Carbohydrate-Coated Fluorescent Silica Nanoparticles as Probes for the Galactose/3-Sulfogalactose Carbohydrate–Carbohydrate Interaction Using Model Systems and Cellular Binding Studies
Bibliographic record
Abstract
The carbohydrates galactose and 3-sulfogalactose, found on sphingolipids in myelin, interact with each other via a carbohydrate-carbohydrate interaction (CCI). In oligodendrocytes, this interaction triggers a signaling cascade resulting in cytoskeletal rearrangements and reorganization of glycolipids and proteins at the cellular surface. These rearrangements can also be triggered by synthetic multivalent glycoconjugates. In this report, we describe the synthesis of glycan-coated silica nanoparticles and their subsequent binding to cultured oligodendrocytes and purified myelin. Fluorescent silica nanoparticles with an azidosiloxane-derived outer shell were functionalized with carbohydrates using the copper-promoted azide-alkyne cycloaddition reaction. The carbohydrate-carbohydrate interaction between galactose and 3-sulfogalactose was examined by measuring the binding of 3-sulfogalactose-containing nanoparticles to galactolipids that had been immobilized in a multiwell plate. Particle aggregation mediated by CCI was observed by TEM. The interaction of the particles with oligodendrocytes and purified myelin was examined using fluorescence microscopy, providing direct evidence for binding of galactose and 3-sulfogalactose-coated nanoparticles to oligodendrocytes and myelin fragments.
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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".