Cytokines, glucose and angiotensin II (ANG II) and the expression of Connexin (Cx) 37, 40 and 43 in cultured microvascular endothelial cells
Bibliographic record
Abstract
Endothelial connexins emerge as molecules that strongly affect the conductive behavior of the endothelial cell layer and are involved in renin release in the kidney. Since many of the cardiovascular disease states are associated with micro‐inflammation and with increased activity of ANG II or with increased glucose, we hypothesized that these could affect expression levels of the connexins 37, 40 and 43. Immortalized microvascular endothelial cells (HMEC‐1) were exposed to Ang II (100 nM), a combination of IL6 (20 IU/ml), IFN (4 ng/ml) and TNFa (20 ng/ml) or to 25 mM Glucose for 4 hours in 50% and 100% confluent HMEC‐1 layers. Then, cells were harvested, RNA extracted and reverse transcribed and subjected to qPCR using pre‐designed primers for Cx37, Cx40 and Cx43 using GAPDH as control. Confluency by itself increased Cx37 expression, but did not affect Cx40 and Cx43 expression. The cytokine mix increased expression of 50% and 100% confluent cells 13‐ and 16‐fold (both P<0.05) for Cx40 and 2‐fold (P<0.05) and 2‐fold (NS) for Cx37. Neither ANG II or high glucose affected Cx expression. The present data indicate that inflammatory factors can affect the gene expression of Cx40 in particular. This might point toward a role for Cx40 in coupling inflammation to microvascular function and to renin release.
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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) | 0.001 | 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".