{"id":"W3201740150","doi":"10.1038/s41598-021-98973-y","title":"New chemical biopsy tool for spatially resolved profiling of human brain tissue in vivo","year":2021,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"White matter; Biopsy; In vivo; Solid-phase microextraction; Human brain; Sampling (signal processing); Profiling (computer programming); Metabolomics; Pathology; Computer science; Brain tissue; Grey matter; Computational biology; Brain biopsy; Medicine; Bioinformatics; Biomedical engineering; Neuroscience; Biology; Chemistry; Radiology; Chromatography; Magnetic resonance imaging; Gas chromatography–mass spectrometry; Mass spectrometry; 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":[0.0005013859,0.0005694441,0.0003662024,0.0007659204,0.0002215237,0.0003292114,0.0003766968,0.000964198,0.001729302],"category_scores_gemma":[0.0005538645,0.0003444523,0.0002257375,0.0004091509,0.0003240881,0.0006968463,0.0005640758,0.0005719166,0.0006800288],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001213405,"about_ca_system_score_gemma":0.0002665368,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003476715,"about_ca_topic_score_gemma":0.001187974,"domain_scores_codex":[0.99977,0.00005902801,0.00000904097,0.00006683278,0.00007715829,0.00001798067],"domain_scores_gemma":[0.9997984,0.00007520805,0.00003257175,0.00002190885,0.00005320254,0.00001889426],"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":[0.00008983848,0.00001727188,0.0004092581,0.0001232568,0.00001485652,0.0001224138,0.0000323762,0.0001339922,0.9841249,0.0002005982,0.0002270646,0.01450403],"study_design_scores_gemma":[0.00004124557,0.0008365183,0.008998433,0.00006771021,0.0001151892,0.004057413,0.0001621122,0.01294202,0.9564421,0.001014291,0.01525468,0.00006839868],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2112456,0.0150553,0.7663176,0.001028705,0.0002881442,0.0003537025,0.001023798,0.001406556,0.003280621],"genre_scores_gemma":[0.3470272,0.007745354,0.6399417,0.0006987182,0.0001282231,0.0004780285,0.0005013543,0.0001335849,0.003345774],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001729302,"threshold_uncertainty_score":0.005785108,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01398831660658414,"score_gpt":0.2891777053965164,"score_spread":0.2751893887899323,"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."}}