Investigating White Matter Inflammatory Cells and their Relationship with Beta‐Amyloid in Alzheimer's Disease
Bibliographic record
Abstract
Alzheimer’s disease (AD) is a chronic neurodegenerative condition affecting millions of people worldwide. With an aging population, it is predicted that by 2050 the number of individuals living with AD will triple, resulting in an increased social and economic burden. Previous research has demonstrated that the pathological process leading to AD occurs years before a positive diagnosis. Therefore, identifying biomarkers that co‐occur within the early stage of AD progression and that predict worsening cognitive outcomes are critically needed. AD is characterized by the presence of beta‐amyloid plaques, neurofibrillary tangles and neuroinflammation. Clinically, being unable to recall new information is the most common outcome of AD. White matter inflammation is mediated by microglial activation and astrocytosis and are thought to be important underlying mechanisms involved in the pathogenesis of AD progression, while also being predictive of future cognitive decline. In a transgenic rat model of AD, it has been previously found that microglial activation within the white matter tracts was strongly associated with impairments in executive function. Age‐related differences in inflammatory cells have also been identified. One study found a significant increase in age‐associated white matter inflammatory cells in amyloid‐plaque negative rats at 7‐and‐8‐months of age. Sex‐and‐age related differences in inflammatory cells and amyloid‐beta have been identified, however, they remain poorly understood in the white matter. The purpose of this study was to investigate the sex‐specific‐age trajectory of white matter inflammatory cells in a wildtype rat model of normal aging and in two transgenic rat models of AD: one in an amyloid‐plaque negative, and one in an amyloid‐plaque positive environment. We hypothesized that microglial activation increases with normal aging and is further exacerbated by the presence of beta‐amyloid deposition. Male (n=5) and female (n=5) Fischer 344 wildtype, transgenic APP21 (amyloid‐plaque negative) and APP/PS1 (amyloid‐plaque positive) rats at 3, 9 and 15‐months of age were included in the experimental design. Immunohistochemical analyses were conducted to detect the presence of microglial activation, pro‐inflammatory M1 microglial activation, astrocytosis and beta‐amyloid. Results demonstrate unique age‐dependant trajectories in microglial activation, particularly in the corpus callosum, supraventricular corpus callosum and internal capsule of the wildtype, transgenic APP/21 and APP/PS1 rat genotypes. Given that only the APP/PS1 rats demonstrated age‐dependant plaque deposition, our results indicate that microglial activation within the white matter is associated with changes in amyloid‐plaque deposition. Future work will aim to better understand the mechanisms related to the relationship between white matter microglial activation, amyloid‐plaque deposition and cognitive impairment. A better understanding of this relationship can aid in the development of an earlier method of detecting AD, which in turn can better define a new therapeutic window of opportunity to target microglial activation and prevent neurodegeneration and cognitive decline associated with 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 | 0.000 |
| 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".