{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000434019,0.0003767665,0.0002281459,0.000324512,0.0001857043,0.0003575546,0.0002959385,0.0003065793,0.000695199],"category_scores_gemma":[0.0004624523,0.0001837667,0.0002557488,0.0001794616,0.0003202937,0.0002727305,0.000331351,0.0003102281,0.0003042148],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000387396,"about_ca_system_score_gemma":0.0003804361,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003152968,"about_ca_topic_score_gemma":0.0008023764,"domain_scores_codex":[0.9997745,0.00002893653,0.00002159188,0.00007647806,0.00007578802,0.00002271265],"domain_scores_gemma":[0.9997688,0.00009450799,0.0000592149,0.00002789514,0.00003246374,0.00001717614],"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":[0.0000119555,0.00000436476,0.0001144979,0.00003523663,0.000003317419,0.00001149927,0.00001004137,0.0000587367,0.9973953,0.0001186604,0.0000341774,0.002202207],"study_design_scores_gemma":[0.000005353399,0.00006786076,0.0009892336,0.000005276634,0.000007747958,0.00008316846,0.000009730331,0.001851657,0.9942076,0.00008928487,0.002675363,0.000007710638],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.7062216,0.005297368,0.2826915,0.0004231471,0.0002307762,0.0002797864,0.001497178,0.001040124,0.002318664],"genre_scores_gemma":[0.6811284,0.00413863,0.3094248,0.0003054565,0.00008716126,0.000555568,0.001064024,0.00008775872,0.003208206],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.000695199,"threshold_uncertainty_score":0.002810776,"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."}}