Multivalent glycoconjugate syntheses and applications using aromatic scaffolds
Bibliographic record
Abstract
Glycan-protein interactions are of utmost importance in several biological phenomena. Although the variety of carbohydrate residues in mammalian cells is limited to less than a dozen different sugars, their spatial topographical presentation in what is now associated as the "glycocodes" provides the fundamental keys for specific and high affinity "lock-in" recognition events associated with a wide range of pathologies. Toward deciphering our understanding of these glycocodes, chemists have developed new creative tools that included dendrimer chemistry in order to provide monodisperse multivalent glycoconjugates. This review provides a survey of the numerous aromatic architectures generated for the multivalent presentation of relevant carbohydrates using covalent attachment or supramolecular self-assemblies. The basic concepts toward their controlled syntheses will be described using modern synthetic procedures with a particular emphasis on powerful organometallic methodologies. The large variety of dendritic aromatic scaffolds, together with a brief survey of their unique biophysical and biological properties will be critically reviewed. The distinctiveness of the resulting multivalent glycoarchitectures, encompassing glycoclusters, glycodendrimers and molecularly defined self-assemblies, in forming well organized cross-linked lattices with multivalent carbohydrate binding proteins (lectins) together with their photophysical, medical, and imaging properties will also be briefly highlighted. The topic will be presented in increasing order of aromatic backbone complexities and will end with fullerenes together with self-assembled nanostructures, thus complementing the various scaffolds described in this special thematic issue dedicated to multivalent glycoscience.
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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) | 0.002 | 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".