Distribution of preproadrenomedullin mRNA in the rat central nervous system and its modulation by physiological stressors
Bibliographic record
Abstract
Adrenomedullin (ADM), encoded by the preproadrenomedullin (ppADM) gene, exerts multiple effects in a wide variety of peripheral and central tissues. Although ADM-like immunoreactivity has been shown to be widely distributed throughout the rat central nervous system (CNS), the detailed distribution of ppADM gene expression in the CNS and its modulation by physiological stimuli remain unknown. In our study, in situ hybridization was used to localize ppADM mRNA in the rat brain and to quantify its levels after exposure to different stressors including lipopolysaccharide (LPS; 100 microg/kg, iv), restraint stress (2 cycles of 1 hour restraint/1 hour rest), and 24 hours of dehydration. In addition, Fos immunoreactivity was used to identify the activation of neurons in response to LPS. Our results show that ppADM mRNA is widely distributed throughout the rat CNS, with especially high levels in autonomic centers including the hypothalamic paraventricular nucleus (PVN), hypothalamic supraoptic nucleus (SON), locus coeruleus, ventrolateral medulla, and intermediolateral cell column of the spinal cord. Furthermore, LPS inhibits ppADM gene expression in the parvocellular PVN (pPVN), magnocellular PVN (mPVN), SON, dorsal motor nucleus of the vagus, and area postrema among examined regions; restraint stress reduces ppADM mRNA levels in the pPVN, mPVN, SON, nucleus of the solitary tract, dorsal motor nucleus of the vagus, area postrema, and subfornical organ; 24 hours of water deprivation decreases ppADM gene expression only in the mPVN and SON. Taken together, our results suggest that ADM is involved in the regulation of the hypothalamo-neurohypophysial system, the hypothalamo-pituitary-adrenal axis, and central autonomic functions.
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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".