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.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".