Fabrication of FITC‐doped silica nanoparticles and study of their cellular uptake in the presence of lectins
Bibliographic record
Abstract
Fluorescent silica nanoparticles are reported to be highly stable and biocompatible materials with high water solubility, which make them ideal candidates for biological applications. These nanoparticles can also be modified with biocompatible and targeting moieties and can be used for a variety of in vitro and in vivo applications, such as targeting, particle tracking, cargo carrier, and as contrast agents. In this study, fluorescent dye-doped silica nanoparticles were prepared by a modified Stöber method. The nanoparticles produced were surface functionalized with amine moieties for their conjugation with glucose-derived and galactose-based residues. The amine, glucose-derived, and galactose-based functionalized fluorescent silica nanoparticles were analyzed for their physiochemical properties such as sizes, polydispersities, organic layer content, and surface chemistries. The nanoparticles produced were then studied for their interactions with carbohydrate-specific lectins. These lectin bioconjugates have helped in understanding their interactions with cell-surface receptors. As expected, galactose-functionalized nanoparticles were found to specifically interact with RCA120 , as compared to other nanoparticles. These specific interactions of galactose-lectin conjugates were further studied on the hepatocytes cell surface in vitro. The aggregation of galactose-lectins conjugates on the plasma membrane was possibly due to the specific interactions of carbohydrates with cell-surface glycoproteins, hence preventing the uptake of these nanoparticles. The study has provided an interesting approach to mark the cell-surface glycoproteins with fluorescent probes using a combination of lectin-carbohydrate conjugates.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| 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 teacher head, 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".