The Proteome of Circulating Large Extracellular Vesicles in Diabetes and Hypertension
Bibliographic record
Abstract
Hypertension and diabetes induce vascular injury through processes that are not fully understood. Changes in extracellular vesicle (EV) composition could provide novel insights. Here, we examined the protein composition of circulating EVs from hypertensive, diabetic and healthy mice. EVs were isolated from transgenic mice overexpressing human renin in the liver (TtRhRen, hypertensive), OVE26 type 1 diabetic mice and wild-type (WT) mice. Protein content was analyzed using liquid chromatography-mass spectrometry. We identified 544 independent proteins, of which 408 were found in all groups, 34 were exclusive to WT, 16 were exclusive to OVE26 and 5 were exclusive to TTRhRen mice. Amongst the differentially expressed proteins, haptoglobin (HPT) was upregulated and ankyrin-1 (ANK1) was downregulated in OVE26 and TtRhRen mice compared with WT controls. Conversely, TSP4 and Co3A1 were upregulated and SAA4 was downregulated exclusively in diabetic mice; and PPN was upregulated and SPTB1 and SPTA1 were downregulated in hypertensive mice, compared to WT mice. Ingenuity pathway analysis identified enrichment in proteins associated with SNARE signaling, the complement system and NAD homeostasis in EVs from diabetic mice. Conversely, in EVs from hypertensive mice, there was enrichment in semaphroin and Rho signaling. Further analysis of these changes may improve understanding of vascular injury in hypertension and diabetes.
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) | 0.000 | 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".