Effect of endurance exercise training on liver gene expression in male and female mice
Bibliographic record
Abstract
Chronic endurance exercise is a therapeutic strategy in the treatment of many chronic diseases in humans, including the prevention and treatment of metabolic diseases such as diabetes mellitus. Metabolic, cardiorespiratory, and endocrine pathways targeted by chronic endurance exercise have been identified. In the liver, however, the cellular and molecular pathways that are modified by exercise and have preventive or therapeutic relevance to metabolic disease need to be elucidated. The mouse model used in the current study allows for the quantification of a human-relevant exercise “dosage”. In this study we show hepatic gene expression differences between sedentary female and sedentary male mice and that chronic exercise modifies the transcription of hepatic genes related to metabolic disease and steatosis in both male and female mice. Chronic exercise induces molecular pathways involved in glucose tolerance, glycolysis, and gluconeogenesis while producing a decrease in pathways related to insulin resistance, steatosis, fibrosis, and inflammation. Given these findings, this mouse exercise model has potential to dissect the cellular and molecular hepatic changes following chronic exercise with application to understanding the role that chronic exercise plays in preventing human diseases. Novelty: Exercise modifies the hepatic gene expression and hepatic pathways related to metabolic disease in male and female mice. Sex differences were seen in hepatic gene expression between sedentary and exercised mice. The mouse exercise model used in this study allows for application and evaluation of exercise effects in human disease.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".