Murine models provide insight to the development of non-alcoholic fatty liver disease
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 0.001 |
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 it