Label-Free Mapping of Osteopontin Adsorption to Calcium Oxalate Monohydrate Crystals by Tip-Enhanced Raman Spectroscopy
Bibliographic record
Abstract
In the ectopic biomineralization of calcium oxalate kidney stones, the competition between calcium oxalate monohydrate (COM) formation and its inhibition by the phosphoprotein osteopontin (OPN) plays a key role in COM stone-forming processes. To get more insights into these processes, tip-enhanced Raman spectroscopy (TERS) was used to provide surface-specific information about the adsorption of OPN to faces of COM crystals. In TERS, the surface plasmon resonance of a metallic AFM tip is locally excited when the tip is placed in the optical near-field of a laser focused on the crystal surface. Excitation of this localized surface plasmon resonance allows the enhancement of the Raman signal as well as the improvement of the spatial resolution beyond the diffraction limit of the light. As TERS works label free and noninvasively, it is an excellent technique to study the distribution of adsorbed proteins on crystal faces at the submicrometer scale. In the present work, we generated Raman intensity maps indicating high spatial resolution and a distinct variation in relative peak intensities. The collected TERS spectra show that the OPN preferentially adsorbs to edges and faces at the ends of COM crystals (order: {100}/{121} edge > {100} face > {100}/{010} edge ≈ {121}/{010} edge > {010} face) providing also relevant information on the inhibition of crystal growth. This study demonstrates that TERS is an excellent technique for detailed investigations of biomolecules adsorbed, layered, or assembled to a large variety of surfaces and interfaces.
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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 | 0.001 | 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".