Gray Matter Hypoxia in the Brain of the Experimental Autoimmune Encephalomyelitis Model of Multiple Sclerosis
Bibliographic record
Abstract
BACKGROUND: Multiple sclerosis (MS) has a significant inflammatory component and may have significant gray matter (GM) pathophysiology. Brain oxygenation is a sensitive measurement of the balance between metabolic need and oxygen delivery. There is evidence that inflammation and hypoxia are interdependent. In this paper, we applied novel, implanted PO2 sensors to measure hypoxia in cortical and cerebellar GM, in an inflammation-induced mouse model of MS. OBJECTIVE: Quantify oxygenation in cortical and cerebellar GM in the awake, unrestrained experimental autoimmune encephalomyelitis (EAE) mouse model and to relate the results to symptom level and disease time-course. METHODS: C57BL/6 mice were implanted with a fiber-optic sensor in the cerebellum (n = 13) and cortex (n = 24). Animals were induced with stimulation of the immune response and sensitization to myelin oligodendrocyte glycoprotein (MOG). Controls did not have MOG. We measured PO2 in awake, unrestrained animals from pre-induction (baseline) up to 36 days post-induction for EAE and controls. RESULTS: There were more days with hypoxia than hyperoxia (cerebellum: 34/67 vs. 18/67 days; cortex: 85/112 vs. 22/112) compared to time-matched controls. The average decline in PO2 on days that were significantly lower than time-matched controls was -8.8±6.0 mmHg (mean ± SD) for the cerebellum and -8.0±4.6 for the cortex. Conversely, the average increase in PO2 on days that were significantly hyperoxic was +3.2±2.8 mmHg (mean ± SD) for the cerebellum and +0.8±2.1 for the cortex. Cortical hypoxia related to increased behavioral deficits. Evidence for hypoxia occurred before measurable behavioral deficits. CONCLUSIONS: A highly inflammatory condition primed to a white matter (WM) autoimmune response correlates with significant hypoxia and increased variation in oxygenation in GM of both cerebellum and cortex in the mouse EAE model of MS.
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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) | 0.000 | 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 | 0.000 | 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".