One‐Step Labeling of Collagen Hydrogels with Polydopamine and Manganese Porphyrin for Non‐Invasive Scaffold Tracking on Magnetic Resonance Imaging
Why this work is in the frame
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Bibliographic record
Abstract
Biomaterial scaffolds are the cornerstone to supporting 3D tissue growth. Optimized scaffold design is critical to successful regeneration, and this optimization requires accurate knowledge of the scaffold's interaction with living tissue in the dynamic in vivo milieu. Unfortunately, non-invasive methods that can probe scaffolds in the intact living subject are largely underexplored, with imaging-based assessment relying on either imaging cells seeded on the scaffold or imaging scaffolds that have been chemically altered. In this work, the authors develop a broadly applicable magnetic resonance imaging (MRI) method to image scaffolds directly. A positive-contrast "bright" manganese porphyrin (MnP) agent for labeling scaffolds is used to achieve high sensitivity and specificity, and polydopamine, a biologically derived universal adhesive, is employed for adhering the MnP. The technique was optimized in vitro on a prototypic collagen gel, and in vivo assessment was performed in rats. The results demonstrate superior in vivo scaffold visualization and the potential for quantitative tracking of degradation over time. Designed with ease of synthesis in mind and general applicability for the continuing expansion of available biomaterials, the proposed method will allow tissue engineers to assess and fine-tune the in vivo behavior of their scaffolds for optimal regeneration.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 it