Bibliographic record
Abstract
Recent evidence from experimental and clinical studies links the development of intratumoral hypoxia and oxidative stress with malignant progression. Cellular adaptation induced by these environmental stresses is also associated with the emergence of drug resistant populations. This adaptation is most likely a multifactorial process involving coordination of various stress-induced signaling pathways, including those regulated by hypoxia-inducible factor-1 (HIF-1) and nuclear factor kappaB (NF-kappaB) together with their downstream targets linked to resistance mechanisms. Experimental data suggest that treatment of human cancer cells with nitric oxide (NO) and NO mimetic agents can effectively restore the sensitivity of resistant populations to the cytotoxic effects of chemotherapeutics both in vitro and in vivo. Furthermore, preliminary results from Phase II clinical trials evaluating NO as an adjuvant to chemotherapy are promising. The present review highlights the significance of intratumoral hypoxia and oxidative stress in the emergence of multidrug resistance, and summarizes the latest data demonstrating the chemosensitizing ability of NO. To date, the specific mechanisms through which NO restores sensitivity to anticancer agents are not clearly understood. However, the data suggest that chemosensitization is likely to involve NO-mediated activities associated with both prevention and inhibition of cellular drug resistance mechanisms. Potential mechanisms contributing to the chemosensitizing activity of NO include vascular changes that promote increased blood delivery and tumor oxygenation, antioxidant effects and down-regulation of the glutathione detoxification/redox buffering system, inhibition of key transcription factors such as HIF-1 and NF-kappaB, as well as inhibition of drug efflux transporters and DNA repair enzymes.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".