Changes in expression of hepatic genes involved in lipid metabolism during prehibernation period in captive adult female Japanese black bears (<i>Ursus thibetanus japonicus</i>)
Bibliographic record
Abstract
Body fat accumulation in the prehibernation period is crucial for survival and reproduction during hibernation for bears. Bear body mass increases rapidly during their autumnal hyperphagia phase, which is attributed not only to an increase in food availability, but also to physiological changes in lipid metabolism. To test this hypothesis, we investigated changes in blood biochemical values and mRNA expression levels of hepatic genes involved in lipid metabolism during the active period (June, August, October, and November) in Japanese black bears ( Ursus thibetanus japonicus Schlegel, 1857), which were fed a constant ration throughout this period. Blood biochemical analysis revealed that plasma triglyceride concentrations decreased in October and November, implying that peripheral triglyceride uptake was accelerated in autumn. The liver was sampled by needle biopsy. Real-time polymerase chain reaction (PCR) analysis revealed that mRNA expressions of enzymes involved in glycolysis (glucokinase), as well as fatty acid and triglyceride synthesis (ATP-citrate lyase, acetyl-CoA carboxykinase 1, fatty acid synthase, and diacylglycerol O-acyltransferase 2), increased in November, which suggests that hepatic lipogenesis becomes accelerated during the hyperphagia phase. These results suggest that lipid metabolism is seasonally controlled even without changes in food intake. These physiological changes seen in the prehibernation period would contribute to the rapid mass gain necessary for hibernation.
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) | 0.000 | 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) | 0.000 | 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".