{"id":"W4377087920","doi":"10.1021/acssensors.2c01349","title":"Aptamer-Based Electrochemical Microfluidic Biosensor for the Detection of <i>Cryptosporidium parvum</i>","year":2023,"lang":"en","type":"article","venue":"ACS Sensors","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Canada; Sunnybrook Health Science Centre; McGill University Health Centre; Health Sciences Centre; University of Calgary; McGill University","funders":"Canada Foundation for Innovation; Natural Sciences and Engineering Research Council of Canada; Faculty of Engineering, McGill University; McGill University","keywords":"Aptamer; Cryptosporidium parvum; Biosensor; Cryptosporidium; Tap water; Detection limit; Microfluidics; Colloidal gold; Nanotechnology; Chromatography; Chemistry; Microbiology; Biology; Materials science; Molecular biology; Nanoparticle; Environmental science; Environmental engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002118805,0.0001608049,0.0001702461,0.00006880829,0.0001157744,0.000009639873,0.0001538678,0.000170217,3.987109e-7],"category_scores_gemma":[0.0001839284,0.0001174266,0.0002028033,0.0003559615,0.0001586238,0.000001896142,0.00003495546,0.00008314371,0.0000038434],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001228947,"about_ca_system_score_gemma":0.00003350425,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007525498,"about_ca_topic_score_gemma":0.000004987623,"domain_scores_codex":[0.9990047,0.00003549388,0.0002335567,0.0003148244,0.0001241583,0.0002872263],"domain_scores_gemma":[0.9992036,0.00009489129,0.0001226842,0.0003858999,0.0001536699,0.00003921921],"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":[0.0001485111,0.00002278952,0.00006285993,0.00001437181,0.00005914968,6.624014e-7,0.000005524519,0.000008932123,0.9961271,0.000008488772,0.001581226,0.001960399],"study_design_scores_gemma":[0.0002470338,0.0002137759,0.00009808944,0.000007176053,0.00006601519,0.000004880454,0.00005587647,0.0002360796,0.9639196,0.00004541694,0.03495712,0.000148874],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9886934,0.0005022336,0.009724256,0.0005440143,0.00008423467,0.0002901811,0.00002896986,0.0001055802,0.00002716665],"genre_scores_gemma":[0.9976434,0.0007171539,0.0008370773,0.0002260102,0.0001886184,0.00002390986,0.0001137732,0.00002886347,0.0002212445],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0333759,"threshold_uncertainty_score":0.4788518,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01059722296225107,"score_gpt":0.2594662747440972,"score_spread":0.2488690517818462,"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."}}