The Role of Urinary Modulators in the Development of Infectious Kidney Stones
Bibliographic record
Abstract
Introduction: The pathogenesis of infectious kidney stones is poorly understood. Bacteria have been implicated in promoting infectious stones via urease production; however, there is mounting evidence indicating the relationship is more complex. The aim of our study was to characterize suspected biotic and abiotic extrinsic factors that may modulate the formation of infectious stones. Materials and Methods: A high-throughput experimental model with Griffith's artificial urine was used to test a wide variety of urinary modulators and cytoplasmic enzymes present in crude cell-free extracts (CFEs) from bacterial strains to investigate how they impact struvite and calcium (Ca) phosphate crystal production. Crystal formation was evaluated with spectrophotometry and growth curve analysis. Light microscopy and scanning electron microscopy/X-ray diffraction was used for crystal structure and composition identification. Results: The acidic urinary modulators used in this study prevented crystal formation, whereas osteopontin had a significant inhibitory effect. Addition of CFEs from Proteus mirabilis 175A and 177A resulted in Ca phosphate and struvite crystals. Of interest, Klebsiella pneumoniae and Klebsiella oxytoca produced crystals including Ca phosphate and Ca oxalate, respectively. Pseudomonas aeruginosa had no urease production detected and produced Ca phosphate crystals. Discussion: Urinary modulators can have a wide variety of effects on infectious stone formation and the role of pH is important but does not guarantee robust crystal formation. Bacterial strains can produce Ca oxalate, Ca phosphate, and struvite stones with and without urease activity. Conclusion: Various urinary modulators appear to influence the process and are worthy of further evaluation as a potential therapeutic strategy to prevent infection-related urinary stone formation. Stones formed from urinary tract infections may be a result of multiple encoded metabolic pathways and discovering these would improve our understanding of the stone-bacterial relationship.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".