Effects of circulating extracellular microvesicles from spinal cord-injured adults on endothelial cell function
Bibliographic record
Abstract
People with spinal cord injury (SCI) have three- to four-fold greater risk of cardiovascular disease (CVD) compared with those without SCI. Although circulating extracellular microvesicles are key effectors of vascular health and disease, how their functional phenotype might be altered with SCI is unknown. The aim of the present study was to determine the effects of microvesicles isolated from SCI adults on endothelial cell inflammation and oxidative stress as well as endothelial nitric oxide (NO) synthase (eNOS) activation and tissue-type plasminogen activator (t-PA) expression. Eighteen young and middle-aged adults were studied: 10 uninjured (7M/3F; age: 39 ± 3 years) and 8 cervical level spinal cord injured (SCI; 7M/1F; 46 ± 4 years; cervical injury: C3: n=1; C5: n=4; C6: n=3). Circulating microvesicles were isolated, enumerated and collected from plasma by flow cytometry. Human umbilical vein endothelial cells (HUVECs) were cultured and treated with microvesicles from either the uninjured or SCI adults. Microvesicles from SCI adults did not affect cellular markers or mediators of inflammation and oxidative stress. However, microvesicles from the SCI adults significantly blunted eNOS activation, NO bioavailability and t-PA production. Intercellular expression of phosphorylated eNOS at Ser1177 and Thr495 sites, specifically, were ∼65% lower and ∼85% higher, respectively, in cells treated with microvesicles from SCI compared with uninjured adults. Decreased eNOS activity and NO production as well as impaired t-PA bioavailability renders the vascular endothelium highly susceptible to atherosclerosis and thrombosis. Thus, circulating microvesicles may contribute to the increased risk of vascular disease and thrombotic events associated with SCI.
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 | 0.000 |
| 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 | 0.000 |
| 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".