{"id":"W4390973582","doi":"10.1038/s41598-024-51864-4","title":"Ultrasensitive detection of vital biomolecules based on a multi-purpose BioMEMS for Point of care testing: digoxin measurement as a case study","year":2024,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Mechanical and Optical Resonators","field":"Physics and Astronomy","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Centre Hospitalier Universitaire de Sherbrooke; Université de Sherbrooke","funders":"","keywords":"Analyte; Repeatability; Point of care; Therapeutic drug monitoring; Detection limit; Biomedical engineering; Biosensor; Materials science; Computer science; Microelectromechanical systems; Reproducibility; Biomolecule; Nanotechnology; Chromatography; Chemistry; Drug; Medicine; Pharmacology; Pathology","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":[0.0005231889,0.0005661532,0.0004195839,0.0004633262,0.0002355188,0.0004678868,0.00059202,0.001577698,0.0002906928],"category_scores_gemma":[0.00032145,0.0002010254,0.0002908511,0.0002640885,0.0003637726,0.0003311716,0.0004504115,0.0004403315,0.0002143162],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002961341,"about_ca_system_score_gemma":0.0001576321,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002752308,"about_ca_topic_score_gemma":0.0004510222,"domain_scores_codex":[0.999459,0.0001234249,0.0000236081,0.0001117901,0.0002414163,0.00004080577],"domain_scores_gemma":[0.9998521,0.00003260744,0.00003056445,0.00001672205,0.00004692114,0.00002111202],"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":[0.0000537271,0.00004219047,0.0003698982,0.00008070863,0.00001052682,0.0004655778,0.00005672278,0.0001702634,0.9934275,0.000177447,0.000108877,0.005036493],"study_design_scores_gemma":[0.000009968706,0.0006358082,0.001379599,0.00001258455,0.00003345765,0.00187769,0.00005668334,0.003793658,0.9893835,0.0001029023,0.002694171,0.00001985011],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8898928,0.01467694,0.09049699,0.001144775,0.0002718745,0.0003428297,0.000150853,0.0004644705,0.002558509],"genre_scores_gemma":[0.91348,0.002914166,0.08077006,0.000282717,0.00006464064,0.00007486613,0.00006844924,0.00001470099,0.002330226],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001577698,"threshold_uncertainty_score":0.002766967,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03410404740714461,"score_gpt":0.2921960541598562,"score_spread":0.2580920067527116,"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."}}