Bibliographic record
Abstract
Complex multicellular organisms require a coordinated response from multiple tissues to maintain whole-body homeostasis in the face of energetic stressors such as fasting, cold, and exercise. It is also essential that energy is stored efficiently with feeding and the chronic nutrient surplus that occurs with obesity. Mammals have adapted several endocrine signals that regulate metabolism in response to changes in nutrient availability and energy demand. These include hormones altered by fasting and refeeding including insulin, glucagon, glucagon-like peptide-1, catecholamines, ghrelin, and fibroblast growth factor 21; adipokines such as leptin and adiponectin; cell stress-induced cytokines like tumor necrosis factor alpha and growth differentiating factor 15, and lastly exerkines such as interleukin-6 and irisin. Over the last 2 decades, it has become apparent that many of these endocrine factors control metabolism by regulating the activity of the AMPK (adenosine monophosphate-activated protein kinase). AMPK is a master regulator of nutrient homeostasis, phosphorylating over 100 distinct substrates that are critical for controlling autophagy, carbohydrate, fatty acid, cholesterol, and protein metabolism. In this review, we discuss how AMPK integrates endocrine signals to maintain energy balance in response to diverse homeostatic challenges. We also present some considerations with respect to experimental design which should enhance reproducibility and the fidelity of the conclusions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.003 | 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".