Clinical and Translational Studies in Fibrosis
Bibliographic record
Abstract
Background: Inflammatory bowel disease (IBD) is found to be associated with several kinds of liver disease.The purpose of this study is to investigate the role of combination with dextran sodium sulfate (DSS) in hepatitis and fibrosis in mice treated by with CCl4.Methods: Male C57BL/6 mice were grouped as follows: Control group (n=10), DSS group (n=10), Olive oil group (n=10), CCl4 group (n=10) and CCl4+DSS group (n=10).Severity of colitis was evaluated by disease activity index (DAI), colon length, colon pathology score, myeloperoxidase (MPO) and histopathology.Haematoxylin and eosin (H&E) staining, Sirius red staining and Masson's trichrome (MT) staining were used to detect liver histopathological changes.Pro-inflammatory cytokines in both colon and liver tissues including TNF-α, IFN-γ and IL-17A were detected by immunohistochemical staining, western blot and real-time Q-PCR, respectively.The protein and mRNA expressions of TGF-β1, α-SMA, collagen I, collagen III, MMP-2 and TIMP-2 in liver tissues were observed by immunohistochemical staining, western blot and real-time Q-PCR, respectively.Results: DSS treatment led to increased BW loss, higher DAI score, shortened colon length, elevated MPO activity, and worsened histologic inflammation in colon.Moreover, TNF-α, IFN-γ and IL-17A expressions in both colon and liver tissues were all enhanced in DSS group.Hepatitis was also found in DSS group as well as CCl4 group and CCl4+DSS group by histological analysis.However, comparing with CCl4 group, hepatitis in CCl4+DSS were more severe, reflected by histology and pro-inflammation cytokines expressions.Increased hepatic fibrosis was observed by Sirius red staining and MT staining, as well as higher levels TGF-β1, α-SMA, collagen I, collagen type III, TIMP-2 and lower level of MMP-2 in liver tissue.Conclusions: The DSS-induced mouse colitis may promote hepatic inflammation and fibrosis in mice treated by CCl4.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.029 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".