Brain Vitalization Gymnastics Improved Cognitive Function Marked by Increased BDNF, Decreased Serum Interleukin-6 and Decreased S-100β Expression among Elderly in West Denpasar Primary Health Clinic
Bibliographic record
Abstract
BACKGROUND: Brain vitalisation gymnastics (BVG) is a form of physical exercise which attempts to synchronise bodily movements with cognition within the same time frame. AIM: This study aims to prove BVG can improve cognitive function among the elderly. METHODS: The impact of BVG was evaluated as opposed to elderly gymnastics (regarded as a control group) for a 4-week study period. Outcomes measured were improvements of cognitive function assessed by MoCA-Ina questionnaire, as well as the difference in serum levels of BDNF, IL-6, and S100β. An experimental pretest-posttest control design was applied to evaluate BDNF and IL-6 levels, while the post-test only designed to evaluate S100β levels. Parametric data were tested for normality before being proceeded into either parametric (independent student' t) or non-parametric (Mann Whitney) test. RESULTS: BVG significantly improved cognitive function better than elderly gymnastics with MoCA-Ina score of 1.53 ± 1.58 dan 0.11 ± 2.54, respectively (p ≤ 0.047). BVG group also had increased BDNF levels when compared with control (-6020.58 ± 7857.22 dan 0.11 ± 2.54; p = 0.027). Whereas BVG had lower IL-6 levels as opposed to the control group (median pre-test IL-6: 2212, median post-test IL-6: 3197.50; p = 0.004). Meanwhile, S100β levels were found lower among BVG when compared with the control group, although statistically insignificant (p = 0.40). CONCLUSION: BVG programme for 4 weeks improved: (1) brain plasticity as shown by increased serum BDNF and S100β levels (although the latter was statistically not-significant), as well as marked decrease of IL-6 levels, (2) cognitive function as proven by an increase of MoCA-Ina score when compared with elderly gymnastics.
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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) | 0.000 | 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) | 0.002 | 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".