{"id":"W4287385011","doi":"10.1039/d2na00008c","title":"Tracking the fates of iron-labeled tumor cells <i>in vivo</i> using magnetic particle imaging","year":2022,"lang":"en","type":"article","venue":"Nanoscale Advances","topic":"Characterization and Applications of Magnetic Nanoparticles","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Magnetic resonance imaging; In vivo; Metastasis; Cell; Preclinical imaging; Cancer cell; Magnetic particle imaging; Pathology; Cancer research; Cancer; Chemistry; Medicine; Biology; Radiology; Magnetic nanoparticles; Materials 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":[],"consensus_categories":[],"category_scores_codex":[0.0001252271,0.00009486738,0.0001230244,0.00003951049,0.0001251605,0.00002219165,0.0002005761,0.00000770989,0.0003315824],"category_scores_gemma":[0.000009994381,0.00008718881,0.00003035591,0.0003829928,0.00006328301,0.0001483345,0.00005197071,0.00009045147,0.000004125676],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003145142,"about_ca_system_score_gemma":0.000009278853,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009314695,"about_ca_topic_score_gemma":0.000007873777,"domain_scores_codex":[0.9991816,0.00003825414,0.0002791107,0.0001345061,0.0001620611,0.0002044526],"domain_scores_gemma":[0.999624,0.00008953155,0.00005352983,0.0001861039,0.0000182171,0.0000285924],"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":[0.000005709511,0.00004820061,0.002755345,0.00002308112,0.000001051088,0.000001804461,0.0001847455,0.06772209,0.9203658,0.0000922649,0.00003812396,0.00876186],"study_design_scores_gemma":[0.0003893563,0.000021203,0.0008123113,0.00001469046,0.00001173231,0.000006901627,0.000654015,0.130637,0.8555182,0.000146729,0.01165112,0.0001367923],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9961112,0.002819306,0.0002747682,0.0001429929,0.0001448413,0.0001979132,0.00003275834,0.00006938812,0.0002068974],"genre_scores_gemma":[0.9990692,0.0000880544,0.0006103605,0.00008312287,0.00001935573,0.00006554748,0.000001671797,0.00001886697,0.00004381538],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06484756,"threshold_uncertainty_score":0.3630594,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006557117561872926,"score_gpt":0.211907688923276,"score_spread":0.205350571361403,"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."}}