{"id":"W4403214114","doi":"10.1021/acssensors.4c01604","title":"A Nitric Oxide-Sensing <i>T</i><sub>1</sub> Contrast Agent for In Vivo Molecular MR Imaging of Inflammatory Disease","year":2024,"lang":"en","type":"article","venue":"ACS Sensors","topic":"Electron Spin Resonance Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ted Rogers Centre for Heart Research; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Ministry of Research, Innovation and Science; University of Toronto; Canada Foundation for Innovation; Government of Ontario","keywords":"In vivo; Nitric oxide; Contrast (vision); Molecular imaging; Chemistry; Medicine; Nanotechnology; Biomedical engineering; Materials science; Nuclear magnetic resonance; Pathology; Computer science; Internal medicine; Biology; Physics; Artificial intelligence; Biotechnology","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":[0.0001540097,0.0001915111,0.0001986063,0.000117982,0.00004005038,0.00002088112,0.0000927959,0.00005920334,6.883826e-7],"category_scores_gemma":[0.0001790881,0.0001978094,0.0001410925,0.0001717696,0.0001007911,0.000005466529,0.00007644869,0.00009164729,0.000002692834],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003562398,"about_ca_system_score_gemma":0.0001100576,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006485089,"about_ca_topic_score_gemma":0.0000159551,"domain_scores_codex":[0.9987812,0.00004455868,0.0002829772,0.0003895974,0.0001371486,0.0003645424],"domain_scores_gemma":[0.999504,0.00003958098,0.00006179536,0.0002426908,0.0000883887,0.00006357912],"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":[0.00008439485,0.00001912897,0.00199081,0.0001589676,0.0000644367,0.0001223334,0.00003654053,0.0002298104,0.9942764,0.00009930927,0.0008745402,0.002043307],"study_design_scores_gemma":[0.0003826693,0.00002847817,0.002086978,0.0001194047,0.00004590922,0.000008748972,0.00008328708,0.0005765356,0.9929637,0.0001394454,0.00335158,0.0002132596],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9858222,0.01247283,0.0009645344,0.0001687599,0.00007431442,0.0003553322,0.00003347959,0.00002526354,0.0000832852],"genre_scores_gemma":[0.9987463,0.0006484537,0.0002018051,0.0001951779,0.00007780101,0.00002619306,0.00001613664,0.00004668588,0.00004149082],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01292405,"threshold_uncertainty_score":0.8066435,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005011989794555478,"score_gpt":0.2328201287704396,"score_spread":0.2278081389758841,"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."}}