Preplaque (‘Preclinical’) Aβ-Induced Inflammation and Nerve Growth Factor Deregulation in Transgenic Models of Alzheimer’s Disease-Like Amyloid Pathology
Bibliographic record
Abstract
BACKGROUND: Alzheimer's disease (AD) neuropathology likely begins decades before clinical symptoms are manifested. Investigations on the early stages of the amyloid pathology are crucial for the discovery of diagnostic biomarkers or new therapeutic targets. Our transgenic (tg) animal models are most suitable to study early AD pathological events, as the pathology evolves in a well-staged manner, starting with intracellular Aβ accumulation and ending with plaque deposition. OBJECTIVE: To determine the occurrence of key inflammatory markers and to look for signs of nerve growth factor (NGF) dysmetabolism at preplaque and postplaque stages in tg models of AD-like amyloid pathology and in human AD brains. METHODS: We used our own tg lines (mice and rat), high-quality human brain material and applied neurochemical and immunohistochemical experimental approaches. RESULTS: In both tg models, we observed an intracellular accumulation of oligomeric Aβ in cortical and hippocampal pyramidal neurons. This coincided with an upregulation of key inflammatory markers (iNOS, MHCII, COX-2) and a recruitment of microglia towards Aβ-burdened neurons. Using human AD brains, we showed alterations in the NGF metabolic pathway, which were mirrored in our tg rat model at early and late stages of amyloid plaque generation. CONCLUSION: A proinflammatory process and, consequently, the deregulation of the NGF metabolic pathway could be amongst the earliest pathological events in the progression of 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".