Unlocking the potential of biocompatible chitosan-hyaluronic acid nanogels labeled with fluorochromes: A promising step toward enhanced FRET bioimaging
Bibliographic record
Abstract
Chitosan is a natural polysaccharide widely used in medical formulations as nanoparticles due to their special properties. Our work aimed to assess the biocompatibility of chitosan-hyaluronic acid nanogels labeled with fluorochromes for use in biomedical applications, based on the FRET effect. The preparation method included the ionic gelation, grafting rhodamine or fluorescein isothiocyanate molecules onto the chitosan backbone. To assess the potential applications as fluorescence imaging tools of chitosan-fluorophores conjugates in diagnostics and therapies, SVEC4-10 cells (simian virus 40-transformed mouse microvascular endothelial cell line) and RAW264.7 murine macrophages were used within this study. Good biocompatibility was observed after 6 and 24 h of incubation with nanogels, with no increase in cell death or membrane damage for concentrations up to 120 μg/mL. Both types of fluorescent nanogels presented the tendency to agglomerate on the cell membrane's surface, and few cells were internalized, especially at the periphery of cells. Molecular dynamics simulations showed that distances between fluorophores fitted at values close to those calculated based on FRET experiments. These formulations can further incorporate gadolinium for better nanomedicine tools. • Chitosan nanogels with fluorochrome labeling are biocompatible for medical use. • Fluorescent nanogels showed good cell viability and membrane integrity. • Confocal microscopy revealed surface aggregation on cell membranes. • Molecular dynamics simulations supported the FRET effect. • These nanogels hold potential for improved MRI diagnostics with gadolinium.
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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 | 0.001 | 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".