Characterizing White Matter Damage in Rat Spinal Cord with Quantitative MRI and Histology
Bibliographic record
Abstract
ABSTRACT Diffusion tensor imaging (DTI) and quantitative T(2) magnetic resonance imaging (MRI) were used to characterize ex vivo the white matter damage at 3 and 8 weeks following dorsal column transection (DC Tx) injury at the cervical level C5 of rat spinal cords. Luxol Fast Blue (LFB) and myelin basic protein (MBP) staining was used to assess myelin damage, and neurofilament-H in combination with neuron specific beta-III-tubulin (NF/Tub) staining was used to assess axonal damage. Average values of myelin water fraction (MWF), fractional anisotropy (FA), longitudinal diffusivity (D(long)), transverse diffusivity (D(trans)), and average diffusivity (D(ave)) were calculated in the fasciculus gracilis, fasciculus cuneatus, and the dorsal corticospinal tract (CST) 5 mm cranial, as well as 5 and 10 mm caudal to injury and correlated with histology. These tracts were selected as these contain bundles of parallel ascending and descending axons in very circumscribed areas with little intermingling of other axonal populations. Axonal and myelin degeneration occur cranial to injury in the funiculus gracilis and caudal to injury in the CST. Both MWF and D(trans) showed significant correlation with LFB staining at 3 weeks (0.64 and -0.49, respectively) and 8 weeks post-injury (0.88 and -0.71, respectively). Both D(long) and FA correlated significantly with NF/Tub staining at 3 weeks post-injury (0.78 and 0.64, respectively), while only D(long) displayed significant correlation 8 weeks post-injury (0.58 and 0.33, respectively). This study demonstrates that quantitative MRI can accurately characterize white matter damage in DC Tx model of injury in rat spinal cord.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".