Exercise and taurine in inflammation, cognition, and peripheral markers of blood-brain barrier integrity in older women
Bibliographic record
Abstract
Immunosenescence contribute to increase the blood-brain barrier (BBB) permeability, leading cognitive impairment and neurodegeneration. Thus, we investigated the anti-inflammatory effect of exercise and taurine supplementation on peripheral markers of BBB, inflammation, and cognition of elderly women. Forty-eight elderly women (age, 83.58 ± 6.9 years) participated in the study, and were allocated into combined exercise training (CET: n = 13), taurine supplementation (TAU: n = 12), exercise training associated with taurine (CET+TAU: n = 11), or control (CG: n = 12) groups. Exercise was applied twice a week (multi-modal exercise). Taurine ingestion was 1.5 g., once a day. Participants were evaluated before and after 14-weeks of intervention. Plasma levels of interleukin (IL)-1β, IL-1ra, IL-6, IL-10, IL-17, tumor necrosis factor alpha (TNF-α), and serum concentration of S100β and neuron specific enolase (NSE) were determined. The mini mental state examination (MMSE) was also applied. Concentrations of S100β were maintained in all intervention groups, while a subtle increase in the CG was found. NSE levels increased only in TAU group (p < 0.05). CET reduced TNF-α, IL-6, and IL-1β/IL-1ra, IL-6/IL10, and TNF-α/IL-10 ratios (p < 0.05). TAU decreased the IL-1β/IL-1ra ratio (p < 0.05). MMSE score increased only in the CET+TAU group (p < 0.05). Multiple regression analysis showed that there was a trend for changes in IL-1β and the Charlson Comorbidity Index to be independently associated with changes in S100β. Exercise and taurine decreased inflammation, and maintained the BBB integrity in elderly women. Exercise emerged as an important tool to improve brain health even when started at advanced ages.
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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) | 0.001 | 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".