Electroacupuncture on Modifying Inflammatory Levels of Cytokines and Metabolites in Stroke Patients
Bibliographic record
Abstract
Abstract Introduction The use of electroacupuncture (EA) in post-ischemic stroke and rehabilitation has been the subject of numerous studies; however, the effect of EA on cholesterol metabolites has not been thoroughly investigated. The inflammatory response in stroke has been associated with serum cholesterol, low HDL-Cc, and high LDL-Cc levels, and early intervention has been linked to improved post-stroke rehabilitation. This study aimed to assess the impact of EA on early ischemic stroke as a modulator of total cholesterol, HDL-c, and LDL-c in the blood, its anti-inflammatory effect, and its effect on pain and stroke scales in patients in the first few days after the onset of stroke. Data Access Statement The datasets generated during the current study are available from the corresponding author on reasonable request Material and Method A total of 90 patients with acute ischemic stroke and a first-time diagnosis of stroke will be randomized into one of three groups: an EA group, a sham EA group, and a sensory control group. All patients will receive the interventions three times a week for a total of six sessions over two weeks. Outcome measurements will include blood tests for total cholesterol, triglycerides, HDL with HDL-c cholesterol, LDL and LDL-c cholesterol, along with Visual Analog Scale (VAS), National Institutes of Health Stroke Scale (NIHSS), and Barthel Index (BI). Expected Outcome This study will help determine the effect of EA on ischemic stroke recovery, focusing on metabolic changes in patients with early stage stroke. EA treatment might modify risk indices (HDL-c), maintain or control (LDL-c), and generate localized reperfusion of the vascular areas involved in stroke. Discussion This randomized controlled trial will determine the ability of EA to support early stroke ischemic injury and neuro-endothelium damage, which could lead to a faster stroke recovery in stroke scales, and reveal whether the mechanism of EA is associated with a reduced inflammatory process via modulation of the levels of total cholesterol, HDL-c, LDL-c, and triglycerides. The results of this study will be of significant value in the treatment of ischemic stroke and could lead to more effective and personalized stroke rehabilitation therapies. Trial registry registered study protocol on www.clinicaltrial.gov ( NCT05734976 )
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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) | 0.003 | 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".