Determination of urinary prostaglandin E2 as a potential biomarker of ureteral stent associated inflammation
Bibliographic record
Abstract
Ureteral stents are the most widely used surgical implant in urology. However, they may cause adverse effects to patients, including pain, discomfort, and inflammation. In this work, the inflammatory effect of stent placement and the associated elevation of cyclooxygenase-2 (COX-2) expression were observed. Furthermore, a capillary electrophoresis mass spectrometry (CE-MS) based approach was subsequently developed to quantify urinary prostaglandin E₂ (PGE₂), a COX-2 metabolite known to contribute to inflammatory renal diseases, to further interrogate the role of this pathway. Urine samples were cleaned and preconcentrated by solid-phase extraction (SPE), and an on-line sample stacking method was used for the enrichment of analytes. The accuracy, precision, and specificity of this method were validated. Standard addition methods were performed to assess the reliability of using deuterated internal standards (IS) in compensating the remaining matrix effect after SPE as well as the detector fluctuation. Through the analysis of 32 pig urine samples, a statistically significant increase of PGE₂ was observed in the stented group compared to the unstented (P = 0.01) and the recovered (P = 0.004) groups. This work determined that stent placement may contribute to COX-2-dependent inflammation and developed a reliable CE-MS based methodology to quantify PGE₂ in stented individuals that may further understand the biology of stent-associated inflammation and inform urologic patient management.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".