Bibliographic record
Abstract
Abstract The contrast agent (CA) has become an essential component of clinical magnetic resonance imaging (MRI) practices, routinely applied to enhance the visualization of various diseases that are otherwise hard to detect. The majority of commercial CAs are based on small Gd(III) complexes, which have been widely used in diagnostic medicine for more than three decades, benefiting tens of millions of patients worldwide. Recently, however, Gd‐based CAs have been identified as the cause of a severe adverse effect known as nephrogenic systemic fibrosis (NSF). In addition, increasing evidence suggest that release of toxic free Gd(III) ions from the MRI CAs lead to the deposit of Gd in the brain and other organs. Development of non‐Gd‐based MRI CAs, therefore, is increasingly important. As an essential micronutrient, Mn is a preferred element of choice in this regard and, therefore, has regained attention in research over recent years. In this article, we will first summarize the key elements for the design and evaluation of MRI CAs. Three types of Mn‐based agents, including small ionic Mn(II) agents, Mn(III) complexes, and Mn‐containing nanoparticles will then be reviewed, according to these key elements. Finally, the recent advances in developing Mn‐based MRI probes as molecular imaging sensors will be highlighted. Throughout the review, the advantages and limitations of Mn‐based agents for MRI, in comparison with the conventional Gd‐based CAs, are the focus of the discussions. Overall, certain types of Mn complexes, such as new water‐soluble MnPs, have shown unique benefits over their Gd counterparts. This includes high sensitivity at high clinical magnetic fields, better biocompatibility of Mn in comparison to Gd, high stability, flexible structure/function, and tunable pharmacokinetics. Some of the new Mn agents have demonstrated potential for future clinical applications, as well as for advanced sensor development.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".