Red blood cells with reduced deformability are selectively cleared from circulation in a mouse model
Bibliographic record
Abstract
ABSTRACT: Donated red blood cells (RBCs) collected for blood transfusions progressively lose their deformability due to natural aging and cold storage in blood bags. This loss accelerates circulatory clearance via mechanical sensing by the spleen, leading to RBC retention and entrapment. Although reduced deformability is known to shorten RBC circulation time, the extent to which splenic clearance distinguishes and removes RBCs with altered deformability is poorly understood. Here, we show that subpopulations of donor RBCs with a deformability distribution distinct from endogenous recipient's RBCs are selectively and specifically cleared from circulation within 24 hours of infusion in a mouse model, whereas donor RBCs with a deformability distribution similar to endogenous recipient RBCs persist and undergo normal clearance. We performed this study by treating murine donor RBCs with the mild catalase inhibitor aminotriazole to generate donor RBCs with a widened range of deformability. These cells were then fluorescently labeled and infused into syngeneic recipients. Using a microfluidic device capable of deformability-based sorting of RBCs, we concurrently measured the deformability distribution of donor RBCs pretransfusion and posttransfusion, along with endogenous recipient RBCs. Our findings provide direct evidence that RBCs with deformability profiles distinct from endogenous recipient RBCs are selectively and specifically cleared from circulation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".