Role of Endothelial Glycocalyx Degradation Markers in the Pathogenesis of Sepsis-Induced Myocardial Dysfunction: A Translational Investigation
Bibliographic record
Abstract
Background: Sepsis-induced myocardial dysfunction (SIMD) is a critical complication in sepsis that contributes to high mortality rates. Understanding its pathogenesis, particularly the role of endothelial glycocalyx degradation, is crucial for developing targeted therapies. Objective: To investigate the association between endothelial glycocalyx degradation markers and the pathogenesis of SIMD, evaluating their potential as biomarkers for diagnosis and prognosis. Methods: A total of 178 sepsis patients (aged 18–85) were enrolled at New York University Grossman School of Medicine between June 2020 and December 2022. Blood samples were collected at baseline and after 24, 48, and 72 hours. Glycocalyx degradation markers (syndecan-1, heparan sulfate, hyaluronic acid) were measured using ELISA. Myocardial dysfunction was assessed by echocardiography and serum troponin levels. Statistical analysis was performed using SPSS version 26.0, with paired t-tests and Pearson’s correlation. Results: Elevated glycocalyx degradation markers correlated significantly with the severity of SIMD. Syndecan-1 levels increased from 300 ± 120 ng/mL at baseline to 650 ± 150 ng/mL (p = 0.03) at 72 hours. Heparan sulfate levels rose from 85 ± 15 ng/mL to 215 ± 50 ng/mL (p = 0.01). Hyaluronic acid increased from 0.5 ± 0.2 µg/mL to 1.5 ± 0.5 µg/mL (p = 0.005). The Pearson correlation between syndecan-1 and troponin levels was 0.85 (p < 0.01). The sensitivity and specificity of syndecan-1 for predicting myocardial dysfunction were 87% and 92%, respectively. Conclusion: Endothelial glycocalyx degradation markers are significantly associated with SIMD and can serve as reliable biomarkers for early diagnosis and prognosis in sepsis.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".