Expedient synthesis of functional single-component glycoliposomes using thiol–yne chemistry
Bibliographic record
Abstract
The preparation of a set of eight unprecedented amphiphilic neoglycolipids forming liposome nanoparticles is reported. The small library was readily obtained from various peracetylated propargyl glycopyranosides via efficient radical-initiated thiol-yne (TYC) coupling reactions using alkanethiols of different chain lengths. In addition, using sequential thiol-yne, both the nature and positioning of the lipophilic alkanethiols could be varied at will, thus providing unparalleled variability within the glycolipid structures. Two different classes of self-assemblies were prepared from the new neoglycolipids. First, liposomes of 150-300 nm were obtained by solvent injection of their ethanol or tetrahydrofuran (THF) solution in water. The resulting structures were analyzed by dynamic light scattering (DLS) and atomic force microscopy (AFM). The mannosylated lipid nanoparticle (compound 14) showed good stability in water. Alternatively, giant soft unilamellar vesicles were also obtained by film hydration and visualized by differential interference contrast microscopy (DIC). Incorporation of a hydrophobic dye to the solution prior to evaporation allowed visualization by confocal microscopy. Finally, the biological functions of the newly formed glycolipid vesicles were evaluated by multivalent carbohydrate-protein binding interactions using concanavalin A (ConA). Agglutination assays and the binding of glycolipid by dendritic cells (DCs) resulted in an increase in DCs immunostimulatory potential. Importantly, we did not see changes in cells viability at tested doses. This study provides a new, simple and highly efficient methodology to produce novel glyconanoparticle candidate as model in development of vaccine adjuvant and drug delivery system.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".