{"id":"W2565254321","doi":"10.3390/proteomes5010001","title":"Targeted Enlargement of Aptamer Functionalized Gold Nanoparticles for Quantitative Protein Analysis","year":2016,"lang":"en","type":"article","venue":"Proteomes","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Canadian Institutes of Health Research; Alberta Innovates; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Aptamer; Colloidal gold; Target protein; Nanoparticle; Chemistry; Protein detection; Western blot; Biophysics; Nanotechnology; Matrix (chemical analysis); Materials science; Molecular biology; Chromatography; Biochemistry; Biology","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":[0.0004519071,0.0006153707,0.0003373688,0.000277031,0.000139245,0.000270466,0.0003981726,0.0005537687,0.0004946935],"category_scores_gemma":[0.0004333604,0.0003012527,0.0002803304,0.0001248563,0.0003104927,0.0004132702,0.0003596884,0.0005259669,0.0002984119],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003908903,"about_ca_system_score_gemma":0.0001721604,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002758072,"about_ca_topic_score_gemma":0.0004928662,"domain_scores_codex":[0.9995634,0.00006931145,0.00002445357,0.0001414071,0.0001653155,0.00003620264],"domain_scores_gemma":[0.9997551,0.00009737767,0.00004582964,0.00002602133,0.00005044555,0.00002516896],"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":[0.000008187638,0.000004608196,0.00001689804,0.00001984662,0.000001993573,0.00002115162,0.000009411439,0.00009364985,0.9985428,0.00007082807,0.00002103388,0.001189527],"study_design_scores_gemma":[0.000002825486,0.00003020037,0.0001475195,0.000001611523,0.000004009621,0.00008701959,0.000003353322,0.001596788,0.9972842,0.00004991956,0.0007885168,0.000003987774],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6324237,0.004599887,0.3568155,0.0003777588,0.000199086,0.0002562807,0.000218515,0.001275777,0.003833488],"genre_scores_gemma":[0.8202231,0.001867408,0.1731003,0.0003936385,0.00004889785,0.0002266484,0.0002711995,0.0001333524,0.003735451],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0006153707,"threshold_uncertainty_score":0.002836108,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01649598225129534,"score_gpt":0.2903170579904272,"score_spread":0.2738210757391318,"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."}}