Effect of liposome-treated red blood cells in an anemic rat model
Bibliographic record
Abstract
CONTEXT: Liposomes have been shown to improve human red blood cell (RBC) in vitro quality by minimizing membrane damage occurring during 42-d hypothermic storage. Small animal models are necessary to evaluate novel blood products and guide future clinical studies. OBJECTIVES: The aim of this study was to assess the effect of liposome treatments on rat RBC hypothermic storage lesion (HSL) and to examine in vivo outcomes of transfusing liposome treated RBCs in a rat model. MATERIALS AND METHODS: Unilamellar liposomes were synthesized which contained saturated (DPPC:CHOL, 7:3 mol%), unsaturated (DOPC:CHOL, 7:3 mol%), saturated charged (DPPC:CHOL:PS, 6:3:1 mol%), and unsaturated charged (DOPC:CHOL:PS, 6:3:1 mol%) phospholipids. After liposome treatment, rat RBC quality was assessed by percent hemolysis, deformability, aggregation, hematological indices, microvesiculation, and cholesterol/phospholipid concentrations. An anemic rat model of myocardial ischemia and reperfusion (I/R) was used to evaluate the outcomes of transfusing liposome-treated RBCs. RESULTS: All four liposome treatments resulted in significant decreases in hemolysis, with the most prominent effect seen with DOPC-liposomes (DOPC: 1.6 ± 0.1% versus control: 3.1 ± 0.2%, p = 0.015). RBCs treated with uncharged liposomes had lower hemolysis compared with charged liposomes (3.4 ± 0.2% versus 3.9 ± 0.4%, p = 0.010). The in vivo study showed no significant difference in the hemoglobin levels and infarct size (53.3 ± 13.1% versus 45.3 ± 8.4%, p = 0.223) between liposome and control groups. DISCUSSION AND CONCLUSION: Liposome treatment improved in vitro quality of stored rat RBCs. However, the changes observed in vitro were not sufficient to improve the in vivo outcomes of myocardial I/R in anemic rats transfused with liposome-treated RBCs.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".