Magnetic Resonance Imaging and Iron-oxide Nanoparticles in the era of Personalized Medicine
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
Medical imaging is an important factor for diagnosis. It can be used to diagnose patients, differentiate disease stages, and monitor treatment regimens. Although different imaging technologies are available, MRI is sensitive over other imaging modalities as it is capable of deep tissue penetration allowing to image the anatomical, structural, and molecular level of diseased organs. Thus, it can be used as screening tool for disease staging. One of the important components of imaging is contrast agents which are used to increase the sensitivity of MRI technology. While different types of contrast agents are available, iron-oxide based nanoparticles (IONPS) are widely used as these are easy to formulate, functionalize, biocompatible and cost effective. In addition to its use as contrast agents, these have been used as drug carriers for the treatment of different types of diseases ranging from cancer, cardiovascular diseases, neurological disorders, autoimmune diseases, and infectious diseases. For the last two decades, there has been advancement in nanotheranostics, where IONPs are formulated to carry drug and be used as contrast agents in one system so that these can be used for image-guided therapy and monitor real-life treatment response in diseased tissue. This technology can be used to stratify patients into responders and non-responders and reduce adverse drug toxicity and lead to a tailored treatment. However, success of nanotheranostics depends on several factor, including identification of disease associated biomarkers that can be targeted on IONPs during formulation. While many challenges exist for the clinical translation of nanotheranostics, it still has the potential to be implemented in personalized treatment strategy. In this review article, we discussed the use of MRI technology and IONPs in relation to their application in disease diagnosis and nanotheranostics application in personalized medicine.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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