The impact of estrogen-related receptor alpha deficiency on intestinal and liver inflammation and cancer
Bibliographic record
Abstract
Both experimentally and epidemiologically, it has been shown that long-standing inflammation secondary to chronic infection predisposes an individual to cancer.Inflammation seems to lead to the development of cancer because of the activities of immune cells, including the production of proteins (cytokines and chemokines) that alter the behaviour of target cells, stimulation of blood vessel growth (angiogenesis) and tissue remodelling.Immune cells also produce oxygen radicals that can cause mutations in DNA.Recently, the presence of Estrogen-Related Receptor α (ERRα) in bone-derived macrophages was shown to be essential for induction of mitochondrial reactive oxygen species (ROS) production and efficient clearance of bacteria in response to interferon-γ, thus identifying ERRα as a key player in cytokine-induced host defense.Hepatocellular carcinoma (HCC) and colon cancer secondary to chronic hepatitis viral infection and inflammatory bowel diseases (IBD), respectively are among the best examples of inflammation-and infection-associated cancers.In this thesis, results obtained from investigating the impact of ERRα deficiency on intestinal and liver inflammation in mice are presented.Surprisingly, the primary results suggest that the role of ERRα is more important in hepatocytes to prevent chronic inflammation and tumorigenesis than in intestinal epithelium.It is hoped that the results of this project may lead to the use of synthetic ERRα agonists for treatment of liver cancer types associated with chronic inflammatory conditions.Increased HCC in ERRα-/mice seems to be associated with increased hepatocyte proliferation, reduced apoptosis and deregulation of cell cycle progression in nontumor tissue ...........................
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".