{"id":"W2096366860","doi":"10.1152/ajplung.00289.2014","title":"Smart imaging of acute lung injury: exploration of myeloperoxidase activity using in vivo endoscopic confocal fluorescence microscopy","year":2015,"lang":"en","type":"article","venue":"American Journal of Physiology-Lung Cellular and Molecular Physiology","topic":"Neutrophil, Myeloperoxidase and Oxidative Mechanisms","field":"Immunology and Microbiology","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"Fonds Québécois de la Recherche sur la Nature et les Technologies; Réseau en Bio-Imagerie du Quebec","keywords":"In vivo; Myeloperoxidase; Intravital microscopy; Pathology; Confocal; Confocal microscopy; Ex vivo; Endomicroscopy; Fluorescence microscope; Neutrophil extracellular traps; Elastase; Medicine; Inflammation; Chemistry; Immunology; Biology; Fluorescence; Cell biology; Biochemistry; Enzyme","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":[0.0002407278,0.0003271187,0.0001853927,0.0001912844,0.0001048214,0.0002095284,0.0002474994,0.000475401,0.0006041618],"category_scores_gemma":[0.0001466588,0.0001151109,0.0001451147,0.0000804328,0.0002357756,0.000335415,0.000296585,0.0003130751,0.0001518414],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001992118,"about_ca_system_score_gemma":0.0001502963,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000452558,"about_ca_topic_score_gemma":0.0005648125,"domain_scores_codex":[0.9998974,0.0000228008,0.000004612753,0.00002985259,0.00002197136,0.00002344087],"domain_scores_gemma":[0.9999176,0.00002197126,0.0000237012,0.00001112367,0.000015,0.00001055971],"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":[0.00002554956,0.00001073423,0.0001887828,0.00002922522,0.000001978425,0.00003696676,0.00001225704,0.00008693111,0.9978449,0.00009505116,0.00003304978,0.001634411],"study_design_scores_gemma":[0.000006934944,0.0002364179,0.003378981,0.000007753485,0.00001021365,0.0004403852,0.0000307037,0.006183206,0.9877034,0.00007867728,0.001913311,0.000009982728],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9030737,0.001914009,0.09204579,0.0002824713,0.00003064777,0.0001051521,0.0002246052,0.0003063692,0.002017185],"genre_scores_gemma":[0.8996821,0.002160789,0.09582693,0.0001503883,0.00002768739,0.0001260409,0.0002174961,0.00002406516,0.001784525],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0006041618,"threshold_uncertainty_score":0.002021074,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01174481521764775,"score_gpt":0.268324305037821,"score_spread":0.2565794898201732,"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."}}