Intracellular ROS profile in hematopoietic progenitors of MDS patients: association with blast count and iron overload
Bibliographic record
Abstract
Objectives Reactive oxygen species (ROS) are under scrutiny as a participant in the pathophysiology of myelodysplastic syndrome (MDS) and the progression of MDS to acute myeloid leukemia (AML). Measurement of intracellular ROS (iROS) is particularly important since iROS is a direct indicator of cellular health and integrity.Methods We developed a technique to measure standardize iROS (siROS) level in lymphocytes and bone marrow (BM) CD34+ hematopoietic progenitors using the fluorescent probe dichlorofluorescein (DCF). We then quantified the siROS in 38 consecutive BM specimens from 27 MDS patients over the course of 10 months. Disease outcome of these patients were also assessed.Results High serum ferritin, high blast count and poor IPSS were associated with inferior survival and AML progression in this cohort. High blast MDS patients had lower siROS in their BM CD34+ cells than those of low blast patients, consistent with increased reliance on glycolysis and enhanced ROS defense in high blast MDS. We also observed narrower siROS distribution in the BM CD34+ cells of high blast patients, suggesting that loss of heterogeneity in ROS content accompanies the clonal evolution of MDS. Furthermore, we observed a strong correlation between CD34+ cells siROS and serum ferritin level in high blast patients. In one case, iron chelation therapy (ICT) resulted in parallel decreases in serum ferritin and CD34+ cells siROS.Conclusion Our findings established the siROS profile in early hematopoietic cells of MDS patients and its relationship with blast count and iron overload.
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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 | 0.001 | 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".