Urinary Expression of Novel Tissue Markers of Kidney Injury After Ureteroscopy, Shockwave Lithotripsy, and in Normal Healthy Controls
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Shockwave lithotripsy (SWL) and ureteroscopy (URS) are minimally invasive treatment alternatives for kidney stones. Although less invasive, SWL subjects the renal parenchyma to a high level of energy and the potential to cause renal injury. The ability to detect renal injury post-SWL in a reliable and noninvasive way would be clinically beneficial. Kidney injury molecule 1 (KIM-1) and N-acetyl-β-D-glucosaminidase (NAG) are two proteins secreted by the kidney into the urine and have been found to be sensitive markers of acute kidney injury in transplant patients. The aim of this work was to measure urinary levels of KIM-1 and NAG in patients with kidney stone who were treated by SWL or URS and in nonstone volunteers. PATIENTS AND METHODS: Patients with kidney stones who were treated by SWL (n = 50) or URS (n = 10) were recruited. Voided urine samples were collected before and 2 to 3 hours after URS and SWL. In addition, further urinary specimens were collected 2 days and 2 weeks post-SWL treatment. Voided urine samples from healthy volunteers were also collected. RESULTS: Mean KIM-1 values were increased in patients with kidney stones when compared with volunteers. KIM-1 and NAG levels significantly increased post-SWL and returned to baseline within 2 weeks post-SWL. Poor kidney function was significantly associated with increased biomarker activity both in baseline and post-SWL measurements. There was no significant change in urinary KIM-1 and NAG concentrations before and after URS. CONCLUSIONS: Kim-1 and NAG levels significantly increased post-SWL treatment suggesting a potential role for these urinary markers in identifying patients at higher risk of tissue injury.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 teacher head, 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".