Characterization of hyaluronic acid interaction with calcium oxalate crystals: implication of crystals faces, pH and citrate
Bibliographic record
Abstract
Interaction between hyaluronic acid (HA) present at the surface of tubular epithelial cells and calcium oxalate monohydrate (COM) crystals is thought to play an important role in kidney stone formation. AFM-based force spectroscopy, where HA is covalently attached to AFM-probes, was used to quantify the interaction between HA and the surfaces of COM crystals. The work of adhesion of the HA-probe as well as the rupture force of single HA molecules were quantified in order to understand the molecular regulation of HA binding to COM crystals. Our results reveal that HA adsorbs to the crystal surface in physiological conditions. We also observed increased adhesion when the pH is lowered to a value that increases the risk of kidney stone formation. HA adhesion to the COM crystal surface can be suppressed by citrate, a physiological inhibitor of stone retention currently used in the treatment and prevention of kidney stone formation. Interestingly, we also observed preferential binding of HA onto the [100] face versus the [010] face, suggesting a major contribution of the [100] faces in the crystal retention process at the surface of tubular epithelial cells and the promotion of stone formation. Our results clearly establish a direct role for the glycosaminoglycan HA present at the surface of kidney tubular epithelium in the process of COM crystal retention.
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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) | 0.001 | 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".