{"id":"W2911514609","doi":"10.1021/acsnano.8b07024","title":"Machine-Learning-Driven Surface-Enhanced Raman Scattering Optophysiology Reveals Multiplexed Metabolite Gradients Near Cells","year":2019,"lang":"en","type":"article","venue":"ACS Nano","topic":"Spectroscopy Techniques in Biomedical and Chemical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":142,"is_retracted":false,"has_abstract":true,"ca_institutions":"Regroupement Québécois sur les Matériaux de Pointe; Université de Montréal","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Max-Planck-Gesellschaft; Canada Foundation for Innovation","keywords":"Extracellular; HeLa; Metabolite; Glutamine; Cancer cell; Nanoprobe; Glycolysis; Biophysics; Secretion; Cell biology; Tumor microenvironment; Biology; Biochemistry; Cell; Materials science; Nanotechnology; Metabolism; Cancer; Tumor cells; Cancer research","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":[0.0002459546,0.0003110123,0.0002417446,0.0001555727,0.00009571126,0.0002593229,0.0002456347,0.0003810297,0.0002935757],"category_scores_gemma":[0.0003325611,0.0001581978,0.00017002,0.0001302706,0.0002666006,0.0003070163,0.0002122473,0.0003411805,0.0001331511],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002290885,"about_ca_system_score_gemma":0.000185464,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003509685,"about_ca_topic_score_gemma":0.0007785881,"domain_scores_codex":[0.9998507,0.00002764568,0.00000720246,0.00004325278,0.00005336992,0.00001775653],"domain_scores_gemma":[0.9998645,0.00005847182,0.00003265776,0.00001303351,0.00002227877,0.000009017979],"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":[0.00002523518,0.0000123081,0.0002241427,0.00002140906,0.000004857799,0.00002735546,0.00001249945,0.002001149,0.99407,0.0002752901,0.00003836371,0.003287299],"study_design_scores_gemma":[0.000003788168,0.00009687898,0.001221035,0.000002554223,0.000004325077,0.00006505623,0.00001477106,0.05879194,0.9389834,0.0002672517,0.0005389752,0.00000999437],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8427956,0.0006960144,0.1536175,0.0002457051,0.00005475551,0.00002974151,0.0001732847,0.0004232617,0.001964168],"genre_scores_gemma":[0.9414932,0.0004403157,0.05638108,0.00007075842,0.0000157117,0.00004809098,0.0001095093,0.00002742097,0.001414097],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0003810297,"threshold_uncertainty_score":0.001662195,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006801643380616618,"score_gpt":0.2724555705970004,"score_spread":0.2656539272163838,"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."}}