Oxalate-Degrading Bacillus subtilis Mitigates Urolithiasis in a Drosophila melanogaster Model
Bibliographic record
Abstract
Kidney stone disease is a morbid condition that is increasing in prevalence, with few nonsurgical treatment options. The majority of stones are composed of calcium oxalate. Unlike humans, some microbes can break down oxalate, suggesting that microbial therapeutics may provide a novel treatment for kidney stone patients. This study demonstrated that Bacillus subtilis 168 (BS168) decreased stone burden, improved health, and complemented the microbiota in a Drosophila melanogaster urolithiasis model, while not exacerbating calcium oxalate aggregation or adhesion to renal cells in vitro . These results identify this bacterium as a candidate for ameliorating stone formation; given that other strains of B. subtilis are components of fermented foods and are used as probiotics for digestive health, strain 168 warrants testing in humans. With the severe burden that recurrent kidney stone disease imposes on patients and the health care system, this microbial therapeutic approach could provide an inexpensive therapeutic adjunct.
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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 | 0.001 | 0.001 |
| 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".