Pro-Inflammatory Signalling PRRopels Cisplatin-Induced Toxicity
Bibliographic record
Abstract
Cisplatin is a platinum-based chemotherapeutic that has long since been effective against a variety of solid-cancers, substantially improving the five-year survival rates for cancer patients. Its use has also historically been limited by its adverse drug reactions, or cisplatin-induced toxicities (CITs). Of these reactions, cisplatin-induced nephrotoxicity (CIN), cisplatin-induced peripheral neuropathy (CIPN), and cisplatin-induced ototoxicity (CIO) are the three most common of several CITs recognised thus far. While the anti-cancer activity of cisplatin is well understood, the mechanisms driving its toxicities have only begun to be defined. Most of the literature pertains to damage caused by oxidative stress that occurs downstream of cisplatin treatment, but recent evidence suggests that the instigator of CIT development is inflammation. Cisplatin has been shown to induce pro-inflammatory signalling in CIN, CIPN, and CIO, all of which are associated with persisting markers of inflammation, particularly from the innate immune system. This review covered the hallmarks of inflammation common and distinct between different CITs, the role of innate immune components in development of CITs, as well as current treatments targeting pro-inflammatory signalling pathways to conserve the use of cisplatin in chemotherapy and improve long-term health outcomes of cancer patients.
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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 | 0.001 |
| 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) | 0.003 | 0.001 |
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".