Distinctive Toll-like Receptors Gene Expression and Glial Response in Different Brain Regions of Natural Scrapie
Bibliographic record
Abstract
Prion diseases are chronic and fatal neurodegenerative diseases characterized by the accumulation of disease-specific prion protein (PrPSc), spongiform changes, neuronal loss, and gliosis. Growing evidence shows that the neuroinflammatory response is a key component of prion diseases and contributes to neurodegeneration. Toll-like receptors (TLRs) have been proposed as important mediators of innate immune responses triggered in the central nervous system in other human neurodegenerative diseases, including Alzheimer’s disease, Parkinson’s disease, and amyotrophic lateral sclerosis. However, little is known about the role of TLRs in prion diseases, and their involvement in the neuropathology of natural scrapie has not been studied. We assessed the gene expression of ovine TLRs in four anatomically distinct brain regions in natural scrapie-infected sheep and evaluated the possible correlations between gene expression and the pathological hallmarks of prion disease. We observed significant changes in TLR expression in scrapie-infected sheep that correlate with the degree of spongiosis, PrPSc deposition, and gliosis in each of the regions studied. Remarkably, TLR4 was the only gene upregulated in all regions, regardless of the severity of neuropathology. In the hippocampus, we observed milder neuropathology associated with a distinct TLR gene expression profile and the presence of a peculiar microglial morphology, called rod microglia, described here for the first time in the brain of scrapie-infected sheep. The concurrence of these features suggests partial neuroprotection of the hippocampus. Finally, a comparison of the findings in naturallyinfected sheep versus an ovinized mouse model (tg338 mice) revealed distinct patterns of TLRgene expression.
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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) | 0.000 | 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) | 0.001 | 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".