Early intraneuronal amyloid triggers neuron-derived inflammatory signaling in APP transgenic rats and human brain
Bibliographic record
Abstract
Chronic inflammation during Alzheimer's disease (AD) is most often attributed to sustained microglial activation in response to amyloid-β (Aβ) plaque deposits and cell death. However, cytokine release and microgliosis are consistently observed in AD transgenic animal models devoid of such pathologies, bringing into question the underlying processes that may be at play during the earliest AD-related immune response. We propose that this plaque-independent inflammatory reaction originates from neurons burdened with increasing levels of soluble and oligomeric Aβ, which are known to be the most toxic amyloid species within the brain. Laser microdissected neurons extracted from preplaque amyloid precursor protein (APP) transgenic rats were found to produce a variety of potent immune factors, both at the transcript and protein levels. Neuron-derived cytokines correlated with the extent of microglial activation and mobilization, even in the absence of extracellular plaques and cell death. Importantly, we identified an inflammatory profile unique to Aβ-burdened neurons, since neighboring glial cells did not express similar molecules. Moreover, we demonstrate within disease-vulnerable regions of the human brain that a neuron-specific inflammatory response may precede insoluble Aβ plaque and tau tangle formation. Thus, we reveal the Aβ-burdened neuron as a primary proinflammatory agent, implicating the intraneuronal accumulation of Aβ as a significant immunological component in the AD pathogenesis.
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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".