Induction of thermogenesis in brown and beige adipose tissues
Bibliographic record
Abstract
PURPOSE OF REVIEW: The purpose of this short review paper is to summarize recent developments in the understanding of the activation, growth and function of brown adipose tissue (BAT). RECENT FINDINGS: Transcriptional markers for increased BAT activity and differentiation of white adipocytes to 'beige' or 'brite' adipocytes include amongst others peroxisome proliferator-activated receptor γ, cytosine-enhancer-binding protein, positive regulatory domain 16 and bone morphogenetic proteins. These markers induce uncoupling protein 1 expression in brown and 'beige' or 'brite' adipocytes which allows energy from macronutrients to be expended as heat. Acute and repeated mild cold exposures of 17-19 °C in adult humans increase BAT volume and activity and this is a novel method for increasing their energy expenditure. Emerging evidence suggests that irisin and melatonin hormones may be involved in BAT activation. Additionally, brown adipocyte stem cell therapy transplantation is a means to stimulate this increased thermogenesis from brown and 'beige' or 'brite' adipocytes. SUMMARY: Markers for increased BAT activation and for white adipocyte differentiation into beige/brite adipocytes have been identified, and these lead to an uncoupling protein 1-mediated increase in metabolic rate. Mild cold exposure and brown adipocyte stem cell transplantation are two potential strategies for inducing activation and growth of BAT for the treatment of human obesity.
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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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".