Detecting Deoxyhemoglobin in Spinal Cord Vasculature of the Experimental Autoimmune Encephalomyelitis Mouse Model of Multiple Sclerosis Using Susceptibility MRI and Hyperoxygenation
Bibliographic record
Abstract
Susceptibility-weighted imaging (SWI) detects hypointensities due to iron deposition and deoxyhemoglobin. Previously it was shown that SWI detects hypointensities in the experimental autoimmune encephalomyelitis (EAE) model of multiple sclerosis (MS), most of which are due to intravascular deoxyhemoglobin, with a small proportion being due to iron deposition in the central nervous system parenchyma and demyelination. However, animals had to be sacrificed to differentiate these two types of lesions which is impractical for time course studies or for human application. Here, we proposed altering the inspired oxygen concentration during imaging to identify deoxyhemoglobin-based hypointensities in vivo. SWI was performed on lumbar spinal cords of naive control and EAE mice using 30% O2 then 100% O2. Some mice were imaged using 30% O2, 100% O2 and after perfusion. Most SWI-visible hypointensities seen with 30% O2 changed in appearance upon administration of 100% O2, and were not visible after perfusion. That hypointensities changed with hyperoxygenation indicates that they were caused by deoxyhemoglobin. We show that increasing the inspired oxygen concentration identifies deoxyhemoglobin-based hypointensities in vivo. This could be applied in future studies to investigate the contribution of vascular-based hypointensities with SWI in EAE and MS over time.
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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 | 0.001 | 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) | 0.001 | 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".