Bibliographic record
Abstract
The proper production, degradation, folding and activity of proteins, proteostasis, is essential for any cellular function. From single cell organisms to humans, selective pressures have led to the evolution of adaptive programs that ensure proteins are properly produced and disposed of when necessary. Environmental factors such as temperature, nutrient availability, pathogens as well as predators have greatly influenced the development of mechanisms such as the unfolded protein response, endoplasmic reticulum-associated protein degradation and autophagy, working together in concert to secure cellular proteostasis. In our modern society, the metabolic systems of the human body face the distinct challenge of changed diets, chronic overnutrition and sedentary lifestyles. Obesity and excess white adipose tissue accumulation are linked to a cluster of metabolic diseases and disturbed proteostasis is a common feature. Conversely, processes that promote energy expenditure such as exercise, shivering as well as non-shivering thermogenesis by brown adipose tissue (BAT) and beige adipocytes counteract metabolic dysfunction. Here we review the basic concepts of proteostasis in obesity-linked metabolic diseases and focus on adipocytes, which are critical regulators of mammalian energy metabolism.
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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 | 0.002 |
| 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".