Cellular MRI contrast via coexpression of transferrin receptor and ferritin
Bibliographic record
Abstract
Recently there has been growing interest in the development and use of iron-based contrast agents for cellular imaging with MRI. In this study we investigated coexpression of the transferrin receptor and ferritin genes to induce cellular contrast in a biological system. Expression of transgenic human transferrin receptor and human ferritin H-subunit was induced in a stably transfected mouse neural stem cell line. When grown in iron-rich medium, the transgenic cells accumulated significantly more iron than control cells, with a trend toward an increase in reactive oxygen species, but no detrimental effects on cell viability. This cellular iron significantly increased the transverse relaxivities, R2 and R2*, at 1.5 T and 7 T. By comparing measurements in the same cell samples at 1.5 T and 7 T, we confirmed the expected increase in relaxivity with increasing field strength. Finally, supplemented transgenic cells transplanted into mouse brain demonstrated increased contrast with surrounding neural tissue on T2*-weighted MR brain images compared to controls. These results indicate that dual expression of proteins at different critical points in the iron metabolism pathway may improve cellular contrast without compromising cell viability.
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".