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 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".