{"id":"W3202173694","doi":"10.20944/preprints202109.0424.v1","title":"Microfluidic Lab-On-a-Chip based on UHF-Dielectrophoresis for Stemness Phenotype Characterization and Discrimination among Glioblastoma Cells","year":2021,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Microfluidic and Bio-sensing Technologies","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Associazione Italiana per la Ricerca sul Cancro; European Commission","keywords":"Dielectrophoresis; Phenotype; U87; Stem cell; Microfluidics; Cancer cell; Cell culture; Cancer stem cell; Ultra high frequency; Microfluidic chip; Lab-on-a-chip; In vitro; Materials science; Cancer research; Cell biology; Cancer; Glioblastoma; Biology; Nanotechnology; Genetics; Electronic engineering; Engineering; Gene","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":[0.0002411201,0.0003872989,0.0003451158,0.0003862363,0.0001961428,0.0004315173,0.0004284253,0.0005282963,0.001032541],"category_scores_gemma":[0.0002953753,0.0002213887,0.000218569,0.0001899616,0.0002563076,0.0003020511,0.0004300025,0.0002691094,0.000437095],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002749714,"about_ca_system_score_gemma":0.0002513885,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003785123,"about_ca_topic_score_gemma":0.0006543607,"domain_scores_codex":[0.9997258,0.00002888004,0.00001563146,0.00009418397,0.00009464037,0.00004094887],"domain_scores_gemma":[0.999872,0.0000470121,0.00002553979,0.00002130861,0.00002144338,0.00001277252],"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":[0.00003249863,0.00003592814,0.0003054672,0.00008219854,0.000009871883,0.00003045682,0.00002259811,0.0004232776,0.9889361,0.0003359587,0.0002839637,0.009501593],"study_design_scores_gemma":[0.00001052186,0.0001192778,0.002395506,0.000007236934,0.00001458158,0.0001255834,0.00001173467,0.007952305,0.9850707,0.00009901858,0.00417479,0.00001873546],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7208122,0.006195764,0.2607878,0.0005166922,0.000514603,0.0003487734,0.001667712,0.00261628,0.006540117],"genre_scores_gemma":[0.7919829,0.00180629,0.200783,0.0002348589,0.00006921922,0.0004323809,0.0006906453,0.00007354819,0.003927168],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001032541,"threshold_uncertainty_score":0.003454149,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03473573758380206,"score_gpt":0.2526579937975268,"score_spread":0.2179222562137248,"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."}}