{"id":"W3189960903","doi":"10.1039/d1ra04305f","title":"MALDI-MS-based biomarker analysis of extracellular vesicles from human lung carcinoma cells","year":2021,"lang":"en","type":"article","venue":"RSC Advances","topic":"Extracellular vesicles in disease","field":"Biochemistry, Genetics and Molecular Biology","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Chinese Academy of Sciences; Special Project for Research and Development in Key areas of Guangdong Province; Shenzhen Engineering Laboratory of Single-molecule Detection and Instrument Development; Science, Technology and Innovation Commission of Shenzhen Municipality; National Natural Science Foundation of China","keywords":"Extracellular vesicles; Biomarker; Chemistry; Human lung; Extracellular; Lung cancer; Vesicle; Carcinoma; Cancer research; Lung; Pathology; Biochemistry; Cell biology; Medicine; Biology; Internal medicine; Membrane","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":[0.0001350163,0.0002299867,0.0003480095,0.0001116994,0.00009137912,0.00002613915,0.0002990018,0.0001388338,0.0002984024],"category_scores_gemma":[0.00005441418,0.0002451123,0.0003882946,0.0004655479,0.0001751471,0.00001137705,0.0001112625,0.00007340778,0.000006083355],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001863354,"about_ca_system_score_gemma":0.0000975646,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003532859,"about_ca_topic_score_gemma":0.0001070475,"domain_scores_codex":[0.9982752,0.0001472565,0.000424395,0.0006269666,0.0002458036,0.0002803537],"domain_scores_gemma":[0.9986037,0.00004797841,0.0002262684,0.0008403226,0.0001463026,0.0001354397],"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":[0.00005516178,0.0004343517,0.0343708,0.00004863932,0.0005001071,0.00007933674,0.00002091988,0.0007607589,0.9621276,0.00005917221,0.0002100455,0.001333115],"study_design_scores_gemma":[0.000895664,0.00004126212,0.01692951,0.00001716733,0.0007972309,7.344335e-7,0.0000780691,0.002041266,0.9698812,0.0001130602,0.008922093,0.0002826968],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9394423,0.0521182,0.007492611,0.0000361526,0.0001103877,0.00009804256,0.0002755733,0.00001681705,0.0004099573],"genre_scores_gemma":[0.9931999,0.0001592404,0.004752675,0.00007968132,0.00009713066,0.00001323743,0.001224283,0.00003320315,0.0004406115],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05375768,"threshold_uncertainty_score":0.9995387,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01072668767060673,"score_gpt":0.2694541278597794,"score_spread":0.2587274401891727,"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."}}