{"id":"W4393260582","doi":"10.1002/adma.202470097","title":"Engineered Nanomaterials to Potentiate CRISPR/Cas9 Gene Editing for Cancer Therapy (Adv. Mater. 13/2024)","year":2024,"lang":"en","type":"article","venue":"Advanced Materials","topic":"Advanced Nanomaterials in Catalysis","field":"Materials Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; McGill University Health Centre","funders":"","keywords":"CRISPR; Materials science; Genome editing; Nanomaterials; Genetic enhancement; Nanotechnology; Cancer therapy; Cancer research; Cancer; Gene; Biology; Genetics","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":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00184199,0.001009879,0.001512576,0.0004782733,0.0004195166,0.001296407,0.00100732,0.0003112121,0.006159676],"category_scores_gemma":[0.0004354418,0.0009082322,0.0002732781,0.0006079311,0.0001303923,0.001414318,0.000389074,0.00009588923,0.001076592],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004370006,"about_ca_system_score_gemma":0.0001872491,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001018027,"about_ca_topic_score_gemma":0.00001367617,"domain_scores_codex":[0.9937392,0.0002118075,0.001745054,0.001873224,0.000762058,0.001668641],"domain_scores_gemma":[0.9972652,0.0003376546,0.0004145514,0.001148094,0.0004642928,0.0003702468],"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":[0.0005755207,0.00005369731,0.000002199889,0.0005461219,0.0001088648,0.00007319968,0.0003352676,0.001940169,0.9898027,0.0004145321,0.001861293,0.004286486],"study_design_scores_gemma":[0.0009911397,0.0002142571,0.00002608837,0.0004017238,0.0001135571,0.00006739484,0.00006621559,0.00003350603,0.9450876,0.002030026,0.04996128,0.001007212],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9306301,0.002699525,0.01778188,0.0005533995,0.03770277,0.002606994,0.006398895,0.001519316,0.0001071001],"genre_scores_gemma":[0.9275463,0.001082554,0.05846342,0.0006625979,0.004356809,0.004734894,0.0003912895,0.0004796949,0.002282507],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04809999,"threshold_uncertainty_score":0.9997404,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01789839661887016,"score_gpt":0.3127465140498371,"score_spread":0.2948481174309669,"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."}}