Novel Methods of Determining Urinary Calculi Composition: Petrographic Thin Sectioning of Calculi and Nanoscale Flow Cytometry Urinalysis
Bibliographic record
Abstract
Accurate determination of urinary stone composition has significant bearing on understanding pathophysiology, choosing treatment modalities and preventing recurrence. A need exists for improved methods to determine stone composition. Urine of 31 patients with known renal calculi was examined with nanoscale flow cytometry and the calculi collected during surgery subsequently underwent petrographic thin sectioning with polarized and fluorescent microscopy. Fluorescently labeled bisphosphonate probes (Alendronate-fluorescein/Alendronate-Cy5) were developed for nanoscale flow cytometry to enumerate nanocrystals that bound the fluorescent probes. Petrographic sections of stones were also imaged by fluorescent and polarized light microscopy with composition analysis correlated to alendronate +ve nanocrystal counts in corresponding urine samples. Urine samples from patients with Ca(2+) and Mg(2+) based calculi exhibited the highest alendronate +ve nanocrystal counts, ranging from 100-1000 nm in diameter. This novel urine based assay was in agreement with composition determined by petrographic thin sections with Alendronate probes. In some cases, high alendronate +ve nanocrystal counts indicated a Ca(2+) or Mg(2+) composition, as confirmed by petrographic analysis, overturning initial spectrophotometric diagnosis of stone composition. The combination of nanoscale flow cytometry and petrographic thin sections offer an alternative means for determining stone composition. Nanoscale flow cytometry of alendronate +ve nanocrystals alone may provide a high-throughput means of evaluating stone burden.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".