Time‐course manganese chloride biodistribution and manganese‐induced <scp>MRI</scp> contrast in mouse organs
Bibliographic record
Abstract
Abstract Purpose To optimize manganese‐enhanced MRI (MEMRI) in mice by profiling the time‐course biodistribution and associated T1 contrast enhancement of MnCl2 injected subcutaneously to avoid abrupt spikes in blood manganese levels. Methods Manganese (Mn) biodistribution and Mn‐induced T1 contrast in healthy female adult CD‐1 mice were investigated at two doses (0.2 and 0.4 mmol/kg) and 2, 6, and 24 h following injection. T1‐weighted MRI and T1 mapping were performed at 3 T. The heart, liver, kidneys, leg skeletal muscle, lungs, spleen, and blood were collected and quantified for Mn content using inductively coupled plasma atomic emission spectroscopy. Toxicity was assessed on hematoxylin and eosin histological sections of the heart, liver, kidneys, lungs, and spleen. Results An injection dose of 0.2 mmol/kg produced significant T1 enhancement in the heart, liver, and kidneys, reaching peak enhancement at 2 h following injection. Doubling the dose did not produce further T1 enhancement in the heart, liver, nor kidneys. Skeletal muscle reached peak enhancement at 24 h and required an injection dose of 0.4 mmol/kg. Inductively coupled plasma atomic emission spectroscopy–measured tissue‐level Mn content corroborated MRI results and revealed peak Mn concentration also at 2 h following injection in the spleen, lungs, and blood. No organ toxicity was observed at either dose on histology. Conclusion The subcutaneous injection route provided substantial T1 contrast enhancement in all tissues investigated, without toxicity at the maximum dose of 0.4 mmol/kg tested. However, the injection dose and optimal postinjection imaging interval must be tailored to the organ of interest.
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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".