Oxygen Sensing by H+: Implications for HIF and Hypoxic Cell Memory
Bibliographic record
Abstract
Hypoxia and acidosis are common features of several physiological and pathological situations, including cancer and stroke. The HIF (hypoxia-inducible factor) transcription factor plays a seminal role in orchestrating cellular responses to alterations in oxygen availability. HIF is degraded in normal oxygen tension by the VHL (von Hippel-Lindau) tumor suppressor protein but stabilized by hypoxia to activate an array of genes implicated in oxygen homeostasis including vascular endothelial growth factor. Cells respond to a comparatively mild decline in oxygen tension by converting to an anaerobic state of respiration and secreting lactic acid. We recently reported that a decrease in environmental pH triggers sequestration of VHL into the nucleolus neutralizing its ability to degrade HIF. This implies that cells have evolved a parallel mechanism of HIF activation that responds to changes in oxygen levels by sensing extracellular [H+]. Here we discuss the implications of this new VHL regulatory mechanism on oxygen homeostasis and hypoxic cell memory.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".