The Effect of Channa striata Extract on Serum Albumin and High Sensitive C-Reactive Protein in End-Stage Renal Disease Patients: A Randomized Controlled Trial
Bibliographic record
Abstract
Background: Albumin is a marker of nutritioinal inflammation and mortality. Chronic inflammation, as indicated by the concentration of a proinflammatory cytokine, high sensitivity C-reactive protein (hs-CRP) was reported to be high in end-stage renal disease (ESRD) patients. Channa striata (CS) contains high protein that can increase albumin levels and has anti-inflammatory effects. This study was conducted to determine the effect of CS extract on serum albumin and hs-CRP on ESRD patients. Methods: This study is a randomized, double blind, placebo-controlled study in patients with ESRD on hemodialysis (HD) and continuous ambulatory peritoneal dialysis (CAPD). Subjects were randomized to either a CS or a placebo group and were given a three times daily dosage of 500 mg of CS extract or 500 mg maltodextrin, respectively for 21 days. Serum albumin and hs-CRP were measured at the baseline, and at the end of the study. Result: Forty subjects were randomized into the study with 20 in the Channa striata group and 20 in the placebo group, with HD and CAPD patient evenly distributed among the group. Significant increase in serum albumin levels (p<0,001) and significant decrease of hs-CRP (p<0,001) were observed in the treatment group compared to control group at the end of the study. At the end of the study, there was no significant difference between serum albumin, hs-CRP, and their gradient between HD and CAPD patients in the intervention group. Conclusion: CS extract results in higher serum albumin and lower hs-CRP levels compared to placebo in our population.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".