Silencing Red Blood Cell Recognition toward Anti-A Antibody by Means of Polyelectrolyte Layer-by-Layer Assembly in a Two-Dimensional Model System
Bibliographic record
Abstract
Silencing the antigenic response of red blood cells (RBCs) is a prerequisite toward the development of universal blood transfusion. Using a two-dimensional (2D) model whereby nonfixed RBCs are adsorbed on a human fibronectin (HFN)-coated surface, we demonstrate that the layer-by-layer (LbL) assembly technique of biocompatible polyelectrolytes can be employed to achieve the immunocamouflage of RBCs against the Anti-A antibody while maintaining the integrity and viability of the cells. The multilayered film consisted of a protecting shell (P-shell), containing five bilayers of chitosan-graft-phosphorylcholine (CH-PC) and sodium hyaluronate (HA), covered by a camouflage shell (C-shell) made up of five bilayers of poly-(L-lysine)-graft-poly(ethylene glycol) (PLL-PEG) and alginate (AL). Control experiments in which RBCs were coated by (CH-PC/HA)(10) bilayers indicated that the two polyelectrolytes alone did not prevent immunorecognition. The LbL film formation on RBCs and model substrates was monitored by quartz crystal microbalance with dissipation factor (QCM-D) and analyzed through zeta-potential measurements, atomic force microscopy (AFM), and optical microscopy. Antibody interaction with the coated RBCs was investigated by QCM-D, fluorescence microscopy, and hemolysis assays. Results from these measurements demonstrated that the hybrid LbL system built-up with different sets of polyelectrolytes was able to protect the RBCs from hemolysis and recognition by the Anti-A antibody.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".