Ghrelin Signaling Within the Paraventricular Nucleus of the Hypothalamus Influences Sympathetic Activity But Not Brown Adipose Tissue Activity During the Stress Response
Bibliographic record
Abstract
In mammals, ghrelin is secreted during chronic stress to promote the utilization of carbohydrates while decreasing the use of fat as a source for energy.Data suggest that ghrelin secretion during stress may also regulate energy expenditure in the form of heat derived from the activation of brown adipose tissue (BAT).Activation of BAT involves the sympathetic stimulation of non-shivering thermogenesis by the action of uncoupling protein-1 (UCP-1).Furthermore, the paraventricular nucleus of the hypothalamus (PVN) plays a role in energy expenditure by reducing sympathetic outflow on to BAT, thereby decreasing UCP-1 expression, and ultimately decreasing heat production.To do this, mice were implanted with cannulae attached to osmotic minipumps delivering a ghrelin receptor antagonist [D-Lys-3]-GHRP6 (20nmol/day/mouse) or vehicle by the PVN.Half of the mice from each group were subjected to chronic social defeat stress for 19-21 days.Results indicated that stressed animals decreased UCP-1 mRNA expression within BAT, although there were no drug effects.Stressed animals given the antagonist showed increased plasma epinephrine (EP) and norepinephrine (NE) compared to mice in the other groups, but appeared to show less utilization of these in BAT.We therefore suggest that stress alters sympathetic tone to modulate the expression of UCP-1 in BAT and that these effects are not mediated by ghrelin acting on receptors in the PVN.make me feel confident in my work and he always believed in my intelligence, even when I never did.I could not have asked for a better thesis supervisor and I am eternally grateful for that.I would also like to thank the rest of my committee, Iain McKinnell, Mike Hildebrand, Shawn Hayley, and John Stead for allowing me to create the best version of my thesis.I would like to especially thank Zack Patterson for introducing me into graduate work.I worked with him during my undergraduate thesis and he suggested I join the Abizaid community.He was my mentor and I always thought of myself as a hard worker until I met Zack.He's moving away from the Abizaid lab but I'm just grateful I met him for that brief time.I would also like to
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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) | 0.003 | 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".