{"id":"W4393991575","doi":"10.1021/acsanm.4c00674","title":"Label-Free Analysis of Protein Biomarkers Using Pattern-Optimized Graphene-Nanopyramid SERS for the Rapid Diagnosis of Alzheimer’s Disease","year":2024,"lang":"en","type":"article","venue":"ACS Applied Nano Materials","topic":"Spectroscopy Techniques in Biomedical and Chemical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Fundamental Research Funds for the Central Universities; Higher Education Discipline Innovation Project; China Postdoctoral Science Foundation","keywords":"Raman spectroscopy; Graphene; Substrate (aquarium); Analyte; Wafer; Chemistry; Nanotechnology; Nanostructure; Materials science; Analytical Chemistry (journal); Chromatography; Optics; Biology; Physics","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":[0.0003409368,0.0006172553,0.0003618247,0.0003175984,0.0001253434,0.0003352415,0.0004442455,0.0006515587,0.0004539681],"category_scores_gemma":[0.0003362468,0.0002672307,0.0003996927,0.0002694858,0.0003596361,0.0005222367,0.000316312,0.0004092907,0.0002425751],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002979298,"about_ca_system_score_gemma":0.0001101852,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003661436,"about_ca_topic_score_gemma":0.0007040037,"domain_scores_codex":[0.9997026,0.00005375019,0.00001594207,0.00006859456,0.0001269861,0.00003194074],"domain_scores_gemma":[0.9998733,0.00005089106,0.00002957489,0.00001533529,0.00002263299,0.000008259769],"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":[0.0000299407,0.00001231905,0.00007325017,0.00005780772,0.000006216883,0.00003092885,0.00001404724,0.0003899363,0.9974678,0.00007080028,0.00002452404,0.001822566],"study_design_scores_gemma":[0.000008510396,0.0001108404,0.001111813,0.000003830073,0.00001422361,0.00006249358,0.00001777824,0.01098264,0.9870921,0.0001031147,0.0004753695,0.00001735555],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9448418,0.002131166,0.05083127,0.0001376423,0.00007924,0.00005637938,0.0002893673,0.0003249428,0.001308144],"genre_scores_gemma":[0.9465271,0.00111916,0.05102513,0.00008235859,0.00002251857,0.00004286466,0.0001974881,0.00003824891,0.0009450626],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0006515587,"threshold_uncertainty_score":0.002161682,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02496967768743294,"score_gpt":0.3225325258788486,"score_spread":0.2975628481914157,"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."}}