The Effect of Aerobic Exercise on Renal Function and Metabolic Syndrome in Kidney Transplant Athletes
Bibliographic record
Abstract
Background & Objectives: The relationship between metabolic disorders and renal functions in kidney transplant recipients has been reported. Considering the identification of many advantages of regular physical activities in maintaining health, for chronic kidney patients, the researchers tried to study the effect of aerobic activities on renal function and metabolic syndrome in kidney transplant athletes. Materials & Methods: Twenty female kidney transplant athletes were randomly selected and divided into two equal groups of experimental (mean of age =24.5±2.7 years, height=161±4.2 cm and weight= 57.9±1.8 kg) and control (mean of age =24.9±2.3 years, height=162±2.4 cm and weight= 59.5±4.02 kg). The experimental group carried out an exercise for eight weeks (three sessions per week). At the end of eighth week, the renal function was assessed based on glomerular filtration rate and metabolic syndrome indices. Paired-samples t-test was used to compare the data before and after physical exercise and independent-samples t-test was used to compare the two groups with the significance level of p< 0.05. The eight weeks of aerobic exercise did not have any significant impact on lipid profile levels, while it caused a significant decrease in glomerular filtration rate and fasting blood sugar in kidney transplant athletes in the experimental group after exercise. Conclusion: The results indicated that physical exercise can be considered as a good way of changing glomerular filtration rate and controling fasting blood sugar in athletes undergoing kidney transplant
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".