ELR-CXC chemokine antagonism and cisplatin co-treatment additively reduce H22 hepatoma tumor progression and ameliorate cisplatin-induced nephrotoxicity
Bibliographic record
Abstract
Cisplatin (cis-diamminedichloroplatinum) is one of the most commonly used agents for the chemotherapy of various types of cancers, but its use is limited by its dose-dependent side-effects (e.g., nephrotoxicity). The ELR-CXC chemokines are potent tumor growth, metastatic and angiogenic factors and can foster tumor resistance to chemotherapeutic agents. They are also potent proinflammatory agents. The aim of the present study was to evaluate the added effects of combining cisplatin chemotherapy with ELR-CXC chemokine antagonism in a mouse H22 hepatoma cancer cell model. The mice were injected with tumor cells and were then treated with cisplatin (12.5 or 2 mg/kg doses), either alone or together with the chemokine antagonist CXCL8(3-72)K11R/G31P (G31P) (50 µg/kg). At varying time-points renal function was examined using blood urea nitrogen (BUN) and serum creatinine (SCr) as read-outs for the toxic effects of cisplatin, while tumor growth and metastasis were assessed as endpoints. High-dose cisplatin therapy reduced the tumor burden by 52%, while co-delivery of G31P further augmented the tumor growth-suppressive effects of this dose of cisplatin to 71%; G31P by itself and low-dose cisplatin reduced the tumor burden by 19 and 39%, respectively. G31P also reduced the nephrotoxic effects of high-dose cisplatin to the effects observed in the low-dose cisplatin-treated animals. These data confirm the beneficial effects of combined cisplatin chemotherapy and ELR-CXC chemokine anatagonism in the context of both tumor progression and adverse side-effects.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".