Patterns of Mixed Pathologies in Down Syndrome
Bibliographic record
Abstract
BACKGROUND: Down syndrome (DS) is frequently associated with Alzheimer's disease (AD)-related neuropathological changes. There are few observations on the spectrum of mixed proteinopathies in DS patients. OBJECTIVE: This study aimed to evaluate multiple disease-associated proteinopathies in a series of DS cases. METHODS: We analyzed the distribution of neurodegenerative disease associated proteins in postmortem brain samples from 11 DS cases (6 females, median age 57, range 38-66 years). Sections were stained for phosphorylated tau, 3-repeat and 4-repeat tau, amyloid-β, alpha synuclein, phosphorylated TDP-43, and p62. A comprehensive anatomical mapping and staging were applied for all proteins. RESULTS: Tau and amyloid-β pathology was prevalent in all cases and compatible with that typically seen in AD with some subtle deviations. Four of 11 cases presented with Lewy-related pathology (LRP). Two cases followed the Braak staging (stage 4 and 5) whereas 2 cases presented with an atypical distribution. Two cases showed limbic predominant age-related TDP-43 encephalopathy (LATE) (stage 1 and stage 2) neuropathologic change. Two cases exhibited aging-related tau astrogliopathy (ARTAG). CONCLUSION: In addition to subtle deviations from AD regarding the morphology of amyloid-β deposition and distribution of neuronal tau pathology, we find that the spectrum of mixed-pathologies in DS show distinctive features such as deviations from the Braak staging of LRP and that LATE neuropathologic change and ARTAG pathology can be seen in individuals younger than in sporadic AD cases. Our observations support the notion that DS has distinctive pathogenic pathways from sporadic AD.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".