Relationships among changes of serum leptin concentration, leptin mRNA expression in white adipose tissue (WAT), and WAT fat-cell size in female Japanese black bears (Ursus thibetanus japonicus)
Bibliographic record
Abstract
As a first step to study the relationship between fat accumulation and reproductive success in Japanese black bears ( Ursus thibetanus japonicus Schlegel, 1857) with the focus on leptin, we determined leptin cDNA sequences in the bears. Next, we studied the possibility of white adipose tissue (WAT) as a leptin secretion source by observing the changes of leptin mRNA expression in WAT by semiquantitative real-time reverse transcript – polymerase chain reaction, the index of WAT fat-cell size, and serum leptin concentration in pregnant bears. Then, based on our results, we discussed roles of leptin in those bears. The amino acid sequences of leptin from the bears were highly identical to that of other carnivores. The expression of leptin mRNA in WAT was detected from September to January, with a tendency to increase in late November and January; the relationship between changes in the index of WAT fat-cell size and those in serum leptin concentration was high (r = 0.55, P < 0.01), with an increase in both in mid-November. These results suggested that leptin was mainly secreted from WAT in bears and that serum leptin concentrations might reflect their nutritional condition. Moreover, leptin might serve as an indicator of their fat mass, which would affect their survival during hibernation and their reproductive success.
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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) | 0.000 | 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".