{"id":"W4404678103","doi":"10.1038/s44172-024-00319-9","title":"Insights from a multiscale framework on metabolic rate variation driving glioblastoma multiforme growth and invasion","year":2024,"lang":"en","type":"article","venue":"Communications Engineering","topic":"Mathematical Biology Tumor Growth","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; University of Manitoba; University of Victoria","funders":"British Columbia Knowledge Development Fund; BC Cancer Foundation; Natural Sciences and Engineering Research Council of Canada; Government of Canada","keywords":"Temozolomide; Glioblastoma; In vitro; Biology; In silico; Spheroid; Cancer cell; Immune system; Cancer research; U87; Biological system; Computational biology; Cancer; Neuroscience; Immunology; Gene; Biochemistry; Genetics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001817952,0.0002143148,0.0002729814,0.0002313293,0.000155607,0.00009892875,0.0004488368,0.000155489,0.00001805264],"category_scores_gemma":[0.002848334,0.000188078,0.00006168539,0.0003514026,0.00005999185,0.0001857176,0.0003513704,0.0005136516,0.0000592648],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003815415,"about_ca_system_score_gemma":0.00001513143,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001212536,"about_ca_topic_score_gemma":0.00001356266,"domain_scores_codex":[0.9989955,0.00008715187,0.0003555131,0.0002710313,0.0001143999,0.000176372],"domain_scores_gemma":[0.9928225,0.005953026,0.00005779968,0.001054903,0.0000352249,0.00007652291],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000003750977,0.00007038463,0.00005135769,0.000123284,0.00007679903,0.00000331466,0.00176241,0.00003180363,0.02181946,0.9756231,0.0000253534,0.0004090203],"study_design_scores_gemma":[0.0002185369,0.00005363334,0.005941099,0.001727045,0.0001277644,0.000009955977,0.00007331709,0.4568055,0.009007719,0.5250745,0.0005436425,0.0004172309],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8003287,0.002013292,0.1951057,0.0006129174,0.0002184614,0.0003985508,0.0000186039,0.0007884766,0.0005153367],"genre_scores_gemma":[0.8451168,0.0002122067,0.15442,0.00002962187,0.00005705357,0.00009468191,0.00001194049,0.00004362266,0.00001400183],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4567737,"threshold_uncertainty_score":0.76696,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02488527690742927,"score_gpt":0.2756087505304342,"score_spread":0.2507234736230049,"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."}}