{"id":"W4386027157","doi":"10.3390/cells12162115","title":"Development of Beta-Amyloid-Specific CAR-Tregs for the Treatment of Alzheimer’s Disease","year":2023,"lang":"en","type":"article","venue":"Cells","topic":"Alzheimer's disease research and treatments","field":"Medicine","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University Health Network","funders":"Technische Universität Braunschweig; Deutsche Forschungsgemeinschaft; European Commission; Government of Canada","keywords":"Alzheimer's disease; BETA (programming language); Amyloid (mycology); Disease; Medicine; Amyloid beta; Neuroscience; Immunology; Biology; Pathology; Computer science","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":[0.0001035245,0.0001678266,0.0003356363,0.0001052742,0.0001061657,0.000006611524,0.00009340404,0.00003412239,0.0001479172],"category_scores_gemma":[0.000006509765,0.00009924934,0.0002780239,0.0002331876,0.000119232,0.00002359937,0.00002629668,0.00002892277,0.0001556641],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006861,"about_ca_system_score_gemma":0.0003713905,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000132995,"about_ca_topic_score_gemma":0.00000340534,"domain_scores_codex":[0.9987299,0.00002147175,0.0003180023,0.0002383979,0.0003703185,0.0003218466],"domain_scores_gemma":[0.998841,0.0001856569,0.00008928885,0.0004480788,0.000122105,0.0003138802],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.01101212,0.02263892,0.04148832,0.00164253,0.1536382,0.0009008893,0.01582209,0.0002084152,0.05363899,0.00145039,0.062153,0.6354061],"study_design_scores_gemma":[0.009688341,0.001735082,0.1325489,0.0001682104,0.006717191,0.000002167962,0.001570553,0.0003491372,0.6870868,0.0001559465,0.1596126,0.000365074],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9332294,0.06032367,0.0000799862,0.0007149678,0.0002643884,0.003873395,0.0005463265,0.00008897877,0.0008788821],"genre_scores_gemma":[0.9952621,0.002535082,0.0008712641,0.00001442608,0.00008453212,0.000405019,0.0002669474,0.00003393957,0.0005266495],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.635041,"threshold_uncertainty_score":0.4047271,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1028382175306936,"score_gpt":0.34392032822198,"score_spread":0.2410821106912864,"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."}}