Interscapular brown adipose tissue denervation does not promote the oxidative activity of inguinal white adipose tissue in male mice
Bibliographic record
Abstract
Brown adipose tissue (BAT) thermogenesis is a key controller of energy metabolism. In response to cold or other adrenergic stimuli, brown adipocytes increase their substrate uptake and oxidative activity while uncoupling ATP synthesis from the mitochondrial respiratory chain activity. Brown adipocytes are found in classic depots such as in the interscapular BAT (iBAT). They can also develop in white adipose tissue (WAT), such as in the inguinal WAT (iWAT), where their presence has been associated with metabolic improvements. We previously reported that the induction of oxidative metabolism in iWAT is low compared with that of iBAT, even after sustained adrenergic stimulation. One explanation to this apparent lack of thermogenic ability of iWAT is the presence of an active iBAT, which may prevent the full activation of iWAT. In this study, we evaluated whether iBAT denervation-induced browning of white fat enhanced the thermogenic activity of iWAT following cold acclimation, under beta-3 adrenergic stimulation (CL 316,243). Following a bilateral denervation of iBAT, we assessed energy balance, evaluated the oxidative activity of iBAT and iWAT using 11C-acetate, and quantified the dynamic glucose uptake of those tissues using 2-deoxy-2-[18F]- fluoro-d-glucose. Our results indicate that despite portraying marked browning and mildly enhanced glucose uptake, iWAT of cold-adapted mice does not exhibit significant oxidative activity following beta-3 adrenergic stimulation in the absence of a functional iBAT. The present results suggest that iWAT is not readily recruitable as a thermogenic organ even when functional iBAT is lacking.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".