{"id":"W4413301173","doi":"10.1021/acs.analchem.5c02472","title":"Multi-Cantilevered Tetrahedral DNA Spider (TDSpider): An Efficient Molecular Machine for Biometrics and Disease Diagnosis","year":2025,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Fundamental Research Funds for the Central Universities; Sichuan Province Science and Technology Support Program; National Natural Science Foundation of China","keywords":"Chemistry; Spider; Tetrahedron; DNA; Computational biology; Biometrics; Cantilever; Nanotechnology; Artificial intelligence; Biochemistry; Crystallography; Structural engineering; Zoology; Computer science","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":[0.0001619791,0.0001839469,0.0001669784,0.0001979244,0.0001077657,0.0002036995,0.0003225669,0.0005148894,0.0006605721],"category_scores_gemma":[0.0002125576,0.0001446855,0.0001435786,0.0001541827,0.0002751959,0.0003149018,0.0003408806,0.0002762614,0.0002681738],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002170074,"about_ca_system_score_gemma":0.0001292597,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001231163,"about_ca_topic_score_gemma":0.0002903789,"domain_scores_codex":[0.9998633,0.00001978484,0.000007837809,0.00004978502,0.00004801336,0.00001120653],"domain_scores_gemma":[0.9998889,0.00002335815,0.0000320024,0.00001029511,0.000019336,0.00002622133],"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":[0.00003653486,0.00001472441,0.0004873126,0.0001102749,0.000006611593,0.00007590187,0.00002982719,0.0005793521,0.9748039,0.001195589,0.0002663378,0.02239366],"study_design_scores_gemma":[0.00002470416,0.0006581613,0.002875746,0.00001766876,0.0000216623,0.001374861,0.00005092484,0.02794471,0.9422016,0.0009912326,0.02379711,0.00004168213],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7846973,0.007706368,0.1974833,0.0007750069,0.0003111125,0.0001382571,0.0003422084,0.0008958793,0.007650631],"genre_scores_gemma":[0.8840416,0.001118885,0.1102463,0.0001968368,0.00002819181,0.00005391522,0.0001623528,0.00002026251,0.004131584],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0006605721,"threshold_uncertainty_score":0.002209842,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0129319605055873,"score_gpt":0.3094835660331527,"score_spread":0.2965516055275654,"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."}}