Taming the Flames: Targeting White Adipose Tissue Browning in Hypermetabolic Conditions
Bibliographic record
Abstract
In this era of increased obesity and diabetes prevalence, the browning of white adipose tissue (WAT) has emerged as a promising therapeutic target to induce weight loss and improve insulin sensitivity in this population. The browning process entails a shift in the WAT from primarily storing excess energy to the dissipation of energy as heat. However, this idealistic view of WAT browning being the savior of the metabolic syndrome has been criticized by studies in burn and cancer patients that have shown browning to be detrimental rather than beneficial. In fact, in the context of hypermetabolic states, the browning of WAT has presented with substantial clinical adverse outcomes related to cachexia, hepatic steatosis, and muscle catabolism. Therefore, the previous thought construct of understanding browning as an all-beneficial physiologic event has now been met with skepticism. In this review, we focus on current knowledge of browning of WAT and its adverse metabolic alterations during hypermetabolic states. We also discuss the regulators and signaling pathways involved in the browning process and their potential for being targeted by new or existing drugs to inhibit or alleviate browning, potentially leading to decreased hypermetabolism and improved clinical outcomes. Lastly, the imminent clinical applications of pharmacological agents are explored in the perspective of attenuating WAT browning and its associated adverse side effects reported in burn patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".