{"id":"W2891384592","doi":"10.1038/s41374-018-0117-5","title":"Application of ex-vivo spheroid model system for the analysis of senescence and senolytic phenotypes in uterine leiomyoma","year":2018,"lang":"en","type":"article","venue":"Laboratory Investigation","topic":"Uterine Myomas and Treatments","field":"Medicine","cited_by":29,"is_retracted":false,"has_abstract":false,"ca_institutions":"Princess Margaret Cancer Centre","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Cancer Institute; National Institutes of Health; Northwestern University; U.S. Department of Health and Human Services","keywords":"Senescence; Ex vivo; Biology; In vivo; Protein kinase B; Spheroid; Cell biology; Cancer research; Phenotype; Gene expression profiling; Cell culture; Gene expression; Gene; Signal transduction; 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":[],"consensus_categories":[],"category_scores_codex":[0.0001366095,0.00007780401,0.0002239054,0.0001031111,0.00002807309,0.000004644782,0.00003968118,0.00004044753,0.000002452534],"category_scores_gemma":[0.00002353132,0.00005680677,0.00002650025,0.0006511781,0.0001419327,0.00006100018,0.00001472677,0.00002330266,6.247445e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003835804,"about_ca_system_score_gemma":0.00005851275,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001246104,"about_ca_topic_score_gemma":0.0001243734,"domain_scores_codex":[0.9994106,0.00001892211,0.0002451401,0.0001480379,0.0001044552,0.00007286586],"domain_scores_gemma":[0.9993017,0.00004069515,0.0001604192,0.000232353,0.000228288,0.00003657816],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001803911,0.00008687166,0.3026427,0.0008166811,0.0005897898,0.000001263633,0.001339971,0.001153214,0.6859671,0.004804772,0.00002284699,0.00239445],"study_design_scores_gemma":[0.0009024324,0.0001586489,0.1892572,0.00008887527,0.0009046793,0.000001429926,0.0002117972,0.635935,0.17224,0.000147186,0.00008153181,0.00007119748],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9858372,0.0001046513,0.01337601,0.0001482198,0.00002057874,0.0004098412,0.00006071885,0.00001429244,0.00002846501],"genre_scores_gemma":[0.9959266,0.000008537316,0.003879603,0.00004791719,0.00003130225,0.00004851663,0.00002382855,0.000008180185,0.00002551962],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6347818,"threshold_uncertainty_score":0.2316513,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01577171687609092,"score_gpt":0.2734577217777918,"score_spread":0.2576860049017009,"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."}}