{"id":"W2807182714","doi":"10.1007/s10753-018-0787-6","title":"Quantification of Inflammasome Adaptor Protein ASC in Biological Samples by Multiple-Reaction Monitoring Mass Spectrometry","year":2018,"lang":"en","type":"article","venue":"Inflammation","topic":"Inflammasome and immune disorders","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"Canada Research Chairs; FP7 People: Marie-Curie Actions; Canadian Institutes of Health Research; Alberta Innovates - Health Solutions","keywords":"Inflammasome; Selected reaction monitoring; Urine; Chemistry; Mass spectrometry; Biomarker; Quantitative proteomics; Urinalysis; Proteomics; Tandem mass spectrometry; Medicine; Inflammation; Chromatography; Internal medicine; Biochemistry","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":[0.0003411721,0.0001570772,0.0001589021,0.0001489636,0.00006315285,0.00001941665,0.0001546575,0.0002326847,0.00001219761],"category_scores_gemma":[0.0003629075,0.000155935,0.00006382899,0.0002242568,0.0001115049,0.00002954893,0.0000364659,0.0001002982,0.00001998256],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003949837,"about_ca_system_score_gemma":0.00003745897,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006689186,"about_ca_topic_score_gemma":0.00003748886,"domain_scores_codex":[0.9987615,0.00009205869,0.0004703255,0.0002965439,0.0001517926,0.0002277936],"domain_scores_gemma":[0.9992498,0.00002051293,0.0002538604,0.000290979,0.0001546242,0.00003028164],"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":[0.000190054,0.00003107445,0.03847952,0.00001782637,0.00001863447,2.543752e-7,0.00006472947,0.00001463457,0.9569493,0.00009447385,0.00003711216,0.004102398],"study_design_scores_gemma":[0.0005743035,0.0002572475,0.06268217,0.00003486553,0.000004212296,9.106902e-7,0.0001869607,0.0001047827,0.9325299,0.0001026606,0.003356561,0.0001654277],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9944564,0.0001605277,0.004385134,0.00007337561,0.0001491168,0.0004059196,0.0000132039,0.00002218172,0.0003341435],"genre_scores_gemma":[0.9975213,0.00009025008,0.001519613,0.000004204941,0.0003692192,0.00007132302,0.0003194823,0.00001669271,0.00008792018],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02441938,"threshold_uncertainty_score":0.6358846,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02086612422679131,"score_gpt":0.2560604065269225,"score_spread":0.2351942823001312,"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."}}