{"id":"W4416203180","doi":"10.1126/sciadv.adz8096","title":"An ultrasensitive and modular platform to detect Siglec ligands and control immune cell function","year":2025,"lang":"en","type":"article","venue":"Science Advances","topic":"Biochemical and Structural Characterization","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; BC Children's Hospital; University of Alberta","funders":"National Institute of Diabetes and Digestive and Kidney Diseases; Canadian Glycomics Network; National Institute of Allergy and Infectious Diseases; Natural Sciences and Engineering Research Council of Canada; Networks of Centres of Excellence of Canada; Canadian Institutes of Health Research; Children's Hospital Foundation; Canada Research Chairs; National Institutes of Health; National Heart, Lung, and Blood Institute; Alberta Innovates; BC Children's Hospital","keywords":"SIGLEC; Glycan; Immune system; Cell; MUC1; Receptor; Function (biology); CD22; Modular design","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":[0.0002921965,0.000462975,0.0002012907,0.0003119742,0.000145895,0.0003137301,0.0003992908,0.0005829975,0.0009064884],"category_scores_gemma":[0.0002039335,0.0002473512,0.0002332246,0.0001557651,0.0002759795,0.000466309,0.0004112437,0.0007823269,0.0004124879],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003337071,"about_ca_system_score_gemma":0.0002490316,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001986187,"about_ca_topic_score_gemma":0.0003132346,"domain_scores_codex":[0.9998123,0.00002776854,0.000008488933,0.00004714228,0.00006988859,0.00003452416],"domain_scores_gemma":[0.9998984,0.00002050542,0.00002494937,0.00001435182,0.00002164263,0.0000200677],"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":[0.00001741088,0.00001083375,0.00003633562,0.00001554013,0.000002162798,0.00001327613,0.000005048467,0.00009396052,0.9976516,0.0002498337,0.00007570843,0.001828246],"study_design_scores_gemma":[0.000007143676,0.0001015095,0.0002633745,0.000002003172,0.000005319818,0.00007312753,0.000005369094,0.001654899,0.9954615,0.00008491707,0.002334319,0.000006463123],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7626148,0.003557171,0.2256047,0.0007351083,0.0002228133,0.000318528,0.0009234809,0.001561554,0.004461709],"genre_scores_gemma":[0.8658612,0.001260143,0.1248525,0.0004309884,0.00005760654,0.0003273786,0.0006648072,0.00006241698,0.006483009],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009064884,"threshold_uncertainty_score":0.003032565,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002170343535567523,"score_gpt":0.2197812070997088,"score_spread":0.2176108635641413,"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."}}