Mild Hyperthermia Promotes and Accelerates Development and Maturation of Erythroid Cells
Bibliographic record
Abstract
Hyperthermia treatment has at times been associated with increased platelet levels in humans. The heat shock protein HSP70, which can be induced by hyperthermia in megakaryocytes and erythrocytes, was recently shown to protect GATA-1 from degradation and to be required for erythroid differentiation. Based on these findings, we hypothesize that mild hyperthermia (MH), such as fever (39°C), could impact the differentiation of hematopoietic progenitors into erythrocytes and their subsequent maturation. Cell growth and erythroid differentiation increased dramatically in cord blood CD34(+) cell cultures incubated under MH. Erythroid maturation was also strongly promoted, which resulted in an increased proportion of hemoglobinized and enucleated erythroids. The rise in erythroid development was traced to a strong synergistic activity between MH and erythropoietin (EPO). The molecular basis for this potent synergy appears to originate from the capacity of MH to increase the basal activation of several signaling molecules downstream of the EPO receptor and the transcriptional activity of GATA-1. Moreover, the potent impact of MH on erythroid development was found be dependent on increased intracellular levels of reactive oxygen species. Thus, fever-like temperatures can promote the differentiation of progenitors along the erythroid lineage and accelerate their maturation through normal regulatory circuitry.
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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 | 0.000 |
| 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".