Mitochondrial uncoupling as a target in the treatment of obesity
Bibliographic record
Abstract
PURPOSE OF REVIEW: Obesity is associated with many health problems and its prevalence is rapidly increasing worldwide. Very few pharmaceutical compounds are available for obesity treatment. Strategies for the development of compounds can be targeted to the outcomes of reduced dietary energy intake and/or increased energy expenditure/thermogenesis. In this review, we focus on recent discoveries that advance our understanding of mitochondrial uncoupling as a target for the treatment of obesity. There are various mechanisms whereby uncoupling can occur and for the purpose of this review, we elaborate upon the uncoupling that can occur (1) through the original uncoupling protein, UCP1, in brown adipocytes, or in 'converted' white adipose tissue, and (2) in skeletal muscle. RECENT FINDINGS: Studies have identified a number of novel receptors and regulatory proteins involved in the emergence of brown adipocytes in white adipose tissue. Molecular and pharmacologic approaches in knockout and transgenic mice have demonstrated their relevance to obesity treatment. Recent research into uncoupling mechanisms in skeletal muscle indicates that uncoupling can occur through basal and inducible processes. SUMMARY: Uncoupling is a naturally occurring phenomenon whose underlying mechanisms require substantial further study for the development of antiobesity therapies.
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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 | 0.002 | 0.002 |
| 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 | 0.003 |
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".