Immunologic Mechanisms Underlying Chronic Hemolysis in Sickle Cell Anemia
Bibliographic record
Abstract
Introduction: The mechanism(s) by which chronic hemolytic anemia occurs in sickle cell disease (SCD) is not fully explained by the abnormal physical properties of hemoglobin. We aim to identify disease-specific immunologic ligand/receptor pairs that mediate chronic hemolysis by macrophages in SCD. Methods: M1/M2 macrophages were produced by in vitro culture and polarization. RBCs, plasma and monocytes were isolated from patients with SCD and were examined for various surface markers and plasma proteins using flow cytometry and ELISA. Phagocytic conditions of sickle RBCs were explored in vitro using the MOnocyte-Macrophage Assay (MOMA). Results: Autologous M2 macrophages, but neither M1 macrophages nor monocytes, phagocytosed sickle RBCs, but not normal RBCs, and the opsonization with sickle plasma, not normal plasma, increases their phagocytosis. SCD RBCs exhibit increased phosphatidylserine (PS) and mannose on their cell surface compared to normal RBCs, and the blockade of PS, mannose receptor (CD206) and Fc- receptors all result in a decrease of phagocytosis. The molecules lactadherin (MFG-E8) and pentraxin-3 (PTX-3) were also significantly elevated in SCD plasma in comparison to normal plasma, suggesting a possible role in RBC phagocytosis. Conclusions: Hemolysis of sickle RBCs spears to be mediated by both the M2 macrophage phenotype, RBC expression of PS and mannose, and substances present in the plasma of SCD patients, possibly including MFG-E8 and PTX-3.
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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".