High-Throughput in vitro Gel-Based Plate Assay to Screen for Calcium Oxalate Stone Inhibitors
Bibliographic record
Abstract
OBJECTIVE: Kidney stones are a common medical condition that is increasing in prevalence worldwide. Approximately, ∼80% of urinary calculi are composed of calcium oxalate (CaOx). There is a growing interest toward identifying therapeutic compounds that can inhibit the formation of CaOx crystals. However, some chemicals (e.g., antibiotics and bacterial metabolites) may directly promote crystallization. Current knowledge is limited regarding crystal promoters and inhibitors. Thus, we have developed an in vitro gel-based diffusion model to screen for substances that directly influence CaOx crystal formation. MATERIALS AND METHODS: We used double diffusion of sodium oxalate and calcium chloride-loaded paper disks along an agar medium to facilitate the controlled formation of monohydrate and dihydrate CaOx crystals. A third disk was used for the perpendicular diffusion of a test substance to assess its influence on CaOx crystal formation. RESULTS: We confirmed that citrates and magnesium are effective inhibitors of CaOx crystals. We also demonstrated that 2 strains of uropathogenic Escherichia coli are able to promote crystal formation. While the other tested uropathogens and most antibiotics did not change crystal formation, ampicillin was able to reduce crystallization. CONCLUSION: We have developed an inexpensive and high-throughput model to evaluate substances that influence CaOx crystallization.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".