Transition metal distribution in the brain and spinal cord of a dysmyelinated rodent model
Bibliographic record
Abstract
Transition metal concentrations in the central nervous system (CNS) are altered in neurodegenerative diseases such as Alzheimer’s, Parkinson’s and multiple sclerosis. A common symptom of these diseases is demyelination, which is the degradation of the myelin sheath that encapsulates the neurons in vertebrates. Transition metal concentrations were measured in Long Evans Shaker (LES) rodent model and compared to healthy age-matched controls to investigate the relationship between transition metals and myelination. Micro probe Synchrotron Radiation X-ray Fluorescence (µSRXRF) was used to measure concentrations of manganese (Mn), iron (Fe), copper (Cu), and zinc (Zn) in regions of grey matter and white matter in Shaker rodents and their age-matched Long Evans (LE) controls in the cerebellum and spinal cord. In the cerebellum, the concentrations of all elements were significantly increased in the white matter of the Shaker model, and decreased in the gray matter of the Shaker model in comparison to their age and region matched controls. In the spinal cord samples, concentrations of all metals were higher in white matter and grey matter of Shaker rat spinal cord compared to those in the control rat spinal cord. This study demonstrated that the sensitivity of µSRXRF is sufficient to discriminate between the elemental distributions of gray and white matter of the brain sections and spinal cords in the two groups. The observed significant increase of Mn, Fe, Zn and Cu in the white matter of the Shaker animals in the cerebellum and spinal cord compared to controls could be the result of astrocytic glial cells replacing the myelin in the CNS. Unlike other imaging techniques, the fine resolution of µSRXRF enables specific regions of gray matter structures namely, the molecular layer and the granule layer to be identified in the rat CNS, and their transition metal concentrations to be quantified. This work will further establish µSRXRF as a powerful analytic technique for compositional studies in brain sections from models of brain disease.
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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".