Magnetic Resonance Imaging and Iron-oxide Nanoparticles in the era of Personalized Medicine
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.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".