{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004141282,0.0007889249,0.0004410057,0.0008240537,0.0003132856,0.0004402379,0.0002729761,0.0007898524,0.0006248162],"category_scores_gemma":[0.0005007538,0.0001711524,0.0003870348,0.0004123169,0.0002090156,0.0004129981,0.000473409,0.0005397823,0.000651361],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002486472,"about_ca_system_score_gemma":0.0003671169,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005837523,"about_ca_topic_score_gemma":0.0007297178,"domain_scores_codex":[0.9997082,0.00003866684,0.00002474715,0.00008112592,0.0001123236,0.00003476153],"domain_scores_gemma":[0.9998715,0.00003260528,0.00001947908,0.000009045665,0.00005098556,0.00001644788],"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":[0.0000347518,0.00001023068,0.0003013962,0.00006735855,0.00000959964,0.0001007357,0.00002483568,0.0000708286,0.9959442,0.00008100389,0.00009212275,0.003262821],"study_design_scores_gemma":[0.000006735401,0.00007036692,0.003849402,0.00001513715,0.00002556649,0.0004168869,0.00004625879,0.003232459,0.9890732,0.0001409488,0.003104713,0.00001826449],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8521995,0.01161387,0.1258398,0.0005751353,0.0002767474,0.0003065787,0.003788284,0.0009569786,0.004443193],"genre_scores_gemma":[0.8365026,0.009809825,0.1419916,0.0005952343,0.00005963795,0.0006016098,0.005445051,0.0002594953,0.004734931],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008240537,"threshold_uncertainty_score":0.002190173,"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."}}