{"id":"W2889905864","doi":"10.1096/fj.201800987r","title":"Characterization of heat shock protein 27 in extracellular vesicles: a potential anti‐inflammatory therapy","year":2018,"lang":"en","type":"article","venue":"The FASEB Journal","topic":"Extracellular vesicles in disease","field":"Biochemistry, Genetics and Molecular Biology","cited_by":78,"is_retracted":false,"has_abstract":true,"ca_institutions":"Libin Cardiovascular Institute of Alberta; University of Calgary","funders":"Canadian Institutes of Health Research; Heart and Stroke Foundation of Canada","keywords":"Heat shock protein; Extracellular vesicles; Extracellular; Characterization (materials science); Chemistry; Extracellular vesicle; Inflammation; Vesicle; Biophysics; Microvesicles; Cell biology; Medicine; Biochemistry; Biology; Immunology; Nanotechnology; Materials science; microRNA; Membrane","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":[0.0005733387,0.0001499596,0.0001402124,0.00006615165,0.0001181044,0.00003956117,0.000362315,0.0001115401,0.00009400069],"category_scores_gemma":[0.0000335646,0.0001137256,0.0001163268,0.0001034911,0.0002410327,0.00001410725,0.00006496454,0.0001810322,0.00001472787],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001740803,"about_ca_system_score_gemma":0.0001297764,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003259361,"about_ca_topic_score_gemma":0.000002983934,"domain_scores_codex":[0.9987136,0.0002358258,0.0003856944,0.0001895523,0.0002230201,0.0002522702],"domain_scores_gemma":[0.9992415,0.000004480672,0.0001760025,0.0003664052,0.0001236662,0.00008794859],"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":[0.0002956312,0.0001214236,0.002785724,0.000009932204,0.00003559147,0.00003030839,0.0001483352,0.00003680526,0.9950982,0.00002575281,0.00003532587,0.001376958],"study_design_scores_gemma":[0.0008424194,0.0002408749,0.007707365,0.00004644314,0.0000108014,0.00008849773,0.00007222378,0.000231646,0.9897534,0.0001076674,0.0007657268,0.0001329859],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.995263,0.001230281,0.002791747,0.0001332081,0.0002008125,0.0003062453,0.00001042041,0.000006486853,0.0000577303],"genre_scores_gemma":[0.9984418,0.0002529585,0.0001455526,0.00006916538,0.0007558136,0.00000709802,0.00002665392,0.00002882344,0.0002721896],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005344864,"threshold_uncertainty_score":0.4637595,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008100226069848577,"score_gpt":0.2246276522890043,"score_spread":0.2165274262191557,"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."}}