{"id":"W4387299229","doi":"10.1021/acs.nanolett.3c00562","title":"Extracellular Vesicle Refractive Index Derivation Utilizing Orthogonal Characterization","year":2023,"lang":"en","type":"article","venue":"Nano Letters","topic":"Extracellular vesicles in disease","field":"Biochemistry, Genetics and Molecular Biology","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"National Center for Advancing Translational Sciences; NIH Clinical Center; National Cancer Institute; Prostate Cancer Foundation; National Institutes of Health; Multiple Sclerosis Society; University of Ottawa; National Multiple Sclerosis Society","keywords":"Nanoparticle tracking analysis; Refractive index; Optics; Particle (ecology); Mie scattering; Light scattering; Refractometry; Materials science; Nanoparticle; Characterization (materials science); Population; Particle size; Scattering; Nanotechnology; Physics; Chemistry; Microvesicles","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":[0.001305275,0.0008575668,0.0004275532,0.001084666,0.0003526926,0.001131871,0.0004010182,0.0006730824,0.0007580171],"category_scores_gemma":[0.002885512,0.0003945899,0.0004233681,0.0005455049,0.0006499125,0.001014308,0.001704008,0.001243248,0.0007180185],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000547987,"about_ca_system_score_gemma":0.0006360229,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006370607,"about_ca_topic_score_gemma":0.0009001503,"domain_scores_codex":[0.9990674,0.0001122328,0.00009194187,0.000169148,0.0004672325,0.00009210828],"domain_scores_gemma":[0.9980996,0.0004872331,0.0004334855,0.0002745681,0.0006230996,0.00008205797],"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":[0.00003475961,0.00003211935,0.00037672,0.00005638508,0.000009541443,0.00008186302,0.0001293221,0.0003632118,0.9888232,0.002582247,0.00006687387,0.007443843],"study_design_scores_gemma":[0.000003657337,0.0000379115,0.0005150575,0.000007768121,0.000007210366,0.00009904015,0.00002221584,0.005733732,0.991392,0.000411121,0.001749355,0.00002088214],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5354118,0.001427513,0.4517778,0.0002169272,0.0001611216,0.0005017365,0.0006160456,0.0005783345,0.009308795],"genre_scores_gemma":[0.6174176,0.002403074,0.372255,0.0001551245,0.00004158262,0.0008513473,0.0009359324,0.0002540912,0.005686327],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001305275,"threshold_uncertainty_score":0.006903052,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01140720613745682,"score_gpt":0.2427015264006339,"score_spread":0.231294320263177,"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."}}