{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001735423,0.0004378135,0.0001551931,0.0001614509,0.00007894026,0.0001440296,0.000217743,0.0003363902,0.000518101],"category_scores_gemma":[0.0001694741,0.0001514934,0.0001413217,0.0001042439,0.0001593219,0.0001895393,0.0001722597,0.0003142863,0.0002432595],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001905196,"about_ca_system_score_gemma":0.0001165107,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002805197,"about_ca_topic_score_gemma":0.0004911876,"domain_scores_codex":[0.9998442,0.00003073069,0.00001290035,0.00003584957,0.00005929,0.00001714778],"domain_scores_gemma":[0.9999163,0.00001726434,0.00002943493,0.000007766257,0.00001851874,0.0000107137],"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":[0.000008530508,0.00000401154,0.00003485544,0.00001263398,0.000001998523,0.00001700497,0.00000420227,0.00002842247,0.9993556,0.00002425205,0.00001491092,0.0004935493],"study_design_scores_gemma":[0.000002097773,0.0000450805,0.0002617454,7.154036e-7,0.000002636997,0.00007365351,0.000002493982,0.0009719988,0.9983042,0.000008990984,0.0003242217,0.000002001314],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9543352,0.0009161169,0.04281009,0.0001219296,0.00005040631,0.0000553206,0.0001107862,0.0002515882,0.001348632],"genre_scores_gemma":[0.9656075,0.0003821897,0.0311559,0.00007372145,0.00001256018,0.00003954026,0.0001381871,0.00001894324,0.002571419],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.000518101,"threshold_uncertainty_score":0.001733243,"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."}}