The role of chronic inflammation on amyloid burden in multiple sclerosis (P5.241)
Bibliographic record
Abstract
OBJECTIVE: To evaluate the role of chronic inflammation on amyloid burden in multiple sclerosis (MS). BACKGROUND: Alzheimer’s disease (AD) is a degenerative disorder wherein β-amyloid deposition and inflammation are key pathological features. The complex interplay between inflammation and β-amyloid burden in AD is increasingly recognised. The influence of chronic inflammation on the development of β-amyloid pathology in MS is not known. DESIGN/METHODS: A cohort of pathologically confirmed MS cases (n=76) was compared to non-demented age- and sex-matched controls (n=68). Formalin-fixed paraffin embedded cortical tissue from the mesial temporal gyrus was immunostained for β-amyloid and myelin. Quantitative measures of β-amyloid burden in MS normal appearing grey matter (NAGM) and cortical lesions were compared to those derived from control NAGM. MS (n=30) and control (n=26) cases lying at the extremes of β-amyloid burden were additionally immunostained for microglial inflammation. RESULTS: MS and control cases did not differ in age or sex. Neuronal counts did not differ significantly between MS and control NAGM but were significantly reduced in MS lesions compared to MS NAGM (p = 0.001). Preliminary data showed that β-amyloid burden was significantly reduced in MS NAGM compared to controls (p=0.03) when adjusting for age and sex. MS cortical lesions demonstrated significantly less β-amyloid compared to MS NAGM (p<0.01), particularly at the lesional border (p < 0.001). In MS, microglial activation was significantly less in the high compared to low amyloid groups (p<0.05), with similar findings not observed in controls. CONCLUSIONS: These findings suggest the chronic inflammatory milieu in the MS brain protects against β-amyloid deposition, with microglial activation playing a central role. Studies evaluating the relationship between MS-specific microglial activation and amyloid processing are warranted. Study Supported by: GD is supported by a Goodger Scholarship (University of Oxford), and the Biomedical Research Centre, NIHR, Oxford.
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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 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".