Serum angioneurin levels following electroconvulsive therapy for mood disorders
Bibliographic record
Abstract
OBJECTIVES: The efficacy of electroconvulsive therapy (ECT) in treating mood disorders (MDs) is hypothesized to be mediated by the induction of neurotrophic factors (denoted "angioneurins") that trigger neuronal plasticity. This study aimed to assess the effects of ECT on serum angioneurin levels in patients with MD. METHODS: A total of 110 patients with MDs including 30 with unipolar depression, 25 with bipolar depression (BD), 55 with bipolar mania (BM), and 50 healthy controls were included in the study. Patients were subdivided into two groups: those who received ECT + medication (12 ECT sessions) and those who received only medication (no-ECT). Depressive and manic symptom assessments and measurements of vascular endothelial growth factor (VEGF), fibroblast growth factor-2, nerve growth factor (NGF), and insulin-like growth factor-1 levels in blood samples were performed at baseline and week 8. RESULTS: Patients in the ECT group, specifically those with BD and BM, had significantly increased levels of VEGF compared to their baseline VEGF levels (p = 0.002). No significant changes in angioneurin levels were observed in the no-ECT group. Serum NGF levels were significantly associated with a reduction in depressive symptoms. Angioneurin levels were not associated with manic symptom reduction. CONCLUSIONS: This study hints that ECT may increase VEGF levels with angiogenic mechanisms that amplify NGF signaling to promote neurogenesis. It may also contribute to changes in brain function and emotional regulation. However, further animal experiments and clinical validation are needed.
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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 | 0.001 |
| 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".