{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002270077,0.0001247706,0.0002256027,0.0001041457,0.00003857449,0.000006707742,0.00008941416,0.00007063904,0.00002515012],"category_scores_gemma":[0.0001967566,0.00007817979,0.0002384936,0.0002283336,0.0001097561,0.000004815631,0.0000414032,0.00001798557,0.000002231784],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001143314,"about_ca_system_score_gemma":0.00002565406,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000559396,"about_ca_topic_score_gemma":0.000009920767,"domain_scores_codex":[0.999097,0.0000506743,0.0002523599,0.0002991399,0.0001273348,0.000173436],"domain_scores_gemma":[0.9992915,0.00001977252,0.0001768236,0.0002330359,0.0002412541,0.00003757252],"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":[0.0002343623,0.00006610361,0.0007970132,0.00001521502,0.0007136626,1.856723e-7,0.000007161821,0.000003122803,0.9936299,0.0005186683,0.0002573536,0.003757256],"study_design_scores_gemma":[0.0004325951,0.0003857525,0.0006390964,0.00001693127,0.0002085917,2.241947e-7,0.00002569362,0.00003168859,0.9882458,0.000654967,0.009221653,0.0001370047],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.922546,0.0003306388,0.07590813,0.0003254557,0.00001474639,0.0006879636,0.00009991245,0.00003038691,0.00005671587],"genre_scores_gemma":[0.9539627,0.00003839529,0.0440091,0.000038043,0.0000319426,0.0002518727,0.00004872309,0.00001156945,0.001607694],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03189903,"threshold_uncertainty_score":0.3188079,"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."}}