Supplementing Yogurt with Probiotic Bifidobacteria to Counter Chronic Kidney Disease
Bibliographic record
Abstract
Chronic kidney disease (CKD) disproportionately affects populations in developing countries. In sub-Saharan Africa, CKD prevalence is high (12–23%) and is associated with cardiovascular manifestations. Uremic toxins, especially p-cresol and p-cresyl sulfate, are associated with the disease. Reducing uremic toxins in the body slows disease progression and improves patient outcomes. Probiotic Bifidobacterium breve HRVD521-US, B. animalis HRVD524-US, B. longum SD-BB536-JP, and B. longum SD-CECT7347-SP internalize p-cresol and improve longevity in vivo. In 2002, Tanzanian communities were taught to produce probiotic yogurt (Fiti®) supplemented with Lacticaseibacillus rhamnosus GR-1. This has expanded to over 100 community producers across the country. To produce yogurt that could reduce the burden of CKD by sequestering uremic toxins, we decided to test the addition of p-cresol-clearing bifidobacterial strains. By repeating the Fiti® production process performed in Tanzanian communities and adding a bifidobacterial strain, we found that they were successfully incorporated into the yogurt without any detrimental effect on sensory properties or viable counts. Three of the four strains significantly reduced p-cresol when added to a simulated colonic environment. In conclusion, this study has shown that Fiti® sachets provided to Tanzanian communities to produce yogurt can be supplemented with strains that can potentially confer additional health benefits.
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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) | 0.001 | 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".