MRI contrast enhancement of liver pre-neoplasia using iron–tannic nanoparticles
Bibliographic record
Abstract
MRI contrast agents which are nanoparticles of iron-tannic complexes (so-called Fe-TA NPs) that can be used for detecting liver preneoplasia. Preliminary assessment of their toxicity in healthy rats provides suitable imaging dose ranges with acceptable toxicity. In diethylnitrosamine (DEN) induced rats, it is shown that Fe-TA NPs are capable of enhancing MRI signals in rat livers having pre-neoplastic lesions within 60 minutes post-injection. The enhancement efficacy is strongly dependent on the characteristics of pre-neoplastic foci (GST-P+ foci). The highest enhancement was in good correlation with the size of GST-P+ foci and amount of Fe-TA NPs accumulated in the liver, and might be caused by the dysfunction of liver sinusoids along with cellular uptake capability of pre-neoplastic hepatocytes. Our results show that Fe-TA NPs are of great interest to develop as an efficient MRI imaging agent for risk assessment of liver cancer.
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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) | 0.001 | 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".