Role of Immunity and Inflammation in the Pathophysiology of Neurodegenerative Diseases
Bibliographic record
Abstract
Neurodegenerative diseases are the result of progressive loss of neurons and axons in the central nervous system (CNS), which can lead to cognition and motor dysfunction. It is well known that CNS inflammation and immune activation play a major role in the pathophysiology of neurodegenerative diseases. Although the blood-brain barrier (BBB) is able to protect the CNS from immune activation, it becomes more permeable during inflammation, which renders the brain vulnerable to infections. A better understanding of the interaction between inflammatory mediators, such as cytokines, and the activated immune response, including astrocytes and microglia, is critical for the development of new therapeutic strategies for neurodegenerative diseases. This review first describes the role of innate immune activation in neurodegenerative diseases and illustrates the factors that contribute to the communication between the CNS and the immune system. A closer look is given at the role of the BBB in inflammation and immunity, as well as at the animal models used to study inflammation in neurodegenerative diseases. Finally, this review outlines the key pathways and biological mechanisms involved in CNS diseases, with a particular focus on multiple sclerosis (MS), Parkinson's disease (PD), and Alzheimer's disease (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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".