Decoding Mast Cell-Microglia Communication in Neurodegenerative Diseases
Bibliographic record
Abstract
Microglia, resident immune cells of the central nervous system (CNS), play a pivotal role in immune surveillance and maintenance of neuronal health. Mast cells are also important resident immune cells of the CNS but they are underappreciated and understudied. Both microglia and mast cells are endowed with an array of signaling receptors that recognize microbes and cellular damage. As cellular sensors and effectors in the CNS, they respond to many CNS perturbations and have been implicated in neuroinflammation and neurodegeneration. Mast cells contain numerous secretory granules packaged with a plethora of readily available and newly synthesized compounds known as 'mast cell mediators'. Mast cells act as 'first responders' to a pathogenic stimuli and respond by degranulation and releasing these mediators into the extracellular milieu. They alert other glial cells, including microglia to initiate neuroinflammatory processes that culminate in the resolution of injury. However, failure to resolve the pathogenic process can lead to persistent activation, release of pro-inflammatory mediators and amplification of neuroinflammatory responses, in turn, resulting in neuronal dysfunction and demise. This review discusses the current understanding of the molecular conversation between mast cells and microglia in orchestrating immune responses during two of the most prevalent neurodegenerative diseases, namely Alzheimer's disease and Parkinson's disease. Here we also survey the potential emerging therapeutic approaches targeting common pathways in mast cells and microglia to extinguish the fire of inflammation.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".