Role of urinary cations in the etiology of interstitial cystitis: A multisite study
Bibliographic record
Abstract
Objective To determine whether patients with interstitial cystitis have elevated levels of toxic urinary cations, to identify and quantify these cationic metabolites, and to assess their cytotoxicity. Methods Isolation of cationic fraction was achieved by solid phase extraction using an Oasis MCX cartridge on urine specimens from interstitial cystitis patients and controls. C18 reverse phase high‐performance liquid chromatography was used to profile cationic metabolites, and they were quantified by the area under the peaks and normalized to creatinine. Major cationic fraction peaks were identified by reverse phase high‐performance liquid chromatography and liquid chromatography–mass spectrometry. HTB‐4 urothelial cells were used to determine the cytotoxicity of cationic fraction and of individual metabolites. Results The reverse phase high‐performance liquid chromatography analysis was carried out on cationic fraction metabolites isolated from urine samples of 70 interstitial cystitis patients and 34 controls. The mean for controls versus patients was 3.84 (standard error of the mean 0.20) versus 6.71 (0.37) mAU*min/µg creatinine, respectively (P = 0.0001). The cationic fraction cytotoxicity normalized to creatinine for controls versus patients in mean percentage was −7.79% (standard error of the mean 3.32%) versus 20.03% (standard error of the mean 2.75%; P < 0.0005). The major toxic cations were 1‐methyladenosine, 1‐methylguanine, N2,N2‐dimethylguanosine and L‐tryptophan. Conclusions These data confirm significant elevation of toxic cations in the urine of interstitial cystitis patients. These toxic cations likely represent a primary cause of interstitial cystitis, as they can injure the bladder mucus and initiate an epithelial leak.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".