Conjugation of A and B Blood Group Structures to Silica Microparticles for the Detection of Antigen-Specific B Cells
Bibliographic record
Abstract
Silica microparticles were functionalized with A and B blood group carbohydrate antigens (A type I, A type II, B type I, and B type II) to enable the detection and monitoring of ABO antigen-specific B cells. Microparticles were prepared via the Stöber synthesis, labeled with an Alexafluor fluorescent dye, and characterized via TEM and fluorescence microscopy. The silica microparticles were functionalized with (3-aminopropyl)trimethoxysilane (APTMS), followed by the use of an established fluorenylmethyloxycarbonyl (Fmoc)-protected PEG-based linker. The terminal Fmoc moiety of the PEG-based linker was then deprotected, yielding free amino groups, to which the A and B antigens were coupled. The carbohydrate antigens were synthesized with a p-nitrophenol ester to enable conjugation to the functionalized silica microparticles via an amide bond. The number of free amine groups available for coupling for a given mass of PEG-functionalized silica microparticles was quantified via reaction with Fmoc-glycine. The antigen-functionalized microparticles were then evaluated for their specificity in binding to A and B antigen-reactive B-cells via flow cytometry, and for blocking of naturally occurring antibodies in human serum. Selective binding of the functionalized microparticles to blood group-reactive B cells was observed by flow cytometry and fluorescence microscopy. The modular approach outlined here is applicable to the preparation of silica microparticles containing any carbohydrate antigen and alternative fluorophores or labels. This approach therefore comprises a novel, general platform for screening B cell populations for binding to carbohydrate antigens, including, in this case, the human A and B blood group antigens.
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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".