Ultra‐small gadolinium oxide nanoparticles to image brain cancer cells <i>in vivo</i> with MRI
Bibliographic record
Abstract
The majority of contrast agents used in magnetic resonance imaging (MRI) is based on the rare-earth element gadolinium. Gadolinium-based nanoparticles could find promising applications in pre-clinical diagnostic procedures of certain types of cancer, such as glioblastoma multiforme. This is one of the most malignant, lethal and poorly accessible forms of cancer. Recent advances in colloidal nanocrystal synthesis have led to the development of ultra-small crystals of gadolinium oxide (US-Gd(2)O(3), 2-3 nm diameter). As of today, this is the smallest and the densest of all Gd-containing nanoparticles. Cancer cells labeled with a sufficient quantity of this compound appear bright in T(1)-weighted MRI images. Here we demonstrate that US-Gd(2)O(3) can be used to label GL-261 glioblastoma multiforme cells, followed by localization and visualization in vivo using MRI. Very high amounts of Gd are efficiently internalized and retained in cells, as confirmed with TEM and ICP-MS. Labeled cells were visualized in vivo at 1.5 T using the chicken embryo model. This is one more step toward the development of "positively contrasted" cell tracking procedures with MRI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".