A Manganese Porphyrin Platform for the Design and Synthesis of Molecular and Targeted MRI Contrast Agents
Bibliographic record
Abstract
Magnetic resonance imaging (MRI) contrast agents, in contrast to the plethora of fluorescent agents available to target disease biomarkers or exogenous implants, have remained predominantly non-specific. That is, they do not preferentially accumulate in specific locations in vivo because doing so necessitates longer contrast retention, which is contraindicated for current gadolinium (Gd) agents. This double-edge sword implies that Gd agents can offer either rapid elimination (but lack specificity) or targeted accumulation (but with toxicity risks). For this reason, MRI contrast agent innovation has been severely constrained. Gd-free alternatives based on manganese (Mn) chelates have been largely ineffective, as they are inherently unstable. In this study, we present a Mn(III) porphyrin (MnP) platform for bioconjugation, offering the highest stability and chemical versatility compared to any other T1 contrast agent. We exploit the inherent metal stability conferred by porphyrins and the absence of pendant bases (found in Gd or Mn chelates) that limit versatile functionalization. As proof-of-principle, we demonstrate labeling of human serum albumin, a model protein, and collagen hydrogels for applications in in-vivo targeted imaging and material tracking, respectively. In-vitro and in-vivo results confirm unprecedented metal stability, ease of functionalization, and high T1 relaxivity. This new platform opens the door to ex-vivo validation by fluorescent imaging and multipurpose molecular imaging in vivo.
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".