Hepcidin is cytotoxic to myeloma cells (P1337)
Bibliographic record
Abstract
Abstract Recent studies suggest that some cationic antimicrobial peptides (CAPs) contribute to innate anticancer immunity. Hepcidin is a CAP and acute phase reactant well known for its antibacterial properties and its role in iron homeostasis. No anticancer function has yet been assigned to hepcidin, although elevated hepcidin levels are found in patients with hematologic malignancies, including plasma cell myeloma. We therefore investigated the anticancer properties of hepcidin against myeloma cells in vitro. A hepcidin isoform lacking the iron-regulatory domain was used. Hepcidin toxicity was assessed by MTT survival assays and DNA fragmentation tests. Propidium iodide (PI) staining was used to measure plasma membrane damage, and scanning electron microscopy (SEM) was performed to visualize cellular membrane changes. Hepcidin impaired the survival of mouse and human myeloma cells and induced DNA fragmentation, suggesting that cytotoxicity may be due to apoptosis. Hepcidin caused substantial PI uptake in the myeloma cells, and SEM confirmed that hepcidin treatment resulted in cellular membrane pore formation. Interestingly, hepcidin was more toxic to melphalan-resistant myeloma cells than to melphalan-sensitive controls. Our data implicate a role for hepcidin in innate immunity against plasma cell myeloma. Further studies on the anticancer properties of hepcidin are warranted in order to fully understand its physiological role in the context of neoplastic disease.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".