{"id":"W4387581041","doi":"10.1038/s41467-023-42055-2","title":"Shadow imaging for panoptical visualization of brain tissue in vivo","year":2023,"lang":"en","type":"article","venue":"Nature Communications","topic":"Optical Imaging and Spectroscopy Techniques","field":"Medicine","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hotchkiss Brain Institute; University of Calgary","funders":"Fédération pour la Recherche sur le Cerveau; National Research Foundation of Korea; Agence Nationale de la Recherche; Alberta Innovates; National Research Foundation","keywords":"Microscopy; Two-photon excitation microscopy; Confocal microscopy; Magnetic resonance imaging; Fluorescence microscope; In vivo; Fluorescence-lifetime imaging microscopy; Brain tissue; Preclinical imaging; Neuroscience; Pathology; Biology; Biomedical engineering; Fluorescence; Cell biology; Medicine; Physics; Optics","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":[0.0002246322,0.0004211925,0.0001876272,0.000571545,0.0002328519,0.0003621439,0.0002981272,0.0003606486,0.002672963],"category_scores_gemma":[0.0002884565,0.0002851042,0.0001596223,0.0002752033,0.0004039979,0.0004942048,0.0006009114,0.0005338236,0.0004525427],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000330244,"about_ca_system_score_gemma":0.0004516868,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000624999,"about_ca_topic_score_gemma":0.001275547,"domain_scores_codex":[0.9998907,0.00002025752,0.000004904049,0.00002621238,0.00004214651,0.00001576138],"domain_scores_gemma":[0.9998261,0.00006841525,0.00003205226,0.00002909343,0.00002480189,0.0000195876],"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":[0.00006272714,0.000009501609,0.0001817984,0.0001021521,0.000006264168,0.00009518339,0.00004884775,0.0006100167,0.9842651,0.00187779,0.0003032863,0.01243723],"study_design_scores_gemma":[0.00002246997,0.0001869035,0.003396577,0.00003578948,0.00002321607,0.001406208,0.00005949592,0.02856249,0.9491135,0.002423411,0.01474369,0.00002623025],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2569336,0.006613695,0.7224091,0.0004083041,0.0001561091,0.0001411484,0.0005007989,0.001734208,0.01110312],"genre_scores_gemma":[0.6410329,0.005416214,0.3472967,0.0003294181,0.00008956336,0.0002307607,0.0003941957,0.0002599572,0.004950318],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002672963,"threshold_uncertainty_score":0.008941948,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02429581825414861,"score_gpt":0.4323787785564024,"score_spread":0.4080829603022538,"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."}}