Effects of Sevelamer Hydrochloride on Uremic Toxins Serum Indoxyl Sulfate and P-Cresyl Sulfate in Hemodialysis Patients
Bibliographic record
Abstract
BACKGROUND: Beside the phosphate binding effect, non-calcium non-aluminum phosphate binder, namely sevelamer hydrochloride (SH), has many other effects in dialysis patients. It can absorb many other compounds, decrease low-density lipoprotein cholesterol (LDL-C) level, and attenuate the progression of vascular calcification; it has been reported to have anti-inflammatory effect. However, it is not clear whether it has any effect on uremic toxins, i.e. serum indoxyl sulfate (IS) and p-cresyl sulfate, (PCS) in hemodialysis (HD) patients. This study was carried out to appraise the effect of sevelamer on serum IS and PCS in HD patients. METHODS: Five adult HD patients from a single medical center were enrolled in this study; these patients were treated with 800 mg of sevelamer thrice per day for 3 months; a series of biochemical parameters, serum IS and PCS were monitored concurrently. RESULTS: There was a significant reduction in the mean level of phosphate from 7.20 ± 0.70 mg/dL (mean ± SD) before treatment to 5.40 ± 0.50 mg/dL (mean ± SD) after treatment, total cholesterol from 151.00 ± 37.40 mg/dL (mean ± SD) before treatment to 119.20 ± 29.40 mg/dL (mean ± SD) after treatment, and PCS from 31.30 ± 10.60 mg/L (mean ± SD) before treatment to 19.70 ± 10.50 mg/L (mean ± SD) after treatment. On the contrary, this treatment had no effect on IS. CONCLUSION: A statistically significant reduction of serum phosphate and PCS in HD patients treated with SH suggests that beside the action of lowering serum phosphate, sevelamer may have an important role in the treatment of uremic syndrome by decreasing the uremic toxin.
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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 | 0.001 |
| 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".