{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007731517,0.0001722937,0.0003220443,0.0002568578,0.00001447083,0.000003817473,0.0002078802,0.0001795135,0.000004034284],"category_scores_gemma":[0.0002428746,0.0001633132,0.0001248884,0.0003415126,0.0001673194,0.000002877978,0.00006798105,0.000134881,3.148876e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000192328,"about_ca_system_score_gemma":0.0000265329,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002825872,"about_ca_topic_score_gemma":0.00001239179,"domain_scores_codex":[0.9983395,0.0005043707,0.0003844309,0.000421581,0.00005153893,0.0002986011],"domain_scores_gemma":[0.9994,0.00002306271,0.0001218261,0.0003742975,0.0000438157,0.00003705835],"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":[0.00003568486,0.00005815036,0.00233848,0.00000370809,0.00001065664,0.00001607772,0.00001188909,0.00001298723,0.9778349,0.0005705638,0.00003968599,0.01906721],"study_design_scores_gemma":[0.0002820678,0.0001690092,0.0004706577,0.00001537226,0.000009286457,0.00001412684,0.00003037675,0.000123975,0.9906747,0.005436927,0.002594719,0.0001787359],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3213764,0.001164706,0.6742521,0.0006055528,0.0001002203,0.0002066257,0.000008018875,0.00002595649,0.002260412],"genre_scores_gemma":[0.5273672,0.0001422295,0.4716891,0.000730708,0.00002001885,0.00000310828,0.000007934135,0.000009100868,0.00003056608],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.2059908,"threshold_uncertainty_score":0.665972,"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."}}