Acute systemic inflammation exacerbates neuroinflammation in Alzheimer's disease: IL‐1β drives amplified responses in primed astrocytes and neuronal network dysfunction
Bibliographic record
Abstract
Neuroinflammation contributes to Alzheimer's disease (AD) progression. Secondary inflammatory insults trigger delirium and can accelerate cognitive decline. Individual cellular contributors to this vulnerability require elucidation. Using APP/PS1 mice and AD brain, we studied secondary inflammatory insults to investigate hypersensitive responses in microglia, astrocytes, neurons, and human brain tissue. The NLRP3 inflammasome was assembled surrounding amyloid beta, and microglia were primed, facilitating exaggerated interleukin-1β (IL-1β) responses to subsequent LPS stimulation. Astrocytes were primed to produce exaggerated chemokine responses to intrahippocampal IL-1β. Systemic LPS triggered microglial IL-1β, astrocytic chemokines, IL-6, and acute cognitive dysfunction, whereas IL-1β disrupted hippocampal gamma rhythm, all selectively in APP/PS1 mice. Brains from AD patients with infection showed elevated IL-1β and IL-6 levels. Therefore, amyloid leaves the brain vulnerable to secondary inflammation at microglial, astrocytic, neuronal, and cognitive levels, and infection amplifies neuroinflammatory cytokine synthesis in humans. Exacerbation of neuroinflammation to produce deleterious outcomes like delirium and accelerated disease progression merits careful investigation in humans.
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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 | 0.000 | 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 | 0.001 |
| 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".