Demyelinating Diseases: Myeloperoxidase as an Imaging Biomarker and Therapeutic Target
Bibliographic record
Abstract
PURPOSE: To evaluate myeloperoxidase (MPO) as a newer therapeutic target and bis-5-hydroxytryptamide-diethylenetriaminepentaacetate-gadolinium (Gd) (MPO-Gd) as an imaging biomarker for demyelinating diseases such as multiple sclerosis (MS) by using experimental autoimmune encephalomyelitis (EAE), a murine model of MS. MATERIALS AND METHODS: Animal experiments were approved by the institutional animal care committee. EAE was induced in SJL mice by using proteolipid protein (PLP), and mice were treated with either 4-aminobenzoic acid hydrazide (ABAH), 40 mg/kg injected intraperitoneally, an irreversible inhibitor of MPO, or saline as control, and followed up to day 40 after induction. In another group of SJL mice, induction was performed without PLP as shams. The mice were imaged by using MPO-Gd to track changes in MPO activity noninvasively. Imaging results were corroborated by enzymatic assays, flow cytometry, and histopathologic analyses. Significance was computed by using the t test or Mann-Whitney U test. RESULTS: There was a 2.5-fold increase in myeloid cell infiltration in the brain (P = .026), with a concomitant increase in brain MPO level (P = .0087). Inhibiting MPO activity with ABAH resulted in decrease in MPO-Gd-positive lesion volume (P = .012), number (P = .009), and enhancement intensity (P = .03) at MR imaging, reflecting lower local MPO activity (P = .03), compared with controls. MPO inhibition was accompanied by decreased demyelination (P = .01) and lower inflammatory cell recruitment in the brain (P < .0001), suggesting a central MPO role in inflammatory demyelination. Clinically, MPO inhibition significantly reduced the severity of clinical symptoms (P = .0001) and improved survival (P = .0051) in mice with EAE. CONCLUSION: MPO may be a key mediator of myeloid inflammation and tissue damage in EAE. Therefore, MPO could represent a promising therapeutic target, as well as an imaging biomarker, for demyelinating diseases and potentially for other diseases in which MPO is implicated.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".