Regulation of adipocyte thermogenesis: mechanisms controlling obesity
Bibliographic record
Abstract
Adipocyte biology has been intensely researched in recent years due to the emergence of obesity as a serious global health concern and because of the realization that adipose tissue is more than simply a cell type that stores and releases lipids. The plasticity of adipose tissues, to rapidly adapt to altered physiological states of energy demand, is under neuronal and endocrine control. The capacity for white adipocytes to store chemical energy in lipid droplets is key for protecting other organs from the toxic effects of ectopic lipid deposition. In contrast, thermogenic (brown and beige) adipocytes combust macronutrients to generate heat. The thermogenic activity of adipocytes allows them to protect themselves and other tissues from lipid overaccumulation. Advances in brown fat biology have uncovered key molecular players involved in adipocyte determination, differentiation, and thermogenic activation. It is now, well appreciated that three distinct adipocyte types exist: white, beige, and brown. Moreover, functional differences are present within adipocyte subtypes located in anatomically distinct locations. Adding to this complexity is the recent realization from single-cell sequencing studies that adipocyte progenitors are also heterogeneous. Understanding the molecular details of how to increase the number of thermogenic fat cells and their activation may delineate some of the pathophysiological basis of obesity and obesity-related diseases. Here, we review recent advances that have extended our understanding of the central role that adipose tissue plays in energy balance and the mechanisms that control their amount and function.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".