{"id":"W3119307037","doi":"10.1101/2021.01.12.426411","title":"A method for the efficient iron-labeling of patient-derived xenograft cells and cellular imaging validation","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Characterization and Applications of Magnetic Nanoparticles","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lawson Health Research Institute; Robarts Clinical Trials; Western University","funders":"Breast Cancer Society of Canada","keywords":"Bioluminescence imaging; Iron oxide nanoparticles; Breast cancer; Cancer research; In vivo; Medicine; Magnetic particle imaging; Cancer cell; Magnetic resonance imaging; Viability assay; Cancer; Luciferase; Cell; Pathology; Cell culture; Chemistry; Internal medicine; Iron oxide; Biology; Nanoparticle; Magnetic nanoparticles; Nanotechnology; Radiology; Materials science; Transfection","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003222352,0.000259587,0.0002804702,0.00009968392,0.000115435,0.0001733092,0.0002039054,0.000111782,0.00001465823],"category_scores_gemma":[0.00004948317,0.000253804,0.00008179469,0.0002343005,0.00005446921,0.00004827043,0.0001745202,0.0001842033,0.000001763258],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004556968,"about_ca_system_score_gemma":0.00006077675,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008867187,"about_ca_topic_score_gemma":1.783745e-7,"domain_scores_codex":[0.9987159,0.00005132025,0.0004462315,0.0003774907,0.000172658,0.0002363728],"domain_scores_gemma":[0.9987058,0.0001708352,0.0001907647,0.0005791527,0.0002698301,0.00008358591],"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":[0.000003810796,0.00004150927,0.0000557901,0.000360575,0.00003765342,7.793573e-7,0.00005116786,0.02909822,0.9701973,0.0000734745,0.00001297839,0.00006678429],"study_design_scores_gemma":[0.0002207651,0.000007135783,0.0004372643,0.0001047206,0.0001077886,6.409751e-9,0.00002456799,0.1596448,0.8386909,5.568683e-7,0.0005392167,0.0002222969],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7440726,0.001631742,0.2529987,0.00009092732,0.000289478,0.0006817661,0.00008842741,0.0001454684,8.616828e-7],"genre_scores_gemma":[0.9322749,0.000157633,0.06713475,0.00003273272,0.00006604131,0.0002643163,0.000001176392,0.00006813775,3.172356e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1882023,"threshold_uncertainty_score":0.9999914,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007330542439972513,"score_gpt":0.2029595726503395,"score_spread":0.195629030210367,"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."}}