Shedding Light on the Dark Side of the Microglia
Bibliographic record
Abstract
Microglia, the resident immune cells of the central nervous system, are not a homogeneous population; their morphology, molecular profile, and even their ultrastructure greatly vary from one cell to another. Recent advances in the field of neuroimmunology have helped to demystify the enigma that currently surrounds microglial heterogeneity. Indeed, numerous microglial subtypes have been discovered such as the disease-associated microglia, neurodegenerative phenotype, and Cd11c-positive developmental population. Another subtype is the dark microglia (DM), a population defined by its ultrastructural changes associated with cellular stress. Since their first characterization using transmission electron microscopy, they have been identified in numerous disease conditions, from mouse models of Alzheimer’s disease, schizophrenia, fractalkine signaling deficiency to chronic stress, just to name a few. A recent study also identified the presence of cells with a similar ultrastructure to the DM in postmortem brain samples from schizophrenic patients, underlining the importance of understanding the function of these cells. In this minireview, we aim to summarize the current knowledge on the DM, from their initial ultrastructural characterization to their documentation in various pathological contexts across multiple species. We will also highlight the current limitations surrounding the study of these cells and the future that awaits the DM.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".