Regulation of Autophagy Through Multiple Independent Hypoxic Signaling Pathways
Bibliographic record
Abstract
The poorly developed vasculature in solid human tumors is responsible for a profound level of intra- and inter-tumor heterogeneity in oxygen concentration. High levels of hypoxia are associated with poor patient prognosis due in part to hypoxia-induced changes in cell metabolism, angiogenesis, invasiveness and resistance to therapy. Over the past decade several distinct oxygen sensing pathways that regulate the cellular response to hypoxia have been defined. These include transcriptional and translational responses initiated by oxygen-dependent stabilization of the HIF-1 transcription factor, activation of the unfolded protein response (UPR) and inhibition of the mTOR (mammalian target of rapamycin) kinase signaling pathway. Variations in the duration and severity of hypoxic stress differentially activate these responses and lead to substantial phenotypic variation amongst otherwise identical tumor cells. Nevertheless, several studies have provided links between HIF-, UPR- and mTOR-mediated signaling and the induction of autophagy. This process facilitates survival during metabolic stress and may also be an important mechanism for the removal of potentially toxic damaged proteins and organelles. We propose that overlapping mechanisms of autophagy regulation by HIF, mTOR and the UPR function to coordinately promote hypoxia tolerance and tumor cell survival.
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 | 0.002 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".