Application of H2N-Fe3O4 Nanoparticles for Prostate Cancer Magnetic Resonance Imaging in an Animal Model
Bibliographic record
Abstract
This paper presents the efficacy of a contrast agent based on H2N-Fe3O4 nanoparticles for the detection of prostate cancer in an animal model using a preclinical 9.4 T MRI system. The relaxivities r1 and r2 of the nanoparticles were 6.31 mM−1s−1 and 8.33 mM−1s−1, respectively. Nanoparticles injected in a concentration of 2 mg Fe/mL decreased the tumor-relative T1 relaxation across all animals from 100 to 76 ± 26, 85 ± 27, 89 ± 20, and 97 ± 16 12 min 1 h, 2 h, and 24 h post injection, respectively. The corresponding T1 decrease in muscle tissues was 90 ± 20, 94 ± 23, 99 ± 12, and 99 ± 14. The relative T2 changes in the tumor were 82 ± 17, 89 ± 19, 97 ± 14, and 99 ± 8 12 min, 1 h, 2 h, and 24 h post injection, respectively, while, for muscle tissues, these values were 95 ± 11, 95 ± 8, 97 ± 6, and 95 ± 10 at the corresponding time points. The differences in the relative T1 and T2 were only significant 12 min after injection (p < 0.05), although a decrease was visible at each time point, but it was statistically insignificant (p > 0.05). The results showed the potential application of H2N-Fe3O4 nanoparticles as contrast agents for enhanced prostate cancer MRI.
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 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 | 0.000 |
| 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 | 0.001 |
| 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 teacher head, 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".