Evaluation of urinary cysteinyl leukotrienes as biomarkers of severity and putative therapeutic targets in COVID-19 patients
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Cysteinyl leukotrienes (CysLT) are potent inflammation-promoting mediators, but remain scarcely explored in COVID-19. We evaluated urinary CysLT (U-CysLT) relationship with disease severity and their usefulness for prognostication in hospitalized COVID-19 patients. The impact on U-CysLT of veno-venous extracorporeal membrane oxygenation (VV-ECMO) and of comorbidities such as hypertension and obesity was also assessed. METHODS: Blood and spot urine were collected in "severe" (n = 26), "critically ill" (n = 17) and "critically ill on VV-ECMO" (n = 17) patients with COVID-19 at days 1-2 (admission), 3-4, 5-8 and weekly thereafter, and in controls (n = 23) at a single time point. U-CysLT were measured by ELISA. Routine markers, prognostic scores and outcomes were also evaluated. RESULTS: U-CysLT did not differ between groups at admission, but significantly increased along hospitalization only in critical groups, being markedly higher in VV-ECMO patients, especially in hypertensives. U-CysLT values during the first week were positively associated with ICU and total hospital length of stay in critical groups and showed acceptable area under curve (AUC) for prediction of 30-day mortality (AUC: 0.734, p = 0.001) among all patients. CONCLUSIONS: U-CysLT increase during hospitalization in critical COVID-19 patients, especially in hypertensives on VV-ECMO. U-CysLT association with severe outcomes suggests their usefulness for prognostication and as therapeutic targets.
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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 | 0.016 | 0.102 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it