Metabolism as a central regulator of β‐cell chromatin state
Bibliographic record
Abstract
Pancreatic β-cells are critical mediators of glucose homeostasis in the body, and proper cellular nutrient metabolism is critical to β-cell function. Several interacting signaling networks that uniquely control β-cell metabolism produce essential substrates and co-factors for catalytic reactions, including reactions that modify chromatin. Chromatin modifications, in turn, regulate gene expression. The reactions that modify chromatin are therefore well-positioned to adjust gene expression programs according to nutrient availability. It follows that dysregulation of nutrient metabolism in β-cells may impact chromatin state and gene expression through altering the availability of these substrates and co-factors. Metabolic disorders such as type 2 diabetes (T2D) can significantly alter metabolite levels in cells. This suggests that a driver of β-cell dysfunction during T2D may be the altered availability of substrates or co-factors necessary to maintain β-cell chromatin state. Induced changes in the β-cell chromatin modifications may then lead to dysregulation of gene expression, in turn contributing to the downward cascade of events that leads to the loss of functional β-cell mass, and loss of glucose homeostasis, that occurs in T2D.
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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 | 0.001 |
| 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".