Adipose‐specific ATGL ablation reduces burn injury‐induced metabolic derangements in mice
Bibliographic record
Abstract
Hypermetabolism following severe burn injuries is associated with adipocyte dysfunction, elevated beige adipocyte formation, and increased energy expenditure. The resulting catabolism of adipose leads to detrimental sequelae such as fatty liver, increased risk of infections, sepsis, and even death. While the phenomenon of pathological white adipose tissue (WAT) browning is well-documented in cachexia and burn models, the molecular mechanisms are essentially unknown. Here, we report that adipose triglyceride lipase (ATGL) plays a central role in burn-induced WAT dysfunction and systemic outcomes. Targeting adipose-specific ATGL in a murine (AKO) model resulted in diminished browning, decreased circulating fatty acids, and mitigation of burn-induced hepatomegaly. To assess the clinical applicability of targeting ATGL, we demonstrate that the selective ATGL inhibitor atglistatin mimics the AKO results, suggesting a path forward for improving patient outcomes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".