B.1 Imaging metabolic changes in white matter following ischemic and hemorrhagic stroke onset in an animal model
Bibliographic record
Abstract
Background: What matter (WM) is particularly sensitive to ischemia and WM changes are observed following onset of ischemic stroke as well as during expansion of the stroke lesion. To better correlate neurobehavioural and functional assessments in our models we have developed imaging methods to aid in the differentiation and quantification of WM injury. Methods: We employ 3 mouse models of stroke: photothrombotic, temporary middle cerebral artery occlusion, and intracerebral hemorrhage. Naïve controls and surgical shams (for each model) are also characterized. We use Fourier transform infrared (FTIR) imaging and synchrotron-based X-ray fluorescence microscopy (XFM) to visualize metabolites and elemental markers, respectively. These post-mortem imaging techniques are combined with conventional histology to confirm neuroanatomic features and cell types. Results: The metabolic profile of WM in naïve, sham, and stroke models has been characterized in C57BL/6 mice. The metabolic markers we identify are highly specific and enable the automated differentiation of WM from other tissues. Our methods have been re-tooled to identify degeneration and injury of WM regions. Conclusions: The combination of FTIR imaging and XFM afford the means to readily differentiate WM changes following stroke onset. Significant dysregulation can be observed before the core or penumbra of the stroke lesion reaches WM-containing regions.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".