Glycodendrimers: versatile tools for nanotechnology
Bibliographic record
Abstract
Combining nanotechnology with glycobiology has triggered an exponential growth of research activities in the design of novel functional bionanomaterials (glyconanotechnology). More specifically, recent synthetic advances towards the tailored and versatile design of glycosylated nanoparticles namely glyconanoparticles, considered as synthetic mimetics of natural glycoconjugates, paved the way toward diverse biomedical applications. The accessibility of a wide variety of these structured nanosystems, in terms of shapes, sizes, and organized around stable nanoparticles have readily contributed to their development and applications in nanomedicine. In this context, glycosylated gold-nanoparticles (GNPs), glycosylated quantum dots (QDs), fullerenes, single-wall natotubes (SWNTs), and self-assembled glycononanoparticles using amphiphilic glycopolymers or glycodendrimers have received considerable attention to afford powerful imaging, therapeutic, and biodiagnostic devices. This review will provide an overview of the most recent syntheses and applications of glycodendrimers in glycoscience that have permitted to deepen our understanding of multivalent carbohydrate-protein interactions. Together with synthetic breast cancer vaccines, inhibitors of bacterial adhesions to host tissues including sensitive detection devices, these novel bionanomaterials are finding extensive relevance.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".