Mimicking the Biomolecular Control of Calcium Oxalate Monohydrate Crystal Growth: Effect of Contiguous Glutamic Acids
Bibliographic record
Abstract
Scanning confocal interference microscopy (SCIM) and molecular dynamics (MD) simulations were used to investigate the adsorption of the synthetic polypeptide poly(l-glutamic acid) (poly-glu) to calcium oxalate monohydrate (COM) crystals and its effect on COM formation. At low concentrations (1 μg/mL), poly-glu inhibits growth most effectively in ⟨001⟩ directions, indicating strong interactions of the polypeptide with {121} crystal faces. Growth in <010> directions was inhibited only marginally by 1 μg/mL poly-glu, while growth in <100> directions did not appear to be affected. This suggests that, at low concentrations, poly-glu inhibits lattice-ion addition to the faces of COM in the order {121} > {010} ≥ {100}. At high concentrations (6 μg/mL), poly-glu resulted in the formation of dumbbell-shaped crystals featuring concave troughs on the {100} faces. The effects on crystal growth indicate that, at high concentrations, poly-glu interacts with the faces of COM in the order {100} > {121} > {010}. This mirrors MD simulations, which predicted that poly-glu will adsorb to a {100} terrace plane (most calcium-rich) in preference to a {121} (oblique) riser plane but will adsorb to {121} riser plane in preference to an {010} terrace plane (least calcium-rich). The effects of different poly-glu concentration on COM growth (1-6 μg/mL) may be due to variations between the faces in terms of growth mechanism and/or (nano)roughness, which can affect surface energy. In addition, 1 μg/mL might not be adequate to reach the critical concentration for poly-glu to significantly pin step movement on {100} and {010} faces. Understanding the mechanisms involved in these processes is essential for the development of agents to reduce recurrence of kidney stone disease.
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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) | 0.000 | 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".