{"id":"W3000921294","doi":"10.1039/c9ay02310k","title":"A novel signal amplification strategy for highly specific and nonenzymatic isothermal electrochemiluminescence detection of tumour markers","year":2020,"lang":"en","type":"article","venue":"Analytical Methods","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Infection and Immunity","funders":"Natural Science Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Electrochemiluminescence; Detection limit; Loop-mediated isothermal amplification; SIGNAL (programming language); Isothermal process; Limit (mathematics); Chemistry; Chromatography; DNA; Computer science; Physics; Biochemistry; Mathematics","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":[0.0003221125,0.0001297049,0.0002141447,0.00003533939,0.00003971149,0.00001462005,0.00008714899,0.0001129914,0.00000143772],"category_scores_gemma":[0.0002044425,0.0001151474,0.0001034521,0.000181913,0.00011614,0.00000471424,0.00002713722,0.0000704735,2.283645e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008436227,"about_ca_system_score_gemma":0.00002079914,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003393601,"about_ca_topic_score_gemma":0.00000103897,"domain_scores_codex":[0.9990839,0.00006491623,0.0002702358,0.0003385005,0.00008062019,0.0001618401],"domain_scores_gemma":[0.9994648,0.0000700336,0.0001234311,0.0001319995,0.000119993,0.00008978484],"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":[0.0002139986,0.00002970114,0.0000187866,0.0000372114,0.00004673008,1.581789e-7,0.000005707995,0.000007504209,0.9785421,0.00009772969,0.00004464629,0.02095572],"study_design_scores_gemma":[0.000215625,0.0005023354,0.0003498718,0.000008934579,0.00007286108,0.000005796829,0.0000426376,0.01963397,0.9782632,0.0001053164,0.0006646817,0.0001347581],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09622904,0.0001769211,0.9031368,0.0002119617,0.000007111395,0.0001452449,0.000008905064,0.00001759595,0.00006635068],"genre_scores_gemma":[0.7649857,0.00006040742,0.23472,0.00009171603,0.00008816345,0.000007078078,0.00001683615,0.00001103615,0.00001903977],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6687567,"threshold_uncertainty_score":0.4695573,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03947781033220291,"score_gpt":0.3441717493544608,"score_spread":0.3046939390222579,"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."}}