Inflammatory pathology markers (activated microglia and reactive astrocytes) in early and late onset Alzheimer disease: a <i>post mortem</i> study
Bibliographic record
Abstract
AIMS: The association between the pathological features of AD and dementia is stronger in younger old persons than in older old persons suggesting that additional factors are involved in the clinical expression of dementia in the oldest old. Cumulative data suggests that neuroinflammation plays a prominent role in Alzheimer's disease (AD) and different studies reported an age-associated dysregulation of the neuroimmune system. Consequently, we sought to characterize the pattern of microglial cell activation and astrogliosis in brain post mortem tissue of pathologically confirmed cases of early and late onset AD (EOAD and LOAD) and determine their relation to age. METHODS: Immunohistochemistry (CD68 and glial fibrillary acidic protein) with morphometric analysis of astroglial profiles in 36 cases of AD and 28 similarly aged controls. RESULTS: Both EOAD and LOAD groups had higher microglial scores in CA1, entorhinal and temporal cortices, and higher astroglial response in CA1, dentate gyrus, entorhinal and temporal cortices, compared to aged matched controls. Additionally, EOAD had higher microglial scores in subiculum, entorhinal and temporal subcortical white matter, and LOAD higher astrogliosis in CA2 region. CONCLUSIONS: Overall, we found that the neuroinflammatory pathological markers in late stage AD human tissue to have a similar pattern in both EOAD and LOAD, though the severity of the pathological markers in the younger group was higher. Understanding the age effect in AD will be important when testing modifying agents that act on the neuroinflammation.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".