Fluorescent chitosan-based nanohydrogels and encapsulation of gadolinium MRI contrast agent for magneto-optical imaging
Bibliographic record
Abstract
In the field of medical imaging, multimodal nanoparticles combining complementary imaging modalities can give rise to new forms of imaging techniques that are able to make diagnosis more precise and confident. In this context, resolution and sensitivity have often to be gathered into a single imaging probe, by combination of MRI and optical imaging for instance. Gadolinium chelate (Gd-CAs) loaded nanohydrogels, obtained from chitosan (CS) and hyaluronic acid (HA) matrix, have shown their efficiency to greatly improve MRI contrast (r1 ≥ 80 mM−1 s−1). In this study, nanohydrogels were made intrinsically fluorescent by chitosan pre-functionalization and a series of fluorescent chitosans were obtained by covalent grafting of rhodamine (Rhod: 6.3µM) or fluorescein (Fluo: 7.3µM) tags. By combining DOSY and fluorescence data, fluorescent chitosans (CS-Rhod and CS-Fluo) with a low degree of substitution were then selected and used to encapsulate high gadolinium loadings to obtain efficient magneto-optical nanohydrogels.
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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 | 0.000 | 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) | 0.000 | 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".