ER stress and/or ER-phagy in drug resistance? Three coincidences are proof
Bibliographic record
Abstract
Cancer is influenced by the tumor microenvironment (TME), which includes factors such as pH, hypoxia, immune cells, and blood vessels. These factors affect cancer cell growth and behavior. The tumor microenvironment triggers adaptive responses such as endoplasmic reticulum (ER) stress, unfolded protein response (UPR), and autophagy, posing a challenge to cancer treatment. The UPR aims to restore ER homeostasis by involving key regulators inositol-requiring enzyme-1(IRE1), PKR-like ER kinase (PERK), and activating transcription factor 6 (ATF6). Additionally, ER-phagy, a selective form of autophagy, eliminates ER components under stress conditions. Understanding the interplay between hypoxia, ER stress, UPR, and autophagy in the tumor microenvironment is crucial for developing effective cancer therapies to overcome drug resistance. Targeting the components of the UPR and modulating ER-phagy could potentially improve the efficacy of existing cancer therapies. Future research should define the conditions under which ER stress responses and ER-phagy act as pro-survival versus pro-death mechanisms and develop precise methods to quantify ER-phagic flux in tumor cells.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| 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".