The role of pituitary adenylate cyclase-activating polypeptide in sympathetic regulation of brown adipose tissue function
Bibliographic record
Abstract
Obesity is a multi-factorial, chronic metabolic disease that forms due to the imbalance of energy metabolism. Scientists are actively working to identify potential therapeutic targets to combat obesity. Pituitary adenylate cyclase-activating polypeptide (PACAP) has been recognized as a neuropeptide involved in regulating adaptive thermogenesis, a physiological mechanism of energy expenditure. The role of PACAP in catecholamine secretion is wellstudied at the sympatho-adrenomedullary axis; however, the function of PACAP in the pripheral sympathetic nervous system (SNS) is not characterized. We developed an ex vivo model aiming to study PACAP‟s role in the peripheral ganglia in regulating catecholamine secretion and downstream adrenergic signaling. To validate the model, we used two nicotinic acetylcholine receptor (nAChR) agonists (dimethylphenylpiperazinium (DMPP), nicotine) to stimulate postganglionic nerves of the stellate ganglia and assessed molecular markers (cyclic adenosine monophosphate (cAMP) and phospho-hormone-sensitive lipase (p-HSL) (Ser563)) of norepinephrine-stimulated adrenergic signaling in interscapular brown adipose tissue (iBAT). Delivering nAChR agonists to the stellate ganglia did not increase the production of cAMP and p-HSL (Ser563) in iBAT, demonstrating that, as currently implemented, our model is not suitable to study PACAP‟s function in the peripheral ganglia. Although the model was deemed to be unsuccessful, outcomes of this study have helped us understand the challenges of working with a sympathetic ganglion, and the optimization of protocols to measure molecular markers of adrenergic stimulation established in this study will be useful for future studies examining sympathetic nerve activity in peripheral organs.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".