Protective effects of purslane seed (Portulaca Oleracea L.) on plasma levels of Cystatin C, Cathepsin S, and Creatinine in women with type 2 Diabetes
Bibliographic record
Abstract
Introduction: Diabetes is a chronic metabolic disease which is associated with the inflammation of cardiovascular system and kidney. Studies have shown that medicinal plants could be effective in reducing inflammation; however, the effectiveness of purslane (Portulaca oleracea) on inflammation is not well defined. Thus, this study attempted to investigate the effect of Portulaca oleracea seed consumption on plasma levels of cystatin C, cathepsin S, and creatinine in women with type 2 diabetes. Methods: In this quasi-experimental study, 14 women with type 2 diabetes were randomly divided into two equal groups of intervention and control (n=7). The subjects received Portulaca oleracea seed 2.5 g at lunch and 5 g at dinner (totally 7.5 g) per day for 8 weeks. Blood was collected before and 48 hours after the last intervention. Data were analyzed with paired and independent t-tests, and P<0.05 was considered significant. Results: Levels of cystatin C, cathepsin S, creatinine, and lipid profile decreased significantly in the intervention group after 8 weeks (P<0.05). There was also a significant difference between the intervention and control groups in levels of cystatin C and cathepsin S. Conclusion: Changes in biochemical markers showed that Portulaca oleracea seed could improve the levels of cardiovascular and kidney damage biomarkers and lipid profile in diabetic patients. However, further research is needed for more accurate conclusions.
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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".