Optimized Mn‐doped iron oxide nanoparticles entrapped in dendrimer for dual contrasting role in MRI
Bibliographic record
Abstract
Magnetic resonance imaging has acquired importance as a major tool for diagnosis and staging of cancers in humans. Injection of certain imaging agents have proved to improve contrast between normal and cancer cells on magnetic resonance imaging (MRI). Using the principles of MR contrast imaging, we have designed a dual mode (T1 and T2) contrast agent based on folic acid functionalized manganese ferrite nanoparticles (MNP) entrapped in 3G polyamidoamide (PAMAM) dendrimers. The ratio of Mn:Fe was tuned to achieve optimal performance. This multifunctional nanocarrier system was developed for targeting cancer cells to produce both T1 and T2 contrast which in turn helps in better diagnosis and staging of cancer. FTIR spectroscopy, X-Ray diffraction, atomic absorption spectroscopy, UV-Visible spectroscopy, and dynamic light scattering measurements were employed to characterize the multifunctional system at different stages of engineering. The ratio of relaxivities r2/r1 is 4.6 at 1.5 T for the MNP prepared with 0.5 molar ratio of Mn/Fe based on MR images obtained from phantom and tumor bearing mouse. The value of r2/r1 shows that the 0.5 molar ratio of Mn/Fe can be used to prepare MNP for the production of dual mode contrast in MR imaging.
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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 | 0.000 | 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 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".