Vagus Nerve Stimulation Modulates Inflammation in Treatment-Resistant Depression Patients: A Pilot Study
Bibliographic record
Abstract
Vagal neurostimulation (VNS) is used for the treatment of epilepsy and major medical-refractory depression. VNS has neuropsychiatric functions and systemic anti-inflammatory activity. The objective of this study is to measure the clinical efficacy and impact of VNS modulation in depressive patients. Six patients with refractory depression were enrolled. Depression symptoms were assessed with the Montgomery–Asberg Depression Rating, and anxiety symptoms with the Hamilton Anxiety Rating Scale. Plasmas were harvested prospectively before the implantation of VNS (baseline) and up to 4 years or more after continuous therapy. Forty soluble molecules were measured in the plasma by multiplex assays. Following VNS, the reduction in the mean depression severity score was 59.9% and the response rate was 87%. Anxiety levels were also greatly reduced. IL-7, CXCL8, CCL2, CCL13, CCL17, CCL22, Flt-1 and VEGFc levels were significantly lowered, whereas bFGF levels were increased (p values ranging from 0.004 to 0.02). This exploratory study is the first to focus on the long-term efficacy of VNS and its consequences on inflammatory biomarkers. VNS may modulate inflammation via an increase in blood–brain barrier integrity and a reduction in inflammatory cell recruitment. This opens the door to new pathways involved in the treatment of refractory depression.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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".