{"id":"W3205094900","doi":"10.1039/d1tb01360b","title":"Complex cellular environments imaged by SERS nanoprobes using sugars as an all-in-one vector","year":2021,"lang":"en","type":"article","venue":"Journal of Materials Chemistry B","topic":"Spectroscopy Techniques in Biomedical and Chemical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Princess Margaret Cancer Centre; University Health Network","funders":"European Regional Development Fund; Fundação para a Ciência e a Tecnologia; Canada Research Chairs","keywords":"Raman spectroscopy; Materials science; Confocal microscopy; Microscopy; Confocal; Nanotechnology; Penetration (warfare); Sensitivity (control systems); Resolution (logic); Confocal laser scanning microscopy; Biomedical engineering; Optics; Computer science; Artificial intelligence; Physics; Medicine; Engineering","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":[0.0002238989,0.0005915902,0.0001878363,0.0001941135,0.000153097,0.0003465569,0.0002549214,0.0003229213,0.0005993747],"category_scores_gemma":[0.0001480225,0.0001958164,0.0001586899,0.0001234205,0.000380982,0.0002841355,0.0002795756,0.0003294704,0.0002982304],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00020361,"about_ca_system_score_gemma":0.0001805124,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003754653,"about_ca_topic_score_gemma":0.0007899349,"domain_scores_codex":[0.9998813,0.0000205606,0.0000096997,0.00003216236,0.00002987034,0.00002644549],"domain_scores_gemma":[0.9999052,0.00003072105,0.00003060353,0.000009199507,0.00001219132,0.00001199341],"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":[0.00001130049,0.000003593476,0.0000306235,0.00001542071,0.000001451172,0.00002112136,0.000008437541,0.0001158166,0.9991829,0.000162998,0.00000989004,0.0004364481],"study_design_scores_gemma":[0.000001168718,0.00001931065,0.0001547448,0.000001127681,0.000002400083,0.00003993982,0.000006163603,0.0006793722,0.9985954,0.0000390871,0.000458618,0.000002712012],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9341418,0.0006272215,0.06282458,0.0001178283,0.0000312115,0.00006569963,0.0002253888,0.0002770931,0.001689224],"genre_scores_gemma":[0.8812017,0.001189648,0.1135534,0.00008210545,0.00001115621,0.0001229054,0.000434887,0.0001125697,0.003291629],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0005993747,"threshold_uncertainty_score":0.00200516,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02769348533356144,"score_gpt":0.3204069518165015,"score_spread":0.2927134664829401,"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."}}