Human <i>in vivo</i> evidence of reduced astrocyte activation and neuroinflammation in patients with treatment‐resistant depression following electroconvulsive therapy
Bibliographic record
Abstract
AIM: The current study aimed to investigate the neuroinflammatory hypothesis of depression and the potential anti-inflammatory effect of electroconvulsive therapy (ECT) in vivo, utilizing astrocyte-derived extracellular vesicles (ADEVs) isolated from plasma. METHODS: A total of 40 patients with treatment-resistant depression (TRD) and 35 matched healthy controls were recruited at baseline, and 34 patients with TRD completed the post-ECT visits. Blood samples were collected at baseline and post-ECT. Plasma ADEVs were isolated and confirmed, and the concentrations of two astrocyte markers (glial fibrillary acidic protein [GFAP] and S100β), an extracellular vesicle marker cluster of differentiation 81 (CD81), and nine inflammatory markers in ADEVs were measured as main analyses. In addition, correlation analysis was conducted between clinical features and ADEV protein levels as exploratory analysis. RESULTS: At baseline, the TRD group exhibited significantly higher levels of two astrocyte markers GFAP and S100β, as well as CD81 compared with the healthy controls. Inflammatory markers interferon γ (IFN-γ), interleukin (IL) 1β, IL-4, IL-6, tumor necrosis factor α, IL-10, and IL-17A were also significantly higher in the TRD group. After ECT, there was a significant reduction in the levels of GFAP, S100β, and CD81, along with a significant decrease in the levels of IFN-γ and IL-4. Furthermore, higher levels of GFAP, S100β, CD81, and inflammatory cytokines were associated with more severe depressive symptoms and poorer cognitive function. CONCLUSION: This study provides direct insight supporting the astrocyte activation and neuroinflammatory hypothesis of depression using ADEVs. ECT may exert an anti-inflammatory effect through inhibition of such activation of astrocytes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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 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".