{"id":"W2790394912","doi":"10.1021/acs.bioconjchem.8b00067","title":"Gastrin-Releasing Peptide Receptor- and Prostate-Specific Membrane Antigen-Specific Ultrasmall Gold Nanoparticles for Characterization and Diagnosis of Prostate Carcinoma via Fluorescence Imaging","year":2018,"lang":"en","type":"article","venue":"Bioconjugate Chemistry","topic":"Gold and Silver Nanoparticles Synthesis and Applications","field":"Materials Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Bundesministerium für Wirtschaft und Energie; Bundesministerium für Bildung und Forschung","keywords":"Chemistry; Colloidal gold; Glutamate carboxypeptidase II; Prostate cancer; Biodistribution; In vivo; Positron emission tomography; Fluorescence; Peptide; Molecular imaging; Homogeneous; In vitro; Cancer research; Biophysics; Nanoparticle; Nanotechnology; Cancer; Internal medicine; Biochemistry; Nuclear medicine; Medicine; Materials science","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0001959053,0.0002088728,0.0002461807,0.0000269649,0.0002178563,0.0001556915,0.0001435746,0.00005321406,0.00006992126],"category_scores_gemma":[0.00002418434,0.0001960156,0.00004141817,0.0001269437,0.0004775476,0.0002138701,0.00006194373,0.0000474861,0.00001078647],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002345066,"about_ca_system_score_gemma":0.00002245265,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001419179,"about_ca_topic_score_gemma":5.903557e-7,"domain_scores_codex":[0.9985178,0.00002469988,0.0004214438,0.0005301672,0.0001379016,0.0003680491],"domain_scores_gemma":[0.9991244,0.00007885279,0.0002217269,0.0002632678,0.0001685142,0.0001432267],"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":[0.0001021429,0.00007212776,0.02289839,0.0001386695,0.000003729736,7.838088e-7,0.0003572521,2.758332e-7,0.9676327,0.00004952824,0.00009920639,0.008645269],"study_design_scores_gemma":[0.0004800797,0.00004175804,0.013449,0.0001177153,0.000009795641,0.000009433169,0.000159386,0.0001173579,0.9797558,0.00002716914,0.005609239,0.0002233206],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9983159,0.0005199363,0.00006218472,0.0002547165,0.00006005954,0.0005163853,0.0001893129,0.00005318941,0.00002826172],"genre_scores_gemma":[0.9962345,0.001669288,0.0016431,0.0000212598,0.0001419731,0.0001621355,0.00004735497,0.00002713328,0.00005326949],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01212312,"threshold_uncertainty_score":0.7993284,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01343662278747314,"score_gpt":0.2128175276540445,"score_spread":0.1993809048665713,"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."}}