Utilization of nanoparticles as X‐ray contrast agents for diagnostic imaging applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Among all the diagnostic imaging modalities, X-ray imaging techniques are the most commonly used owing to their high resolution and low cost. The improvement of these techniques relies heavily on the development of novel X-ray contrast agents, which are molecules that enhance the visibility of internal structures within the body in X-ray imaging. To date, clinically used X-ray contrast agents consist mainly of small iodinated molecules that might cause severe adverse effects (e.g. allergies, cardiovascular diseases and nephrotoxicity) in some patients owing to the large and repeated doses that are required to achieve good contrast. For this reason, there is an increasing interest in the development of alternative X-ray contrast agents utilizing elements with high atomic numbers (e.g. gold, bismuth, ytterbium and tantalum), which are well known for exhibiting high absorption of X-rays. Nanoparticles (NPs) made from these elements have been reported to have better imaging properties, longer blood circulation times and lower toxicity than conventional iodinated X-ray contrast agents. Additionally, the combination of two or more of these elements into a single carrier allows for the development of multimodal and hybrid contrast agents. Herein, the limitations of iodinated X-ray contrast agents are discussed and the parameters that influence the efficacy of X-ray contrast agents are summarized. Several examples of the design and production of both iodinated and iodine-free NP-based X-ray contrast agents are then provided, emphasizing the studies performed to evaluate their X-ray attenuation capabilities and their toxicity in vitro and 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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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