{"id":"W4294311539","doi":"10.1007/s13346-022-01234-2","title":"Modeling and simulation of smart magnetic self-assembled nanomicelle trajectories in an internal thoracic artery flow for breast cancer therapy","year":2022,"lang":"en","type":"article","venue":"Drug Delivery and Translational Research","topic":"Nanoparticle-Based Drug Delivery","field":"Materials Science","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"Dasatinib; Materials science; Magnetic field; Biomedical engineering; Breast cancer; Medicine; Drug delivery; Magnetic nanoparticles; Permeability (electromagnetism); Drug; Magnetic particle inspection; Nanotechnology; Cancer; Cancer research; Pharmacology; Internal medicine; Chemistry; Physics; Nanoparticle; Myeloid leukemia","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001711167,0.0002892367,0.0003590966,0.0003806175,0.0005665837,0.0006955211,0.0006285803,0.0014523,0.00254235],"category_scores_gemma":[0.0007735754,0.0003004249,0.0004487105,0.0002636583,0.0005641214,0.0004387675,0.0003329469,0.0004331,0.0002401851],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001007492,"about_ca_system_score_gemma":0.00131433,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0154692,"about_ca_topic_score_gemma":0.008308291,"domain_scores_codex":[0.9999366,0.0000134545,0.000002342224,0.0000098261,0.00001386754,0.00002388718],"domain_scores_gemma":[0.999746,0.0001389005,0.00002876183,0.00001310913,0.00003414402,0.00003912015],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004857148,0.00006234091,0.0006229043,0.00001936413,0.000008766699,0.0001064686,0.00002806939,0.9942394,0.002145045,0.001618721,0.0001810654,0.0009191654],"study_design_scores_gemma":[0.000008575247,0.00001309978,0.0001078176,0.000001809188,0.000002032418,0.000005401944,0.000007007742,0.9992937,0.0003088897,0.0001193943,0.000129899,0.000002373789],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9077628,0.000606804,0.05862401,0.001046387,0.0001690384,0.0001350925,0.0004081515,0.0003686963,0.030879],"genre_scores_gemma":[0.9887243,0.0001688144,0.00652445,0.00009622614,0.00001679571,0.00006819493,0.0001339452,0.00004783429,0.004219397],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0154692,"threshold_uncertainty_score":0.03075832,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04215857459797299,"score_gpt":0.3351676734840501,"score_spread":0.2930090988860771,"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."}}