{"id":"W2402856136","doi":"10.1007/978-1-59745-557-2_15","title":"In Vivo Imaging of Oligonucleotidic Aptamers","year":2009,"lang":"en","type":"article","venue":"Methods in molecular biology","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institute of Cancer Research; Agence Nationale de la Recherche; Institut National Du Cancer; Fondation de France; Association pour la Recherche sur le Cancer","keywords":"Aptamer; Positron emission tomography; In vivo; Preclinical imaging; Molecular imaging; Pet imaging; Labelling; Positron; Optical imaging; Fluorescence; Chemistry; Medical physics; Nanotechnology; Biomedical engineering; Materials science; Nuclear medicine; Molecular biology; Medicine; Biology; Optics; Physics; Biochemistry; Genetics; Nuclear physics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002097297,0.0004937074,0.000275991,0.0002390487,0.0001312668,0.000358391,0.0003308279,0.0004734509,0.002457859],"category_scores_gemma":[0.0001285198,0.0002315535,0.0001743625,0.000116912,0.0001669166,0.000281605,0.000193217,0.0006078578,0.0008735617],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002585763,"about_ca_system_score_gemma":0.0001472195,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003363972,"about_ca_topic_score_gemma":0.0005314045,"domain_scores_codex":[0.9998939,0.00002552406,0.000004940808,0.00002790291,0.00002653404,0.00002121651],"domain_scores_gemma":[0.9999301,0.00002720709,0.00001235106,0.000007979222,0.00001019801,0.0000120421],"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":[0.00003489299,0.000009304145,0.0000233292,0.0000589915,0.000003522636,0.00002798337,0.000009704017,0.0001071914,0.9969854,0.0001846418,0.00007824307,0.00247674],"study_design_scores_gemma":[0.00001556255,0.0002097405,0.0004438458,0.00001135646,0.00001664819,0.0003491539,0.00001195399,0.001219232,0.9898053,0.0001173568,0.007793819,0.00000600289],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5171871,0.0270358,0.426916,0.0007909667,0.0004593249,0.0003632942,0.00111886,0.001433206,0.02469546],"genre_scores_gemma":[0.7259342,0.02909304,0.2047824,0.0006492453,0.0001726888,0.0004759246,0.001349836,0.0002552947,0.03728737],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.002457859,"threshold_uncertainty_score":0.008222342,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009764044917254756,"score_gpt":0.3789037582052572,"score_spread":0.3691397132880025,"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."}}