Neuroinflammation distinguishes HLA haplotypes in progressive supranuclear palsy
Bibliographic record
Abstract
ABSTRACT Objectives Progressive supranuclear palsy (PSP) is a neurodegenerative 4R tauopathy clinically presenting with atypical parkinsonism or cognitive behavioral changes and a relatively uniform neuropathology. We recently identified rare HLA haplotypes in PSP and now examine whether HLA haplotypes are associated with different cytopathological and clinical phenotypes. Methods Retrospective collection of clinical data and mapping of T and B cells, microglia, and phosphorylated-tau (p-Tau) cytopathologies in 32 PSP cases. Machine learning was used to analyze whether pathological variables and their ratios, or the sequence of clinical symptoms cluster or predict HLA haplotypes. Results Four groups were defined based on HLA haplotypes: i) 12 cases with the haplotype associated with narcolepsy ( DRB1 *15:01- DQB1 *06:02); ii) 11 cases with other DQ5-DQ6 haplotypes; iii) 8 cases with various haplotypes frequent in the general population; and iv) one case with the haplotype frequent in IgLON5-disease ( DRB1 *10:01- DQB1 *05:01). Neuropathology revealed regional differences in the severity of microglia load, density of cytotoxic T cells, and p-Tau cytopathologies between groups. HLA haplotypes were most distinguishable using machine learned features of inflammatory markers and ratios of neuropathological variables (clustering accuracy: 86.96% and 91.30%, respectively). The sequence of clinical symptoms and the ratios of neuropathological variables were the strongest predictors of HLA haplotypes (prediction accuracy=80.00% and 71.43%, respectively). Interpretation PSP pathology might be associated with various etiological-pathogenic events including targetable autoimmune mechanisms. The HLA-haplotype dependent diversity of neuroinflammatory markers should be evaluated in clinical and biomarker studies in, and beyond, PSP to understand its relevance for patient stratification in disease modifying therapy trials.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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".