Empagliflozin mitigates endothelial inflammation and attenuates endoplasmic reticulum stress signaling caused by sustained glycocalyx disruption
Bibliographic record
Abstract
Abstract The disruption of the endothelial cell (EC) glycocalyx (GCX) leads to cellular dysfunction promoting inflammation and cardiovascular disease progression. Recent studies have shown that empagliflozin (EMPA; Jardiance), a sodium-glucose cotransporter 2 inhibitor used in the treatment of type 2 diabetes, can improve EC functions impacted by GCX disruption although the exact cellular mechanisms remain to be elucidated. In this study, the effect of EMPA on EC inflammatory response induced by sustained GCX disruption was investigated. Human aortic ECs were cultured under shear (10 dyne/cm2) for 24 h with or without sustained degradation of heparan sulfate (HS). HS degradation increased inflammatory cell adhesion to ECs. EMPA (50 μM) normalized adhesion levels under sustained HS degradation. Protein expressions of eNOS, phospho-eNOS Ser1177 and ICAM-1 remained unchanged between conditions. Transcriptome analysis revealed the induction of the unfolded protein response (UPR) through the increased expression of ATF3, ATF4, DDIT3 (CHOP), EIF2AK3 (PERK), HSPA5 (Grp78), PPP1R15A (GADD34) and TRIB3 which was in part downregulated by EMPA. mRNA and protein expression of thioredoxin interacting protein (TXNIP) was also downregulated by EMPA. Mitigation of oxidative stress with N-Acetyl-l-cysteine resulted in similar reduction in inflammatory cell adhesion compared to EMPA which could indicate a potential mechanism by which EMPA normalized the inflammatory response. In conclusion, this study demonstrated the potential of EMPA to resolve the inflammatory response of ECs caused by sustained GCX disruption while altering UPR signaling under endoplasmic reticulum stress.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".