Immunosenescence of microglia and macrophages: impact on the ageing central nervous system
Bibliographic record
Abstract
Ageing of the central nervous system results in a loss of both grey and white matter, leading to cognitive decline. Additional injury to both the grey and white matter is documented in many neurological disorders with ageing, including Alzheimer's disease, traumatic brain and spinal cord injury, stroke, and multiple sclerosis. Accompanying neuronal and glial damage is an inflammatory response consisting of activated macrophages and microglia, innate immune cells demonstrated to be both beneficial and detrimental in neurological repair. This article will propose the following: (i) infiltrating macrophages age differently from central nervous system-intrinsic microglia; (ii) several mechanisms underlie the differential ageing process of these two distinct cell types; and (iii) therapeutic strategies that selectively target these diverse mechanisms may rejuvenate macrophages and microglia for repair in the ageing central nervous system. Most responses of macrophages are diminished with senescence, but activated microglia increase their expression of pro-inflammatory cytokines while diminishing chemotactic and phagocytic activities. The senescence of macrophages and microglia has a negative impact on several neurological diseases, and the mechanisms underlying their age-dependent phenotypic changes vary from extrinsic microenvironmental changes to intrinsic changes in genomic integrity. We discuss the negative effects of age on neurological diseases, examine the response of senescent macrophages and microglia in these conditions, and propose a theoretical framework of therapeutic strategies that target the different mechanisms contributing to the ageing phenotype in these two distinct cell types. Rejuvenation of ageing macrophage/microglia may preserve neurological integrity and promote regeneration in the ageing central nervous system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".