Multimodal gadolinium oxysulfide nanoparticles: a versatile contrast agent for mesenchymal stem cell labeling
Bibliographic record
Abstract
Despite a clear development of innovative therapies based on stem cell manipulation, the availability of new tools to better understand and follow stem cell behavior and improve their biomedical applications is not adequate. Indeed, an ideal tracking device must have good ability to label stem cells as well as complete neutrality relative to their biology. Furthermore, preclinical studies imply in vitro and in vivo approaches that often require several kinds of labeling and/or detection procedures. Consequently, the multimodality concept presented in this work may present a solution to this problem as it has the potential to combine complementary imaging techniques. Spherical europium-doped gadolinium oxysulfide (Gd2O2S:Eu3+) nanoparticles are presented as a candidate as they are detectable by (1) magnetic resonance (MRI), (2) X-ray and (3) photoluminescence imaging. Whole body in vivo distribution, elimination and toxicity evaluation revealed a high tolerance of nanoparticles with a long-lasting MRI signal and slow hepatobiliary and renal clearance. In vitro labeling of a wide variety of cells unveils the nanoparticle potential for efficient and universal cell tracking. Emphasis on mesenchymal stromal cells (MSCs) leads to the definition of optimal conditions for labeling and tracking in the context of cell therapy: concentrations below 50 μg mL-1 and diameters between 170 and 300 nm. Viability, proliferation, migration and differentiation towards mesodermal lineages are preserved under these conditions, and cell labeling appears to be persistent and without any leakage. Ex vivo detection of as few as five thousand Gd2O2S:Eu3+-labeled MSCs by MRI combined with in vitro examination with fluorescence microscopy highlights the feasibility of cell tracking in cell therapy using this new nanoplatform.
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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".