Poly(ethylene glycol)-alendronate coated nanoparticles for magnetic resonance imaging of lymph nodes
Bibliographic record
Abstract
Nanoparticulate systems can passively target regional lymphatic vessels and lymph nodes (LNs) after interstitial administration. Highly sensitive non-invasive imaging techniques, such as magnetic resonance imaging (MRI), can take advantage from particles' lymphotropic properties to provide a reliable tool to monitor lymphatic function and LN morphology with high spatial resolution. In this work, we developed and characterised a bioerodible nanosystem with MRI contrast properties, based on poly(ethylene glycol)-alendronate stabilised gadolinium calcium phosphate nanoparticles (NPs). After foot paw injection in mice, the particles exhibited a distinct pattern of gradual uptake into the local lymphatics and a localised deposition in the popliteal LN. Less variability in the onset of the signal, intensity and localisation was observed compared to the commercially available tracer gadobutrol, suggesting that these NPs could be useful to monitor physiological and dysfunctional lymphatic conditions. Moreover, dissolution of the particles indicated that they would be rapidly cleared from the body after imaging. Nevertheless, our findings call for an improvement of the system that includes reduction of gadolinium leakage from the NPs, and decrease in size of the latter to increase their selective uptake by the LN.
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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) | 0.000 | 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".