Potential effect of green tea extract for adjuvant treatment of acute ischemic stroke by s100ß upregulation in non-thrombolysis patient
Why this work is in the frame
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Bibliographic record
Abstract
Introduction: In ischemic stroke, the cerebral cortex suffers from hypoxia-ischemia, leading to inflammation and oxidative stress. Green tea extract has an anti-inflammation effect and antioxidant. This study aimed to determine the efficacy of green tea extract for adjuvant treatment of acute ischemic stroke in non-thrombolysis patients. Methods: A double-blind randomised controlled trial was conducted in November 2020-November 2021. The subjects were all acute ischemic stroke patients who presented to the Emergency Room during recruitment, randomised into control (n=13) and intervention groups (n=18); the intervention groups were given green tea extract 350 mg. Treatment was for 30 days. National Institutes of Health Stroke Scale (NIHSS), modified Rankin Scale (mRS), Montreal Cognitive Assessment - Indonesia (MoCAIna), IL-10 and S100ß were analysed. Results: Data were compared with a significance level of p<0.05. The differences in NIHSS from day 0 to 7, day 0 to 14 and day 0 to 30 were statistically significant in the intervention group (p=0.019, p=0.002 and p=0.000, respectively). The mRS score was statistically significant in the intervention group on day 30 (p=0.46). The differences in mRS score from day 0 to 14 and day 0 to 30 were statistically significant (p=0.042 and p=0.001, respectively) The S100ß were statistically significant in day 7 (p=0.006). The difference in S100ß from day 0 to 7 was statistically significant (p=0.001).Conclusions: The green tea extract, through up-regulation S100ß, can improve the clinical outcomes of acute ischemic stroke.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it