Could the Crosstalk Between Myeloid-Derived-Suppressor Cells and Regulatory T Cells Have a Role in Beta-Thalassemia?
Bibliographic record
Abstract
Background: Secondary iron overload, alloimmunization, and increased risk of infection are common complications in patients with transfusion-dependent thalassemia (TDT). Regulatory T cells (Tregs) and myeloid-derived suppressor cells (MDSCs) play an essential role in preventing excessive immune response. This research aimed to study the interaction between Tregs and MDSCs in TDT patients and to evaluate the association of these cell types with disease severity. Methods: This case-control study included 26 patients with TDT and 23 healthy, age- and sex-matched controls. All patients were investigated for complete blood count (CBC), serum ferritin, and flow cytometric analysis of peripheral blood to detect Tregs, MDSCs, and MDSC subsets. Results: A significant increase was observed in the frequencies of Tregs and MDSCs, particularly monocytic MDSCs (MO-MDSCs), in TDT patients compared with controls. The frequencies of these cells showed a direct association with ferritin level and total leukocyte count and an inverse association with hemoglobin level. Furthermore, a positive correlation was observed between Tregs and each of the total MDSCs and MO-MDSCs. Conclusions: Levels of Tregs and MDSCs increased in TDT and may probably have a role in suppressing the active immune systems of TDT patients.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".