Suicidal death of erythrocytes in cancer and its chemotherapy: A potential target in the treatment of tumor‐associated anemia
Bibliographic record
Abstract
In analogy to apoptosis of nucleated cells, erythrocytes may enter eryptosis characterized by cell shrinkage and cell membrane scrambling. Eryptotic erythrocytes are rapidly cleared from circulating blood and may adhere to the vascular wall. Stimulation of eryptosis thus impairs microcirculation and leads to anemia as soon as the loss of erythrocytes cannot be fully compensated by enhanced erythropoiesis. Signaling stimulating eryptosis includes increase of cytosolic Ca2+‐activity, ceramide, caspases, calpain, p38‐kinase, protein‐kinase C, Janus‐activated kinase 3, casein‐kinase 1α, and cyclin‐dependent kinase 4. Eryptosis is inhibited by AMP‐activated kinase, p21‐activated kinase 2, cGMP‐dependent protein‐kinase, mitogen‐ and stress‐activated kinase, and sorafenib‐ and sunitinib‐sensitive tyrosine‐kinases. Eryptosis is triggered by complement, hyperosmotic shock, energy‐depletion, oxidative stress, multiple xenobiotics including diverse cytostatic drugs, diabetes, hepatic failure, iron‐deficiency, chronic kidney disease, hemolytic‐uremic‐syndrome, fever, systemic lupus erythematosus, infections, sepsis, sickle cell anemia, thalassemia, glucose‐6‐phosphate‐dehydrogenase deficiency, and Wilson´s disease. Compelling evidence points to a decisive role of eryptosis in anemia of malignancy. As shown for lung cancer, eryptosis inducing plasma components accumulate in cancer patients and trigger oxidative stress and ceramide. The tumor‐induced eryptosis leads to anemia despite increased erythropoiesis. The stimulation of eryptosis in malignancy is compounded by cytostatic treatment, as a large number of cytostatic agents trigger eryptosis. Inhibiting eryptosis may be a useful strategy in reducing tumor‐induced anemia and impaired microcirculation. Inhibitors of eryptosis may, however, be harmful, if they similarly interfere with death of tumor cells. Clearly, additional experimental effort is required to achieve killing of tumor cells with simultaneous avoidance of stimulated eryptosis.
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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) | 0.002 | 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".