Hypoxia-Inducible Factors and the Regulation of Lipid Metabolism
Bibliographic record
Abstract
Oxygen deprivation or hypoxia characterizes a number of serious pathological conditions and elicits a number of adaptive changes that are mainly mediated at the transcriptional level by the family of hypoxia-inducible factors (HIFs). The HIF target gene repertoire includes genes responsible for the regulation of metabolism, oxygen delivery and cell survival. Although the involvement of HIFs in the regulation of carbohydrate metabolism and the switch to anaerobic glycolysis under hypoxia is well established, their role in the control of lipid anabolism and catabolism remains still relatively obscure. Recent evidence indicates that many aspects of lipid metabolism are modified during hypoxia or in tumor cells in a HIF-dependent manner, contributing significantly to the pathogenesis and/or progression of cancer and metabolic disorders. However, direct transcriptional regulation by HIFs has been only demonstrated in relatively few cases, leaving open the exact and isoform-specific mechanisms that underlie HIF-dependency. This review summarizes the evidence for both direct and indirect roles of HIFs in the regulation of genes involved in lipid metabolism as well as the involvement of HIFs in various diseases as demonstrated by studies with transgenic animal models.
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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) | 0.004 | 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".