FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4293493372> ?p ?o ?g. }

• W4293493372 endingPage "e0268954" @default.
• W4293493372 startingPage "e0268954" @default.
• W4293493372 abstract "Inflammatory bowel disease (IBD) is a chronic immune-mediated disease of the gastrointestinal tract. While therapies exist, response can be limited within the patient population. Researchers have thus studied mouse models of colitis to further understand pathogenesis and identify new treatment targets. Flow cytometry and RNA-sequencing can phenotype immune populations with single-cell resolution but provide no spatial context. Spatial context may be particularly important in colitis mouse models, due to the simultaneous presence of colonic regions that are involved or uninvolved with disease. These regions can be identified on hematoxylin and eosin (H&E)-stained colonic tissue slides based on the presence of abnormal or normal histology. However, detection of such regions requires expert interpretation by pathologists. This can be a tedious process that may be difficult to perform consistently across experiments. To this end, we trained a deep learning model to detect 'Involved' and 'Uninvolved' regions from H&E-stained colonic tissue slides. Our model was trained on specimens from controls and three mouse models of colitis-the dextran sodium sulfate (DSS) chemical induction model, the recently established intestinal epithelium-specific, inducible Klf5ΔIND (Villin-CreERT2;Klf5fl/fl) genetic model, and one that combines both induction methods. Image patches predicted to be 'Involved' and 'Uninvolved' were extracted across mice to cluster and identify histological classes. We quantified the proportion of 'Uninvolved' patches and 'Involved' patch classes in murine swiss-rolled colons. Furthermore, we trained linear determinant analysis classifiers on these patch proportions to predict mouse model and clinical score bins in a prospectively treated cohort of mice. Such a pipeline has the potential to reveal histological links and improve synergy between various colitis mouse model studies to identify new therapeutic targets and pathophysiological mechanisms." @default.
• W4293493372 created "2022-08-29" @default.
• W4293493372 creator A5000994247 @default.
• W4293493372 creator A5023347218 @default.
• W4293493372 creator A5023363049 @default.
• W4293493372 creator A5036343196 @default.
• W4293493372 creator A5039253714 @default.
• W4293493372 creator A5046688742 @default.
• W4293493372 creator A5070647090 @default.
• W4293493372 creator A5072946826 @default.
• W4293493372 creator A5074509608 @default.
• W4293493372 creator A5088769071 @default.
• W4293493372 date "2022-08-29" @default.
• W4293493372 modified "2023-09-30" @default.
• W4293493372 title "Deep learning-based approach to the characterization and quantification of histopathology in mouse models of colitis" @default.
• W4293493372 cites W1594235143 @default.
• W4293493372 cites W1596258860 @default.
• W4293493372 cites W1597959509 @default.
• W4293493372 cites W1995162574 @default.
• W4293493372 cites W1997923130 @default.
• W4293493372 cites W2005959793 @default.
• W4293493372 cites W2010506461 @default.
• W4293493372 cites W2013264917 @default.
• W4293493372 cites W2027894434 @default.
• W4293493372 cites W2044417031 @default.
• W4293493372 cites W2052179475 @default.
• W4293493372 cites W2060373663 @default.
• W4293493372 cites W2065548709 @default.
• W4293493372 cites W2082884465 @default.
• W4293493372 cites W2084422721 @default.
• W4293493372 cites W2088014510 @default.
• W4293493372 cites W2092083061 @default.
• W4293493372 cites W2092190921 @default.
• W4293493372 cites W2098711659 @default.
• W4293493372 cites W2104488459 @default.
• W4293493372 cites W2117539524 @default.
• W4293493372 cites W2128503092 @default.
• W4293493372 cites W2136857997 @default.
• W4293493372 cites W2144898082 @default.
• W4293493372 cites W2149063442 @default.
• W4293493372 cites W2166021109 @default.
• W4293493372 cites W2344711780 @default.
• W4293493372 cites W2460600979 @default.
• W4293493372 cites W2742267660 @default.
• W4293493372 cites W2755548020 @default.
• W4293493372 cites W2897998041 @default.
• W4293493372 cites W2995942064 @default.
• W4293493372 cites W3007943565 @default.
• W4293493372 cites W3128694421 @default.
• W4293493372 cites W3156774813 @default.
• W4293493372 cites W3164182598 @default.
• W4293493372 cites W4223638209 @default.
• W4293493372 doi "https://doi.org/10.1371/journal.pone.0268954" @default.
• W4293493372 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36037173" @default.
• W4293493372 hasPublicationYear "2022" @default.
• W4293493372 type Work @default.
• W4293493372 citedByCount "2" @default.
• W4293493372 countsByYear W42934933722023 @default.
• W4293493372 crossrefType "journal-article" @default.
• W4293493372 hasAuthorship W4293493372A5000994247 @default.
• W4293493372 hasAuthorship W4293493372A5023347218 @default.
• W4293493372 hasAuthorship W4293493372A5023363049 @default.
• W4293493372 hasAuthorship W4293493372A5036343196 @default.
• W4293493372 hasAuthorship W4293493372A5039253714 @default.
• W4293493372 hasAuthorship W4293493372A5046688742 @default.
• W4293493372 hasAuthorship W4293493372A5070647090 @default.
• W4293493372 hasAuthorship W4293493372A5072946826 @default.
• W4293493372 hasAuthorship W4293493372A5074509608 @default.
• W4293493372 hasAuthorship W4293493372A5088769071 @default.
• W4293493372 hasBestOaLocation W42934933721 @default.
• W4293493372 hasConcept C125473707 @default.
• W4293493372 hasConcept C142724271 @default.
• W4293493372 hasConcept C151730666 @default.
• W4293493372 hasConcept C203014093 @default.
• W4293493372 hasConcept C204232928 @default.
• W4293493372 hasConcept C2775862500 @default.
• W4293493372 hasConcept C2778260677 @default.
• W4293493372 hasConcept C2779134260 @default.
• W4293493372 hasConcept C2779343474 @default.
• W4293493372 hasConcept C2908647359 @default.
• W4293493372 hasConcept C544855455 @default.
• W4293493372 hasConcept C71924100 @default.
• W4293493372 hasConcept C86803240 @default.
• W4293493372 hasConcept C99454951 @default.
• W4293493372 hasConceptScore W4293493372C125473707 @default.
• W4293493372 hasConceptScore W4293493372C142724271 @default.
• W4293493372 hasConceptScore W4293493372C151730666 @default.
• W4293493372 hasConceptScore W4293493372C203014093 @default.
• W4293493372 hasConceptScore W4293493372C204232928 @default.
• W4293493372 hasConceptScore W4293493372C2775862500 @default.
• W4293493372 hasConceptScore W4293493372C2778260677 @default.
• W4293493372 hasConceptScore W4293493372C2779134260 @default.
• W4293493372 hasConceptScore W4293493372C2779343474 @default.
• W4293493372 hasConceptScore W4293493372C2908647359 @default.
• W4293493372 hasConceptScore W4293493372C544855455 @default.
• W4293493372 hasConceptScore W4293493372C71924100 @default.
• W4293493372 hasConceptScore W4293493372C86803240 @default.
• W4293493372 hasConceptScore W4293493372C99454951 @default.

• next