Citrate Modulates Calcium Oxalate Crystal Growth by Face-Specific Interactions
Bibliographic record
Abstract
Because of its ability to inhibit the growth of calcium oxalate monohydrate (COM) crystals, citrate plays an important role in preventing the formation of kidney stones. To determine the mechanism of inhibition, we studied the citrate-COM interaction using a combination of microscopic and simulation techniques. Using scanning confocal interference microscopy, we found that addition of citrate preferentially inhibits crystal growth in <100> and, to a lesser extent, <001> directions, suggesting that citrate adsorbs to the faces of COM in the order {100} > {121} > {010}. Scanning electron microscopy showed that the resulting crystals are plate shaped, with large {100} faces and rounded ends. Molecular-dynamics simulations predicted, however, that citrate interacts with the faces of COM in a different order, i.e. {100} > {010} > {121}. Our simulations showed that citrate molecules align with the rows of Ca²⁺ ions on the {010} face but do not form close contacts, presumably because of electrostatic repulsion by the carboxylate groups that project from the Ca²⁺-rich plane. We propose that this weak interaction is responsible for citrate's limited inhibition of COM growth in <010> directions. Overall, these findings indicate that electrostatic interactions with the Ca²⁺-rich faces of COM crystals are responsible for the growth-modulating properties of citrate.
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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 | 0.000 | 0.000 |
| 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".