{"id":"W2984415080","doi":"10.1002/mrm.28072","title":"MRI method for labeling and imaging decellularized extracellular matrix scaffolds for tissue engineering","year":2019,"lang":"en","type":"article","venue":"Magnetic Resonance in Medicine","topic":"Tissue Engineering and Regenerative Medicine","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Institute for Regenerative Medicine; Toronto General Hospital; University Health Network; Ted Rogers Centre for Heart Research; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Decellularization; In vivo; Extracellular matrix; Biomedical engineering; Ex vivo; Chemistry; Matrix (chemical analysis); Biophysics; Medicine; Biology; Biochemistry; Chromatography","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001264816,0.0003381983,0.0008933406,0.0003365192,0.00004997382,0.00001291745,0.0001146904,0.0001273495,0.0001190831],"category_scores_gemma":[0.0004438368,0.0002714383,0.00006059413,0.0003143407,0.00008163703,0.00005023663,0.00003160289,0.0001926686,0.000005124768],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000680718,"about_ca_system_score_gemma":0.00004278855,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004784475,"about_ca_topic_score_gemma":0.000004117047,"domain_scores_codex":[0.9979545,0.00003281004,0.0005879758,0.0005785152,0.0002965929,0.0005496412],"domain_scores_gemma":[0.9986207,0.00061919,0.00007747691,0.0003617165,0.0001260268,0.0001949206],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003307344,0.00006208373,0.005575354,0.001800044,0.00002488665,0.00008456865,0.000838652,0.0009562827,0.8789756,0.003222547,0.001377396,0.1067518],"study_design_scores_gemma":[0.01587748,0.001737501,0.003928794,0.002508186,0.0002147425,0.0002379862,0.000325032,0.4901437,0.01234875,0.0001489258,0.4720847,0.0004441614],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"review","genre_gemma":"methods","genre_scores_codex":[0.1245232,0.4873718,0.3773543,0.006563907,0.0009716292,0.003016999,0.000009646747,0.0001259903,0.00006260841],"genre_scores_gemma":[0.1500734,0.001813615,0.7949445,0.0002164325,0.001818691,0.0006402365,0.0001022017,0.0002251086,0.05016581],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8666269,"threshold_uncertainty_score":0.9999738,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00972695948872892,"score_gpt":0.3018001673492408,"score_spread":0.2920732078605119,"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."}}