{"id":"W4366783097","doi":"10.1002/advs.202207223","title":"Liquid NanoBiosensors Enable One‐Pot Electrochemical Detection of Bacteria in Complex Matrices","year":2023,"lang":"en","type":"article","venue":"Advanced Science","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hamilton Regional Laboratory Medicine Program; Hamilton General Hospital; McMaster University","funders":"Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; McMaster University","keywords":"Escherichia coli; Bacillus subtilis; Bacteria; Deoxyribozyme; Detection limit; Nucleic acid; Klebsiella pneumoniae; Chemistry; DNA; Biosensor; Microbiology; Computational biology; Biology; Nanotechnology; Combinatorial chemistry; Chromatography; Biochemistry; Materials science","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005850816,0.0006027489,0.0003750599,0.0002784265,0.0001594916,0.0007649649,0.0006551036,0.0006160878,0.001535348],"category_scores_gemma":[0.0006444695,0.0004747884,0.0001984272,0.000162545,0.0004816426,0.0007722857,0.0006161911,0.0009377773,0.001007563],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002642842,"about_ca_system_score_gemma":0.0002045024,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001358134,"about_ca_topic_score_gemma":0.0004425603,"domain_scores_codex":[0.9993355,0.0001278225,0.0000293806,0.0001203057,0.0003423639,0.00004459512],"domain_scores_gemma":[0.9996228,0.000155108,0.00009036101,0.00003165904,0.00006520475,0.00003482668],"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":[0.00001478812,0.00001370329,0.00004477634,0.00005279435,0.000004339997,0.00001875516,0.00001146502,0.000103654,0.9958748,0.0002397307,0.000126529,0.003494539],"study_design_scores_gemma":[0.000004210966,0.00008047591,0.0001646539,0.000008311396,0.000003498693,0.00006016559,0.00001217118,0.002286144,0.9949514,0.0001799779,0.002241826,0.000007177208],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3175928,0.007327307,0.6623625,0.00115665,0.0006015871,0.0003891183,0.0005849215,0.002565921,0.007419222],"genre_scores_gemma":[0.7265715,0.003083064,0.2548611,0.0007793639,0.0001045066,0.0004046847,0.0004159434,0.0001422124,0.01363756],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001535348,"threshold_uncertainty_score":0.005136251,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01276494805585296,"score_gpt":0.2816624062287005,"score_spread":0.2688974581728475,"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."}}