Fluorinated contrast agents for magnetic resonance imaging; a review of recent developments
Bibliographic record
Abstract
The development of medical imaging probes for magnetic resonance imaging (MRI) is a particularly dynamic area of research. At present, many prominent groups are dedicating significant resources to tailoring and optimising the performance of potential contrast agents. Whilst 1H MRI has become an indispensable tool for the imaging of disease states, it frequently suffers from low contrast owing to background signal from intrinsic 1H. As a result, increasing attention is being directed at compounds containing 19F as this nucleus has a similar NMR sensitivity to 1H and, importantly, intrinsic 19F signals are virtually undetectable in vivo. For several decades, perfluorinated molecules (in which all of the C–H bonds in the parent molecule have been replaced with C–F bonds) and highly fluorous gases such as SF6 have traditionally been used for these kinds of investigations and there have been some excellent reviews of these compounds and their applications. However, recently 19F imaging is showing signs of evolution, particularly as there have been several reports of fluorinated responsive (smart) agents, micelles, dendrimers and hyperbranched polymers being investigated as targets for 19F-MRI. Furthermore, examples of multimodal contrast agents containing 19F nuclei are also starting to emerge. In this review we aim to summarise these exciting recent chemical developments.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".