{"id":"W2765319200","doi":"10.2147/ijn.s145585","title":"Highly sensitive protein detection via covalently linked aptamer to MoS<sub>2</sub> and exonuclease-assisted amplification strategy","year":2017,"lang":"en","type":"article","venue":"International Journal of Nanomedicine","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Priority Academic Program Development of Jiangsu Higher Education Institutions; Natural Science Foundation of Liaoning Province; Government of Jiangsu Province; Jiangsu University","keywords":"Aptamer; Exonuclease; Nanosheet; Detection limit; Covalent bond; Thrombin; Biosensor; Exonuclease III; Combinatorial chemistry; Nanotechnology; Chemistry; Colloidal gold; Molecular beacon; Biophysics; DNA; Materials science; Nanoparticle; Oligonucleotide; Molecular biology; Biochemistry; Chromatography; DNA polymerase; Biology","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":[0.0003929607,0.0001852675,0.0002571015,0.0002157392,0.0001492476,0.00008434305,0.0002781809,0.0001466244,0.000001733136],"category_scores_gemma":[0.0003055545,0.0001529867,0.0001120233,0.00006718602,0.000180816,0.00002967445,0.00009339573,0.000154598,0.000003157503],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000771764,"about_ca_system_score_gemma":0.0000570276,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001311638,"about_ca_topic_score_gemma":0.00005359264,"domain_scores_codex":[0.9985862,0.00006105792,0.0004857059,0.0002876839,0.0004196851,0.0001597114],"domain_scores_gemma":[0.9976804,0.0000161851,0.0007661819,0.0002714539,0.001110197,0.0001555474],"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":[0.0004840742,0.00007222724,0.0001051818,0.000005725183,0.0002137825,0.00005264005,0.00001797893,0.000005604349,0.9102101,0.00001832334,0.0001380553,0.08867632],"study_design_scores_gemma":[0.0009804892,0.0009215391,0.0303627,0.0001669282,0.00008405031,0.0004209212,0.00003610202,0.0001545957,0.9613469,0.0001443899,0.00519487,0.0001865586],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9723579,0.0001454793,0.02483787,0.002019636,0.0002713385,0.0001957107,0.00002147014,0.00001496498,0.0001356355],"genre_scores_gemma":[0.9963756,0.0002652181,0.001983357,0.0002882132,0.0008589042,0.00000500906,0.00004065391,0.00002010564,0.0001629794],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08848976,"threshold_uncertainty_score":0.6238617,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01406740062570554,"score_gpt":0.2859106788358502,"score_spread":0.2718432782101446,"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."}}