Prolactin Regulates Seasonal Changes in Renal Rheostasis in Djungarian Hamsters
Bibliographic record
Abstract
Seasonal changes in a photoperiod regulate multiple physiological systems in vertebrates, including metabolism, reproduction, and immune function. Kidney mass and renal physiology are known to vary annually, but the endocrine and molecular mechanisms underlying these changes are poorly defined. Prolactin (PRL), a photosensitive hormone is implicated in seasonal energy rheostasis, yet its role in programmed regulation of renal physiology is unknown. Using Djungarian hamsters (Phodopus sungorus), we investigated how photoperiod and PRL regulate seasonal changes in kidney mass, morphology, and transcriptome. Ingestive behavior, kidney histology, and transcriptomic profiles were assessed. We found that long photoperiods and PRL treatment induced renal hypertrophy and convoluted tubule expansion, whereas exposure to short photoperiods resulted in a reduction in all measurements. Transcriptomic analysis revealed photoperiod- and PRL-responsive gene modules related to mitochondrial metabolism, solute transport, and epithelial remodeling. Among these, Cdh2, encoding N-cadherin, was downregulated by long photoperiods and PRL, and negatively correlated with convoluted tubule diameter, suggesting a role in epithelial adhesion during tubular expansion. These findings place prolactin as a key hormonal effector for programmed seasonal kidney function and identify Cdh2 as a target to drive renal physiology.
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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".