{"id":"W3177586396","doi":"10.21203/rs.3.rs-668958/v1","title":"SPME Probes: A Novel Chemical Biopsy Tool for Spatially Resolved Profiling of Human Brain Tissue in vivo","year":2021,"lang":"en","type":"preprint","venue":"Research Square","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"White matter; Solid-phase microextraction; Biopsy; In vivo; Human brain; Sampling (signal processing); Brain tissue; Pathology; Profiling (computer programming); Metabolomics; Grey matter; Computational biology; Computer science; Chemistry; Biomedical engineering; Neuroscience; Medicine; Biology; Chromatography; Radiology; Magnetic resonance imaging; Mass spectrometry; Gas chromatography–mass spectrometry; Computer vision; Biotechnology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0005862648,0.0007076882,0.0003872518,0.0006516041,0.0002131817,0.0003804129,0.0004914201,0.001277864,0.002526197],"category_scores_gemma":[0.0007140196,0.0003810599,0.0002309952,0.0004310037,0.0004971417,0.0006978223,0.0006577172,0.0006001899,0.00114399],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001488862,"about_ca_system_score_gemma":0.0003042557,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003653117,"about_ca_topic_score_gemma":0.0008511604,"domain_scores_codex":[0.9997371,0.00006488584,0.000009641733,0.00007378814,0.00009132289,0.00002333114],"domain_scores_gemma":[0.9997628,0.0001071307,0.0000334447,0.00003028158,0.00004461767,0.00002168991],"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":[0.00009016645,0.00001188535,0.000144899,0.00008430656,0.00001240543,0.0001002054,0.00002316136,0.0001764564,0.982111,0.0002215071,0.0003923801,0.01663159],"study_design_scores_gemma":[0.00003074526,0.0003844839,0.002715399,0.00002308008,0.00003845787,0.001485803,0.00005712407,0.01090669,0.970876,0.0006447244,0.01279678,0.00004061999],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1448866,0.006066313,0.8402572,0.001002517,0.0003035353,0.0003917736,0.001089239,0.002736168,0.003266688],"genre_scores_gemma":[0.3142147,0.003658296,0.6746506,0.0004599674,0.0001390008,0.000556807,0.0006169178,0.0002753413,0.0054283],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002526197,"threshold_uncertainty_score":0.008450985,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05132587606609252,"score_gpt":0.3941149888315056,"score_spread":0.3427891127654131,"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."}}