Background And Aims: Although several endoscopic and histopathologic indices are available for evaluating the severity of inflammation in mouse models of colitis, the reliability of these scoring instruments is unknown. Our aim was to evaluate the reliability of the individual items in the existing indices and develop new scoring systems by selection of the most reliable index items.
Methods: Two observers scored the histological slides [n = 224] and endoscopy videos [n = 201] from treated and untreated Interleukin[IL]-10 knock-out and T-cell transferred SCID mice. Intra-rater and inter-rater reliability for endoscopy and histology scores, and each individual item, were measured using intraclass correlation coefficients [ICCs]. The Mouse Colitis Histology Index [MCHI] and Mouse Colitis Endoscopy Index [MCEI] were developed using the most reliable items. Both were correlated to the colon density and to each other and were evaluated for their ability to detect changes in pathobiology.
Results: The intraclass correlation coefficients (ICCs) for inter-rater agreement (95% CIs) for the total histology and endoscopy scores were 0.90 [0.87-0.92] and 0.80 [0.76-0.84], respectively. The MCHI and MCEI were highly correlated with colon density, with a Spearman Rho = 0.81[0.75-0.85] and 0.73 [0.66-0.79], respectively, and with each other, Spearman Rho = 0.71 [0.63-0.77]. The MCHI and MCEI were able to distinguish between the experimental groups within the models, with pairwise differences between the treated and untreated groups being statistically significant [p < 0.001].
Conclusions: These histological and endoscopic indices are valid and reliable measures of intestinal inflammation in mice, and they are responsive to treatment effects in pre-clinical studies.
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http://dx.doi.org/10.1093/ecco-jcc/jjy035 | DOI Listing |
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The Third Ward of Orthopaedic Department, General Hospital of Ningxia Medical University, 804 Shengli South Street, Hui Autonomous Region, Yinchuan, Ningxia, 750004, People's Republic of China.
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Department of General Surgery, The Second Affiliated Hospital of the Air Force Medical University, Xi'an, 710038, China.
A common digestive system cancer with a dismal prognosis and a high death rate globally is breast cancer (BRCA). BRCA recurrence, metastasis, and medication resistance are all significantly impacted by cancer stem cells (CSCs). However, the relationship between CSCs and the tumor microenvironment in BRCA individuals remains unknown, and this information is critically needed.
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January 2025
University of Ghana, P.O. Box 134, Legon-Accra, Ghana.
Sentiment analysis has become a difficult and important task in the current world. Because of several features of data, including abbreviations, length of tweet, and spelling error, there should be some other non-conventional methods to achieve the accurate results and overcome the current issue. In other words, because of those issues, conventional approaches cannot perform well and accomplish results with high efficiency.
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Department of Respiratory and Critical Care Medicine, Henan Provincial People's Hospital, Zhengzhou University People's Hospital, Zhengzhou, China.
China has implemented the "tiered medical services" policy since 2015, while there is a paucity of data evaluating the the current status of chronic obstructive pulmonary disease (COPD) management under the system. Characteristics and treatments from 11,905 COPD patients in 88 hospitals across different tiers in China were included and analyzed. We assessed the statistical significance of differences by one way analysis of variance (ANOVA) for continuous variables and with the chi-squared test for categorical variables.
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January 2025
Department of Mathematics, Dambi Dollo University, Dambi Dollo, Oromia, Ethiopia.
A novel method for solving the multiple-attribute decision-making problem is proposed using the complex Diophantine interval-valued Pythagorean normal set (CDIVPNS). This study aims to discuss aggregating operations and how they are interpreted. We discuss the concept of CDIVPN weighted averaging (CDIVPNWA), CDIVPN weighted geometric (CDIVPNWG), generalized CDIVPN weighted averaging (CGDIVPNWA) and generalized CGDIVPN weighted geometric (CGDIVPNWG).
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