{"id":"W4386315834","doi":"10.1021/acs.jproteome.3c00281","title":"Digital Microfluidics for Microproteomic Analysis of Minute Mammalian Tissue Samples Enabled by a Photocleavable Surfactant","year":2023,"lang":"en","type":"article","venue":"Journal of Proteome Research","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Canada Research Chairs; Canada Foundation for Innovation; Ontario Research Foundation","keywords":"Proteome; Proteomics; Workflow; Pulmonary surfactant; Microfluidics; Biomarker; Biomarker discovery; Sample preparation; Cell; Chemistry; Computational biology; Chromatography; Computer science; Nanotechnology; Biology; Biochemistry; Materials science","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":[0.001812427,0.0001561367,0.0004574936,0.0005987929,0.0001224981,0.00009657825,0.0004744033,0.0001854553,0.00002622652],"category_scores_gemma":[0.0003758029,0.0001349942,0.0003470004,0.0010721,0.0001950341,0.00001671346,0.00009097622,0.0002411198,0.000005804633],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005097514,"about_ca_system_score_gemma":0.0002932068,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000978597,"about_ca_topic_score_gemma":0.00001723225,"domain_scores_codex":[0.9979767,0.0001144665,0.0006244025,0.0002699327,0.0004914794,0.0005230639],"domain_scores_gemma":[0.9985498,0.00009152741,0.0002455818,0.0002893273,0.0006601131,0.0001636436],"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":[0.0007863974,0.000131837,0.002005572,0.000114728,0.0006867515,0.00001074087,0.00008359008,0.0000291437,0.9839609,0.000002079917,0.01094408,0.001244229],"study_design_scores_gemma":[0.0008836634,0.001166511,0.0003550336,0.00003479148,0.000106449,0.00001132685,0.0001710881,0.0001137516,0.9438782,0.00006120685,0.05308239,0.0001356359],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9939804,0.0008321125,0.003550275,0.0002610699,0.00006943951,0.0007883307,0.0004512603,0.000005332917,0.00006176038],"genre_scores_gemma":[0.991955,0.000643961,0.002708111,0.00001518922,0.0001760167,0.00005038742,0.0002386468,0.00004775889,0.004164869],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04213831,"threshold_uncertainty_score":0.5504903,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0611796768116999,"score_gpt":0.3466075153786268,"score_spread":0.2854278385669269,"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."}}