{"id":"W2019653593","doi":"10.1016/j.nano.2011.01.012","title":"A novel blood plasma analysis technique combining membrane electrophoresis with silver nanoparticle-based SERS spectroscopy for potential applications in noninvasive cancer detection","year":2011,"lang":"en","type":"article","venue":"Nanomedicine Nanotechnology Biology and Medicine","topic":"Spectroscopy Techniques in Biomedical and Chemical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":161,"is_retracted":false,"has_abstract":false,"ca_institutions":"BC Cancer Agency","funders":"Canadian Institutes of Health Research; National Natural Science Foundation of China; Science and Technology Projects of Fujian Province; Ministerio de Sanidad, Consumo y Bienestar Social","keywords":"Surface-enhanced Raman spectroscopy; Silver nanoparticle; Raman spectroscopy; Nanoparticle; Membrane; Electrophoresis; Chemistry; Blood proteins; Cancer; Silver stain; Gel electrophoresis; Blood plasma; Analytical Chemistry (journal); Chromatography; Materials science; Nanotechnology; Raman scattering; Pathology; Biochemistry; Biology; Medicine","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":[0.0006171592,0.0006945173,0.000727376,0.0004518681,0.0004172253,0.0006297343,0.001060472,0.001675602,0.0009583707],"category_scores_gemma":[0.000739642,0.0005189085,0.0004692102,0.0003499538,0.0003836775,0.0009752096,0.000696611,0.0009834217,0.001097464],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002321632,"about_ca_system_score_gemma":0.0003867648,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001808056,"about_ca_topic_score_gemma":0.0003602135,"domain_scores_codex":[0.9994843,0.00009819094,0.0000340043,0.0001522011,0.0001915273,0.00003979747],"domain_scores_gemma":[0.999587,0.0001399709,0.0000606217,0.00004591782,0.0001188503,0.00004772071],"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":[0.00005841451,0.00003114032,0.0001354097,0.00005137776,0.0000105758,0.00006633272,0.00001716864,0.00002237604,0.9892884,0.0001350926,0.0002179027,0.009965872],"study_design_scores_gemma":[0.0000204671,0.0002018299,0.001153405,0.000006323824,0.00003355559,0.001748743,0.00001806021,0.003288179,0.9883903,0.0002619217,0.004840307,0.00003683678],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1801784,0.005588402,0.8054762,0.001594961,0.0009177739,0.0002675746,0.0002812477,0.002596784,0.003098625],"genre_scores_gemma":[0.396319,0.00289015,0.589218,0.001500228,0.0004173837,0.0002882088,0.0003326256,0.0002202235,0.008814199],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001675602,"threshold_uncertainty_score":0.003263891,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01009332548384237,"score_gpt":0.2926303673524747,"score_spread":0.2825370418686323,"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."}}