Brain Pathology in Mucopolysaccharidoses (MPS) Patients with Neurological Forms
Bibliographic record
Abstract
Mucopolysaccharidoses (MPS) are the group of lysosomal storage disorders caused by deficiencies of enzymes involved in the stepwise degradation of glycosaminoglycans. To identify brain pathology common for neurological MPS, we conducted a comprehensive analysis of brain cortex tissues from post-mortem autopsy materials of eight patients affected with MPS I, II, IIIA, IIIC, and IIID, and age-matched controls. Frozen brain tissues were analyzed for the abundance of glycosaminoglycans (heparan, dermatan, and keratan sulfates) by LC-MS/MS, glycosphingolipids by normal phase HPLC, and presence of inflammatory cytokines interleukin-6 (IL-6) and tumor necrosis factor superfamily member 10 (TNFSF10) by Western blotting. Fixed tissues were stained for the markers for microgliosis, astrogliosis, misfolded proteins, impaired autophagy, and GM2ganglioside. Our results demonstrate that increase of heparan sulfate, decrease of keratan sulfate, and storage of simple monosialogangliosides 2 and 3 (GM2 and GM3) as well as the neutralglycosphingolipid, LacCer, together with neuroinflammation and neuronal accumulation of misfolded proteins are the hallmarks of brain pathology in MPS patients. These biomarkers aresimilar to those reported in the corresponding mouse models, suggesting that the pathological mechanism is common for all neurological MPS in humans and mice.
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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) | 0.001 | 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 | 0.000 | 0.000 |
| 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".