Implication of cerebral astrocytes in major depression: A review of fine neuroanatomical evidence in humans
Bibliographic record
Abstract
Postmortem investigations have implicated astrocytes in many neurological and psychiatric conditions. Multiple brain regions from individuals with major depressive disorder (MDD) have lower expression levels of astrocyte markers and lower densities of astrocytes labeled for these markers, suggesting a loss of astrocytes in this mental illness. This paper reviews the general properties of human astrocytes, the methods to study them, and the postmortem evidence for astrocyte pathology in MDD. When comparing astrocyte density and morphometry studies, astrocytes are more abundant and smaller in human subcortical than cortical brain regions, and immunohistochemical labeling for the astrocyte markers glial fibrillary acidic protein (GFAP) and vimentin (VIM) reveals fewer than 15% of all astrocytes that are present in cortical and subcortical regions, as revealed using other staining techniques. By combining astrocyte densities and morphometry, a model was made to illustrate that domain organization is mostly limited to GFAP-IR astrocytes. Using these markers and others, alterations of astrocyte densities appear more widespread than those for astrocyte morphologies throughout the brain of individuals having died with MDD. This review suggests how reduced astrocyte densities may relate to the association of depressive episodes in MDD with elevated S100 beta (S100B) cerebrospinal fluid serum levels. Finally, a potassium imbalance theory is proposed that integrates the reduced astrocyte densities generated from postmortem studies with a hypothesis for the antidepressant effects of ketamine generated from rodent studies.
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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 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| 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".