A Nanoparticle Ink Allowing the High Precision Visualization of Tissue Engineered Scaffolds by MRI
Bibliographic record
Abstract
Abstract Hydrogels are widely used as cell scaffolds in several biomedical applications. Once implanted in vivo, cell scaffolds must often be visualized, and monitored overtime. However, cell scaffolds appear poorly contrasted in most biomedical imaging modalities such as magnetic resonance imaging (MRI). MRI is the imaging technique of choice for high‐resolution visualization of low‐density, water‐rich tissues. Attempts to enhance hydrogel contrast in MRI are performed with “negative” contrast agents that produce several image artifacts impeding the delineation of the implant's contours. In this study, a magnetic ink based on ultra‐small iron oxide nanoparticles (USPIONs; <5 nm diameter cores) is developed and integrated into biocompatible alginate hydrogel used in cell scaffolding applications. Relaxometric properties of the magnetic hydrogel are measured, as well as biocompatibility and MR‐visibility ( T 1 ‐weighted mode; in vitro and in vivo). A 2‐week MR follow‐up study is performed in the mouse model, demonstrating no image artifacts, and the retention of “positive” contrast overtime, which allows very precise delineation of tissue grafts with MRI. Finally, a 3D‐contouring procedure developed to facilitate graft delineation and geometrical conformity assessment is applied on an inverted template alginate pore network. This proof‐of‐concept establishes the possibility to reveal precisely engineered hydrogel structures using this USPIONs ink high‐visibility approach.
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".