{"id":"W3217426506","doi":"10.3390/s21237851","title":"Development of Electrochemical Aptasensor for Lung Cancer Diagnostics in Human Blood","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Ministry of Science and Higher Education of the Russian Federation; Tomsk State University","keywords":"Aptamer; Dielectric spectroscopy; Materials science; Electrode; Electrochemistry; Oligonucleotide; Lung cancer; Nanotechnology; Biomedical engineering; Chemistry; Pathology; Medicine; DNA; Molecular biology; Biology; Biochemistry","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":[0.0006966305,0.0004929538,0.0004613696,0.0003613574,0.0001390953,0.0003601285,0.0005002312,0.0008554698,0.0008075671],"category_scores_gemma":[0.0007100966,0.0003058308,0.0002485856,0.0002380382,0.0001348995,0.0003451036,0.0002897202,0.0006895018,0.0009089945],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001985201,"about_ca_system_score_gemma":0.0002416147,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003544358,"about_ca_topic_score_gemma":0.0007467962,"domain_scores_codex":[0.9995496,0.0000968985,0.00003445914,0.0001376752,0.0001543721,0.00002696662],"domain_scores_gemma":[0.9997385,0.00008699049,0.00002609802,0.00002066641,0.00009585541,0.00003189976],"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":[0.00003085939,0.00002654606,0.0003067827,0.00008234233,0.00001247653,0.00005888036,0.00003052736,0.0001747257,0.9876899,0.0001464181,0.0001122455,0.01132839],"study_design_scores_gemma":[0.00001058159,0.0002574509,0.001307784,0.0000111876,0.00002277577,0.0007239903,0.00001870957,0.006564238,0.9863886,0.0001780759,0.004501514,0.00001508319],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2812075,0.02191146,0.6865399,0.001183234,0.0004908559,0.0006526567,0.0008790613,0.002598404,0.004536834],"genre_scores_gemma":[0.5356368,0.007301891,0.4483069,0.0008777616,0.0001077715,0.0004204491,0.0006906951,0.00006851459,0.006589207],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0008554698,"threshold_uncertainty_score":0.003684163,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009263913690850426,"score_gpt":0.3037672304794264,"score_spread":0.2945033167885759,"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."}}