Enhanced Cell Surface Polymer Grafting in Concentrated and Nonreactive Aqueous Polymer Solutions
Bibliographic record
Abstract
Macromolecular cell surface modification techniques have shown tremendous utility in various biomedical applications. However, a major drawback concerns inefficient cell surface modification caused by the poor association of hydrophilic macromolecules with cell surfaces. Here, a novel, highly efficient, and universal strategy in which nonreactive "additive" macromolecules are used to modulate the grafting efficiency of cell surface reactive, hydrophilic macromolecules is described. Unprecedented enhanced cell surface modifications by up to 10-fold were observed when various concentrations of a suitable "additive" polymer was present with a constant and low concentration of a "reactive" macromolecule. The importance of this increased efficiency and the possible mechanisms involved are discussed. The cell compatible technique is demonstrated in the case of four different cell types--red blood cells (RBC), leukocytes, platelets, and Jurkat cells. A practical application of grafting macromolecules to cell surfaces in concentrated polymer solutions is demonstrated by the enhanced camouflage of RBC surface antigens for the development of RhD null RBC. In principle, the technique can be adapted to various macromolecular systems and cell types, with significant potential for biomedical applications such as live cell based technologies.
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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) | 0.000 | 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".