistry; Medicine; Biology; Radiology; Magnetic nanoparticles; Materials science; Computer science; Nanotechnology; Nanoparticle; Biochemistry; Internal medicine","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002926775,0.0002985317,0.0003527264,0.0001081797,0.00006739505,0.0002030506,0.0003779574,0.00009219121,0.0001452923],"category_scores_gemma":[0.00005645656,0.0003047366,0.00007569025,0.000548032,0.00009582799,0.0001255053,0.0001893861,0.0003257252,0.00000853843],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001000118,"about_ca_system_score_gemma":0.0001026809,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003215776,"about_ca_topic_score_gemma":0.000003583775,"domain_scores_codex":[0.9983692,0.00007240363,0.000593247,0.0003872261,0.0002090762,0.0003688089],"domain_scores_gemma":[0.9987428,0.0000772932,0.0001565717,0.0007693512,0.0001635884,0.00009038706],"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":[0.000002848486,0.0000697323,0.003867541,0.0002358215,0.00001047838,0.00001209769,0.00003578462,0.01091352,0.9847782,0.00005070102,0.00001338139,0.000009951305],"study_design_scores_gemma":[0.0002646993,0.000004637733,0.01601231,0.0002575536,0.00004591505,2.544138e-8,0.00002309616,0.1130083,0.8699478,7.422596e-7,0.0001451649,0.0002897711],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9965622,0.001579692,0.0007935855,0.00009032972,0.0003007311,0.0004202776,0.00006420688,0.0001851226,0.000003884818],"genre_scores_gemma":[0.9969217,0.0001389902,0.002638364,0.00005260308,0.00007656439,0.00008721971,1.559285e-7,0.00008326228,0.000001095087],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1148303,"threshold_uncertainty_score":0.9999405,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009767162589608379,"score_gpt":0.1971273165113988,"score_spread":0.1873601539217904,"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."}}