{"id":"W4288914046","doi":"10.1002/smtd.202200547","title":"Holistic Analysis of Glioblastoma Stem Cell DNA Using Nanoengineered Plasmonic Metasensor for Glioblastoma Diagnosis","year":2022,"lang":"en","type":"article","venue":"Small Methods","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Toronto Metropolitan University; St. Michael's Hospital","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Liquid biopsy; Biomarker; Glioblastoma; Stem cell; Oncology; Medicine; Circulating tumor cell; Cancer; Cell-free fetal DNA; Computational biology; Cancer research; Pathology; Internal medicine; Bioinformatics; Biology; Metastasis","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002437629,0.0003915066,0.0003302475,0.0004352875,0.0001134356,0.000288681,0.0003561761,0.0007147408,0.0005154395],"category_scores_gemma":[0.000266129,0.0002025637,0.0003452846,0.0002536884,0.0002006537,0.0003117473,0.000357084,0.0003366417,0.0002878864],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003457058,"about_ca_system_score_gemma":0.0002310236,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003372009,"about_ca_topic_score_gemma":0.0007780293,"domain_scores_codex":[0.9997953,0.00002419195,0.000009694427,0.00005896558,0.00009329883,0.00001857706],"domain_scores_gemma":[0.9999044,0.0000247428,0.00002474313,0.000007900345,0.00002763921,0.00001054832],"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":[0.00002533211,0.000009187369,0.0001229914,0.00004004937,0.000004293121,0.00001836073,0.00001700554,0.0002188822,0.9964902,0.0000767565,0.00003978587,0.002937241],"study_design_scores_gemma":[0.000004786401,0.0001303327,0.0007087946,0.000005594754,0.00001409486,0.00008502136,0.00002320377,0.007143183,0.990968,0.0001157867,0.0007900586,0.00001111678],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.858179,0.004149957,0.1329596,0.0004568592,0.0001702977,0.0001090019,0.0005912603,0.0008750819,0.002508945],"genre_scores_gemma":[0.9061118,0.001656348,0.08874947,0.0003100641,0.00003302893,0.0001299957,0.0002723547,0.00004337573,0.002693534],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0007147408,"threshold_uncertainty_score":0.002508283,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04330041939616548,"score_gpt":0.3486214471509205,"score_spread":0.3053210277547551,"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."}}