Tissue microarray methodology identifies complement pathway activation and dysregulation in progressive multiple sclerosis
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Bibliographic record
Abstract
The complement pathway has potential contributions to both white (WM) and grey matter (GM) pathology in Multiple Sclerosis (MS). A quantitative assessment of complement involvement is lacking. Here we describe the use of Tissue MicroArray (TMA) methodology in conjunction with immunohistochemistry to investigate the localization of complement pathway proteins in progressive MS cortical GM and subcortical WM. Antibodies targeting complement proteins C1q, C3b, regulatory proteins C1 inhibitor (C1INH, complement receptor 1 (CR1), clusterin, factor H (FH) and the C5a anaphylatoxin receptor (C5aR) were utilised alongside standard markers of tissue pathology. All stained slides were digitised for quantitative analysis. We found that numbers of cells immunolabelled for HLA-DR, GFAP, C5aR, C1q and C3b were increased in WM lesions (WML) and GM lesions (GML) compared to normal appearing WM (NAWM) and GM (NAGM), respectively. The complement regulators C1INH, CR1, FH and clusterin were more abundant in WM lesions, while the number of C1q+ neurons were increased and the number of C1INH+, clusterin+, FH+ and CR1+ neurons decreased in GM lesions. The number of complement component positive cells (C1q, C3b) correlated with complement regulator expression in WM, but there was no statistical association between complement activation and regulator expression in the GM. We conclude that TMA methodology and quantitative analysis provides evidence of complement dysregulation in MS GML, including an association of the numerical density of C1q+ cells with tissue lesions. Our work confirms that complement activation and dysregulation occur in all cases of progressive MS and suggest that complement may provide potential biomarkers of the disease.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it