Manganese-Enhanced Magnetic Resonance Imaging in Experimental Spinal Cord Injury
Bibliographic record
Abstract
OBJECTIVE: Manganese (Mn(2+))-enhanced magnetic resonance imaging (MEMRI) is a potentially important tool for assessing neural tissue regeneration after spinal cord injury (SCI). We evaluated the relation between Mn(2+) and T1-weighted magnetic resonance (MR) signals in an SCI rat model. METHODS: Rats were divided into 4 groups with or without SCI (T9-level transection) and with or without Mn(2+) injection. Two microliters of 0.2 mol/L MnCl(2) was injected into the lateral ventricles. Magnetic resonance imaging (MRI) was performed 60 hours after injection. Signal intensities at cervical, thoracic, and lumbar levels were measured and normalized to the intensity of perivertebral muscles. Spinal cord sections were analyzed by inductively coupled plasma mass spectrometry (ICP-MS) for total Mn(2+) content. The results of ICP-MS were compared with MR signal intensity. RESULTS: T1-weighted MR signal intensity and ICP-MS-measured Mn(2+) were significantly decreased below the SCI injury site in Mn(2+)-injected groups with or without SCI, and were similar to intensity and Mn(2+) levels of noninjected animals. Signal intensity and Mn(2+) concentration tended to decrease from cervical to lumbar spinal levels in the control rats. ICP-MS data correlated with MRI results. CONCLUSION: The results confirmed Mn(2+) uptake in the spinal cord after intraventricular injection. T1-weighted MR signal intensity correlates with spinal Mn(2+) concentration as measured with ICP-MS. This work establishes the repeatability of MEMRI of the injured spinal cord and makes it possible to compare changes in axonal transport rates through the spinal cord after neuronal regeneration in vivo at different stages. MEMRI in animal models may improve understanding of the factors required to promote spinal cord regeneration.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".