{"id":"W2889094617","doi":"10.1016/j.actbio.2018.08.035","title":"CX-5461-loaded nucleolus-targeting nanoplatform for cancer therapy through induction of pro-death autophagy","year":2018,"lang":"en","type":"article","venue":"Acta Biomaterialia","topic":"RNA Interference and Gene Delivery","field":"Biochemistry, Genetics and Molecular Biology","cited_by":59,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"Ministry of Education of the People's Republic of China; Fundamental Research Funds for the Central Universities; Development and Reform Commission of Shenzhen Municipality; Science, Technology and Innovation Commission of Shenzhen Municipality; Natural Science Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Autophagy; Nucleolus; Cancer; Materials science; Cancer research; Cancer therapy; Programmed cell death; Oncology; Nanotechnology; Apoptosis; Medicine; Biology; Internal medicine; Cell 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":[0.0001055909,0.0002047229,0.0002072308,0.0001282851,0.0001586166,0.0001781029,0.0001314136,0.0003531294,0.0009385541],"category_scores_gemma":[0.00007430171,0.0001034991,0.0001701855,0.00009058999,0.0001308957,0.0002470154,0.0001775481,0.0002254617,0.0002031916],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002658361,"about_ca_system_score_gemma":0.0002014505,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003967826,"about_ca_topic_score_gemma":0.0005442252,"domain_scores_codex":[0.9999419,0.000005710669,0.000004396287,0.00001742716,0.00001597473,0.00001458629],"domain_scores_gemma":[0.9999725,0.00000542516,0.000008655801,0.000002689268,0.000004864733,0.0000058724],"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":[0.00006632254,0.00001129724,0.00002790583,0.00004709902,0.000002891553,0.00003446713,0.000008637145,0.0001485151,0.9975508,0.0002125037,0.00006580473,0.001823682],"study_design_scores_gemma":[0.00000958643,0.00009024872,0.0002289615,0.000003064907,0.000008983145,0.00006646893,0.000004937458,0.001026324,0.9974775,0.00003255924,0.001047635,0.000003667143],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9826438,0.002347973,0.01058391,0.0001344311,0.00008074161,0.00004814125,0.0001665455,0.0001704075,0.003824107],"genre_scores_gemma":[0.9927375,0.0007427686,0.004024236,0.00006215675,0.000009608327,0.00003420902,0.00008917835,0.00001677521,0.002283721],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009385541,"threshold_uncertainty_score":0.003139794,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04294422167589199,"score_gpt":0.3150889749074985,"score_spread":0.2721447532316065,"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."}}