Microglial Inflammatory-Metabolic Pathways and Their Potential Therapeutic Implication in Major Depressive Disorder
Bibliographic record
Abstract
Increasing evidence supports the notion that neuroinflammation plays a critical role in the etiology of major depressive disorder (MDD), at least in a subset of patients. By virtue of their capacity to transform into reactive states in response to inflammatory insults, microglia, the brain's resident immune cells, play a pivotal role in the induction of neuroinflammation. Experimental studies have demonstrated the ability of microglia to recognize pathogens or damaged cells, leading to the activation of a cytotoxic response that exacerbates damage to brain cells. However, microglia display a wide range of responses to injury and may also promote resolution stages of inflammation and tissue regeneration. MDD has been associated with chronic priming of microglia. Recent studies suggest that altered microglial morphology and function, caused either by intense inflammatory activation or by senescence, may contribute to depression and associated impairments in neuroplasticity. In this context, modifying microglia phenotype by tuning inflammatory pathways might have important translational relevance to harness neuroinflammation in MDD. Interestingly, it was recently shown that different microglial phenotypes are associated with distinct metabolic pathways and analysis of the underlying molecular mechanisms points to an instrumental role for energy metabolism in shaping microglial functions. Here, we review various canonical pro-inflammatory, anti-inflammatory and metabolic pathways in microglia that may provide new therapeutic opportunities to control neuroinflammation in brain disorders, with a strong focus on MDD.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".