{"id":"W4401155069","doi":"10.1016/j.biomaterials.2024.122731","title":"Patient-specific vascularized tumor model: Blocking monocyte recruitment with multispecific antibodies targeting CCR2 and CSF-1R","year":2024,"lang":"en","type":"article","venue":"Biomaterials","topic":"CAR-T cell therapy research","field":"Medicine","cited_by":40,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"National Cancer Institute; National Institutes of Health; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Ludwig Center at Harvard; Merck KGaA","keywords":"Blocking (statistics); Antibody; Cancer research; Materials science; Biomedical engineering; Biology; Immunology; Medicine; Computer science","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":[0.0005902245,0.0003254297,0.0005892886,0.0002704215,0.0001583276,0.0004201301,0.00008815494,0.00009194518,0.0008085543],"category_scores_gemma":[0.00002939162,0.0002358901,0.0001110354,0.0002353881,0.0001602982,0.0001447985,0.000134874,0.00005357274,0.00005499686],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001603244,"about_ca_system_score_gemma":0.00008582351,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005053686,"about_ca_topic_score_gemma":0.000002660564,"domain_scores_codex":[0.9976544,0.0001293942,0.0004946808,0.000678018,0.0005225054,0.0005210114],"domain_scores_gemma":[0.9991324,0.00008501205,0.00008211786,0.0004052938,0.000112774,0.0001824003],"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":[0.0009652558,0.00007835837,0.0002485799,0.0003794542,0.0002735575,0.0004683562,0.001182471,0.00001378481,0.9801274,0.00008417972,0.0005659919,0.01561264],"study_design_scores_gemma":[0.0026194,0.0005247059,0.000519811,0.0009787699,0.0001147433,0.0002494459,0.0004001633,0.00751902,0.9368758,0.00009478318,0.04967505,0.0004283295],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9884558,0.007739366,0.0004037021,0.0002177406,0.0004768787,0.001883465,0.00004335909,0.0003482985,0.0004313637],"genre_scores_gemma":[0.9868658,0.001714499,0.009878207,0.00005611177,0.0003387577,0.000227941,0.00008116099,0.0001230107,0.0007145263],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04910906,"threshold_uncertainty_score":0.9619321,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06925131991838904,"score_gpt":0.3244801512705208,"score_spread":0.2552288313521317,"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."}}