{"id":"W3154723095","doi":"10.3390/biom11050621","title":"Overcoming the Challenges of High Quality RNA Extraction from Core Needle Biopsy","year":2021,"lang":"en","type":"article","venue":"Biomolecules","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; London Health Sciences Centre","funders":"Amsterdam University Medical Centers; Agentschap Innoveren en Ondernemen","keywords":"Core (optical fiber); Extraction (chemistry); Core biopsy; Biopsy; RNA; RNA extraction; Computer science; Computational biology; Medicine; Radiology; Biology; Internal medicine; Chemistry; Chromatography; Biochemistry; Gene; Telecommunications","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":[0.000100711,0.0001024358,0.0001249424,0.0000139686,0.00005364114,0.00001824255,0.000128177,0.0001028745,0.00002493771],"category_scores_gemma":[0.0001541823,0.00008653698,0.00009014511,0.00005621037,0.00009568272,0.000002032963,0.0001114053,0.00004784127,0.000004156483],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001171756,"about_ca_system_score_gemma":0.00007951639,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009163514,"about_ca_topic_score_gemma":0.0004206951,"domain_scores_codex":[0.9992879,0.00004771536,0.0001917929,0.0002484734,0.0001011982,0.0001228988],"domain_scores_gemma":[0.9992746,0.00006867159,0.0001143397,0.0004084161,0.00009661854,0.00003738967],"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":[0.00003956409,0.00006086739,0.0002488065,0.00001054722,0.00005728898,0.000006330706,0.00006589383,0.00001465597,0.9838748,0.008540943,0.0003828261,0.006697502],"study_design_scores_gemma":[0.0003219125,0.00004861569,0.01687747,0.00001651848,0.00002577521,0.000009710833,0.0005883191,0.00001145284,0.9702716,0.00178029,0.009914014,0.0001342521],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.980266,0.01601563,0.001355077,0.0008430645,0.0003087631,0.00005875554,0.0001774033,0.000006008145,0.0009693042],"genre_scores_gemma":[0.9949628,0.003383712,0.001013074,0.0001670862,0.0002668742,0.000006838351,0.000137824,0.00001393415,0.00004792098],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01662866,"threshold_uncertainty_score":0.3528876,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03650955022371093,"score_gpt":0.3032695645553403,"score_spread":0.2667600143316293,"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."}}