{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002200484,0.0004283511,0.0001714224,0.0002576893,0.0001665737,0.0002931313,0.0002385099,0.0005925032,0.001370421],"category_scores_gemma":[0.000139887,0.0002357367,0.0003511342,0.0001168487,0.0002094904,0.0003199287,0.0002954916,0.000560792,0.000586331],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003761171,"about_ca_system_score_gemma":0.0001716237,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003117218,"about_ca_topic_score_gemma":0.0005841283,"domain_scores_codex":[0.9998809,0.00001113326,0.000006629017,0.00002628677,0.00005863701,0.00001636665],"domain_scores_gemma":[0.9999592,0.000009509912,0.00001342028,0.000003405758,0.000008897242,0.000005426062],"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":[0.00002259631,0.00003570753,0.00005631519,0.0000977843,0.00001300968,0.00006625724,0.00001506691,0.0002943294,0.9871587,0.001568403,0.001357164,0.009314668],"study_design_scores_gemma":[0.00001167486,0.00008425517,0.0002332117,0.000006834071,0.00001007298,0.0001410098,0.000005127927,0.001121806,0.9789987,0.0002150292,0.01916219,0.00001006509],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6180983,0.03951371,0.2443902,0.003923817,0.003475228,0.0008638012,0.001908615,0.004217754,0.08360846],"genre_scores_gemma":[0.846328,0.01037997,0.1117286,0.001075193,0.0001725774,0.000418968,0.001033403,0.0002693906,0.02859381],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001370421,"threshold_uncertainty_score":0.004584551,"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."}}