{"id":"W4415981338","doi":"10.1101/2025.11.05.686836","title":"Ambient mass spectrometry imaging enables spatial metabolomics of optimal cutting temperature compound (OCT)-embedded tumors","year":2025,"lang":"","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Mass Spectrometry Techniques and Applications","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria; Vancouver Island University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Michael Smith Health Research BC; Terry Fox Research Institute; Vancouver Island University; Lotte and John Hecht Memorial Foundation","keywords":"Metabolomics; Metabolite; Mass spectrometry imaging; Mass spectrometry; Tumor microenvironment; Metabolism; Metabolome","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":[0.0002147304,0.0003868861,0.0001351393,0.0004163043,0.0001358068,0.0003444072,0.0001744239,0.0003424614,0.0008187953],"category_scores_gemma":[0.000175201,0.0001734482,0.0001889104,0.0001830103,0.0002092828,0.0002818986,0.0002823173,0.0004065624,0.0002772723],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002509201,"about_ca_system_score_gemma":0.0001848045,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005528606,"about_ca_topic_score_gemma":0.001124943,"domain_scores_codex":[0.9998983,0.00001149496,0.000006670505,0.00003562052,0.00002986285,0.00001792284],"domain_scores_gemma":[0.999873,0.00002662967,0.00003994633,0.00001215417,0.00003017076,0.00001807054],"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":[0.00002230453,0.000003242971,0.0001833177,0.00001018249,0.000002277711,0.00002363513,0.000006306859,0.00005225253,0.99883,0.00004055514,0.00002158616,0.0008043807],"study_design_scores_gemma":[0.000003156585,0.00006734083,0.003145471,0.000005398068,0.000008146348,0.0001867766,0.00002301787,0.002745853,0.9926018,0.000081465,0.001124584,0.000006982582],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9193076,0.001140499,0.07392273,0.0001655494,0.00003813111,0.0001031413,0.001261685,0.0006654521,0.00339521],"genre_scores_gemma":[0.8888383,0.00119889,0.1062186,0.0001948555,0.00002314294,0.0001208063,0.0009284606,0.0002215991,0.002255398],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008187953,"threshold_uncertainty_score":0.002739131,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007046721372773344,"score_gpt":0.2265152221678875,"score_spread":0.2194685007951142,"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."}}