Reversible Inhibition of Calcium Oxalate Monohydrate Growth by an Osteopontin Phosphopeptide
Bibliographic record
Abstract
Calcium oxalate, primarily as calcium oxalate monohydrate (COM), is the primary constituent of most kidney stones. Certain proteins, such as osteopontin (OPN), inhibit stone formation. The complexity of stone formation and the effects of urinary proteins at various stages of the process make it hard to predict the exact physiological roles of these proteins in growth inhibition. The inhibition of crystallization due to adsorbed impurities is usually explained in terms of a model proposed in 1958 by Cabrera and Vermilyea. In this model, impurities adsorb to growth faces and pin growth steps, forcing them to curve, thus impeding their progress via the Gibbs-Thomson effect. To determine the role of OPN in the biomineralization of kidney stones, crystal growth on the {010} face of COM was examined in real time with atomic force microscopy in the presence of a synthetic peptide corresponding to amino acids 65-80 (hereafter referred to as pOPAR) of rat bone OPN. We observed clear changes in the morphology of the growth-step structure and a decrease in step velocity upon addition of pOPAR, which suggest adsorption of inhibitors on the {010} growth hillocks. Experiments in which pOPAR was replaced in the growth cell by a supersaturated solution showed that COM hillocks are able to fully recover to their preinhibited state. Our results suggest that recovery occurs through incorporation of the peptide into the growing crystal, rather than by, e.g., desorption from the growth face. This work provides new insights into the mechanism by which crystal growth is inhibited by adsorbants, with important implications for the design of therapeutic agents for kidney stone disease and other forms of pathological calcification.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 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".