Potential effect of green tea extract for adjuvant treatment of acute ischemic stroke by s100ß upregulation in non-thrombolysis patient
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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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".