{"id":"W4407429061","doi":"10.1021/acsami.4c19178","title":"Strategy of “Controllable Ions Interference” for Boosting MRI-Guided Ferroptosis Therapy of Tumors","year":2025,"lang":"en","type":"article","venue":"ACS Applied Materials & Interfaces","topic":"Mass Spectrometry Techniques and Applications","field":"Chemistry","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Cancer Research","funders":"Guangzhou Medical University","keywords":"Materials science; Boosting (machine learning); Ion; Interference (communication); Nanotechnology; Magnetic resonance imaging; Optoelectronics; Cancer research; Medicine; Artificial intelligence; Computer science; Radiology; Telecommunications; Channel (broadcasting); Physics","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":[0.00008522557,0.0003271817,0.0001392537,0.0001139839,0.00009241664,0.0001728622,0.0002241089,0.0003418041,0.0005495208],"category_scores_gemma":[0.00008536482,0.000126111,0.0001532567,0.00007521147,0.0001755749,0.0002123898,0.0002247871,0.0002386882,0.0002299711],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002384489,"about_ca_system_score_gemma":0.0001566903,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003494696,"about_ca_topic_score_gemma":0.0005873064,"domain_scores_codex":[0.9999369,0.000008511264,0.000003599383,0.00002248295,0.00001489014,0.00001359696],"domain_scores_gemma":[0.9999666,0.000004737621,0.00001315959,0.00000346428,0.000005901295,0.000006071813],"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":[0.00003058216,0.000009365056,0.00003316324,0.00002566188,0.00000218079,0.00002820861,0.000007990732,0.0001485308,0.9975581,0.0001830464,0.00007112179,0.00190208],"study_design_scores_gemma":[0.000007226124,0.00008127048,0.0001465646,0.000001581909,0.000005181386,0.00006568887,0.000003536082,0.002196648,0.9957261,0.00003264263,0.001730265,0.00000330423],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.890983,0.002310021,0.09905836,0.0004438748,0.000130321,0.0001888736,0.0001752213,0.0007219512,0.005988394],"genre_scores_gemma":[0.9667469,0.0007275938,0.02956517,0.0001853857,0.00002166602,0.00006458311,0.00007854784,0.00004156729,0.002568608],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0005495208,"threshold_uncertainty_score":0.001838326,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02680807390986602,"score_gpt":0.3017342793050207,"score_spread":0.2749262053951547,"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."}}