Regulation of Multidrug Resistance by Pro-Inflammatory Cytokines
Bibliographic record
Abstract
Various mechanisms have been implicated in the development of resistance of cancer cells to chemotherapy. Multidrug resistance (MDR) is a phenomenon in which cancer cells are resistant to the cytotoxic effects of various structurally and mechanistically unrelated chemotherapeutic agents. One major mechanism by which this occurs is through the over-expression of ATP-dependent drug efflux transporters such as the P-glycoprotein (PGP) and multidrug resistance-associated protein (MRP). Regulation of MDR can occur at many levels including transcriptional, mRNA, protein and post-translational. In recent years it has been demonstrated that alterations in the expression and activity of the MDR transporters are seen in numerous tissues during an inflammatory response. An acute inflammatory response is associated with many conditions including infection, injury, hypoxia and stress and is known to result in the induction of several pro-inflammatory cytokines. Whether the function of cytokines can be harnessed in overcoming drug resistance of tumors has yet to be examined and explored. In this review, we will focus on the various studies investigating the regulation of MDR during an inflammatory response, in particular by cytokines. The mediators and pathways involved as well as the possible mechanisms of MDR regulation will be discussed. It is hoped that by understanding the clinical importance of inflammatory mediators in MDR, new doors will open and future insights will lead to the development of novel immunotherapeutics for the treatment of cancer.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".