Repetitive transcranial magnetic stimulation elevates the serum levels of neurotrophic factors and serotonin and its metabolites in patients with ischemic stroke
Bibliographic record
Abstract
Objective Repetitive transcranial magnetic stimulation (rTMS) can effectively treat cognitive impairment in stroke patients; however, its mechanism of action remains unclear. The aim of this study was to investigate whether rTMS improves cognitive function by regulating the levels of brain-derived neurotrophic factor (BDNF), nerve growth factor (NGF), 5-hydroxytryptamine (5-HT), and 5-hydroxyindoleacetic acid (5-HIAA). Methods In a 4-week study, 70 patients with ischemic stroke were randomly assigned to two groups: one received rTMS (n = 35) and the other received sham-stimulation (n = 35) in addition to conventional medication and rehabilitation training. Patients in the rTMS group were treated with rTMS at 10 Hz for 20 min per session. The Montreal Cognitive Assessment (MoCA) and response time in the n-back task were used to assess the severity of the disease. Fasting venous blood was collected in the early morning, both before and after the treatment. The peripheral blood levels of BDNF, NGF, 5-HT, and 5-HIAA were measured using the enzyme-linked immunosorbent assay (ELISA). Results The levels of BDNF and NGF were higher in the rTMS group than in the sham group (p = 0.017, p = 0.008), after the rTMS treatment, and the levels of 5-HT and 5-HIAA were also elevated in the rTMS group (p = 0.049, p = 0.004). The changes in serum 5-HT and 5-HIAA levels after the rTMS treatment correlated with the changes in the MoCA and response time in the n-back task. There was a positive correlation between the serum 5-HT and BDNF levels (r = 0.4034). Conclusion Our results showed that the BDNF, NGF, 5-HT, and 5-HIAA levels were upregulated after the rTMS treatment, which likely contributed to improvements in cognitive function and quality of life in the patients with stroke. Clinical trial registration https://www.chictr.org.cn/showproj.html?proj=216761 , ChiCTR2400082383.
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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) | 0.001 | 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) | 0.001 | 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".