Hypoxemia-induced leptin secretion: a mechanism for the control of food intake in diseased fish
Bibliographic record
Abstract
Leptin is a potent anorexigen, but little is known about the physiological conditions under which this cytokine regulates food intake in fish. In this study, we characterized the relationships between food intake, O2-carrying capacity, liver leptin-A1 (lep-a1) gene expression, and plasma leptin-A1 in rainbow trout infected with a pathogenic hemoflagellate, Cryptobia salmositica. As lep gene expression is hypoxia-sensitive and Cryptobia-infected fish are anemic, we hypothesized that Cryptobia-induced anorexia is mediated by leptin. A 14-week time course experiment revealed that Cryptobia-infected fish experience a transient 75% reduction in food intake, a sharp initial drop in hematocrit and hemoglobin levels followed by a partial recovery, a transient 17-fold increase in lep-a1 gene expression, and a sustained increase in plasma leptin-A1 levels. In the hypothalamus, peak anorexia was associated with decreases in mRNA levels of neuropeptide Y (npy) and cocaine- and amphetamine-regulated transcript (cart), and increases in agouti-related protein (agrp) and pro-opiomelanocortin A2 (pomc). In contrast, in non-infected fish pair-fed to infected animals, lep-a1 gene expression and plasma levels did not differ from those of non-infected satiated fish. Pair-fed fish were also characterized by increases in hypothalamic npy and agrp, no changes in pomc-a2, and a reduction in cart mRNA expression. Finally, peak infection was characterized by a significant positive correlation between O2-carrying capacity and food intake. These findings show that hypoxemia, and not feed restriction, stimulates leptin-A1 secretion in Cryptobia-infected rainbow trout and suggest that leptin contributes to anorexia by inhibiting hypothalamic npy and stimulating pomc-a2.
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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) | 0.001 | 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".