The anti-inflammatory effect of resistance training in hypertensive women: the role of purinergic signaling
Bibliographic record
Abstract
BACKGROUND AND METHODS: Essential arterial hypertension triggers a chronic inflammatory process that seems to be linked to purinergic signaling. Physical exercise exhibit anti-inflammatory properties and is able to modulates purinergic system. The aim of this study was to evaluate the effect of 6 months of resistance training on inflammatory markers, purinergic system components, hemodynamic and anthropometric parameters in hypertensive woman. METHODS: A total of 31 hypertensive group and 28 normotensive (control group) middle-aged sedentary women were submitted to 6 months of resistance training. All measurements and blood collection were carried out before (pretest), after 3 months and after 6 months (posttest) of training. Purinergic enzymes [nucleoside triphosphate diphosphohydrolase (NTPDase) and adenosine deaminase] were assessed in lymphocytes; IL-6, IL-10, ATP and C-reactive protein levels were measured in serum. RESULTS: Six months of resistance training was able to significantly reduce blood pressure (BP), IL-6, C-reactive protein, ATP levels as well as NTPDase and adenosine deaminase activities in hypertensive group. Physical training was also able to increase IL-10 levels in hypertensive group. A positive correlation was found between BP, enzyme activities and levels of ATP and IL-6. A negative correlation was found between BP and IL-10. Positive correlation was found between NTPDase and IL-6 levels (P < 0.05) as well as ATP levels and IL-6 levels. CONCLUSION: Our findings demonstrated the relationship between purinergic signaling and inflammation in hypertension and suggests that resistance training serve as tool to reduce inflammation in hypertensive woman by modulating purinergic system.
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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".