Murine models provide insight to the development of non-alcoholic fatty liver disease
Bibliographic record
Abstract
Associated with the obesity epidemic, non-alcoholic fatty liver disease (NAFLD) has become the leading liver disease in North America. Approximately 30 % of patients with NAFLD may develop non-alcoholic steatohepatitis (NASH) that can lead to cirrhosis and hepatocellular carcinoma (HCC). Frequently animal models are used to help identify underlying factors contributing to NAFLD including insulin resistance, dysregulated lipid metabolism and mitochondrial stress. However, studying the inflammatory, progressive nature of NASH in the context of obesity has proven to be a challenge in mice. Although the development of effective treatment strategies for NAFLD and NASH is gaining momentum, the field is hindered by a lack of a concise animal model that reflects the development of liver disease during obesity and the metabolic syndrome. Therefore, selecting an animal model to study NAFLD or NASH must be done carefully to ensure the optimal application. The most widely used animal models have been reviewed highlighting their advantages and disadvantages to studying NAFLD and NASH specifically in the context of obesity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".