Reference gene recommendations and <scp>PACAP</scp> receptor expression in murine sympathetic ganglia of the autonomic nervous system that innervate adipose tissues after chronic cold exposure
Bibliographic record
Abstract
Pituitary adenylate cyclase-activating polypeptide (PACAP) is an important regulator of the stress response in mammals, influencing both the hypothalamic-pituitary-adrenal (HPA) axis and the sympathetic nervous system (SNS). PACAP has been reported to influence energy homeostasis, including adaptive thermogenesis, an energy burning process in adipose tissue regulated by the SNS in response to cold stress and overfeeding. While research suggests PACAP acts centrally at the level of the hypothalamus, knowledge of PACAP's role within the sympathetic nerves innervating adipose tissues in response to metabolic stressors is limited. This work shows, for the first time, gene expression of PACAP receptors in stellate ganglia and highlights some differential expression with housing temperature. Additionally, we present our dissection protocol, analysis of tyrosine hydroxylase gene expression as a molecular biomarker for catecholamine producing tissue and recommend three stable reference genes for the normalization of quantitative real time-polymerase chain reaction (qRT-PCR) data when working with this tissue. This study adds to information about neuropeptide receptor expression in peripheral ganglia of the sympathetic nervous system innervating adipose tissue and provides insight into PACAP's role in the regulation of energy metabolism.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.012 |
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".