The Inflammation-Induced Dysregulation of Reelin Homeostasis Hypothesis of Alzheimer’s Disease
Bibliographic record
Abstract
Alzheimer's disease (AD) accounts for most dementia cases, but we lack a complete understanding of the mechanisms responsible for the core pathology associated with the disease (e.g., amyloid plaque and neurofibrillary tangles). Inflammation has been identified as a key contributor of AD pathology, with recent evidence pointing towards Reelin dysregulation as being associated with inflammation. Here we describe Reelin signaling and outline existing research involving Reelin signaling in AD and inflammation. Research is described pertaining to the inflammatory and immunological functions of Reelin before we propose a mechanism through which inflammation renders Reelin susceptible to dysregulation resulting in the induction and exacerbation of AD pathology. Based on this hypothesis, it is predicted that disorders of both inflammation (including peripheral inflammation and neuroinflammation) and Reelin dysregulation (including disorders associated with upregulated Reelin expression and disorders of Reelin downregulation) have elevated risk of developing AD. We conclude with a description of AD risk in various disorders involving Reelin dysregulation and inflammation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".