A global profile of glucose-sensitive endothelial-expressed long non-coding RNAs
Bibliographic record
Abstract
Hyperglycemia-related endothelial dysfunction is believed to be the crux of diabetes-associated micro- and macro-vascular complications. We conducted a systematic transcriptional survey to screen for human endothelial long non-coding RNAs (lncRNAs) regulated by elevated glucose levels. lncRNAs and protein-coding transcripts from human umbilical vein endothelial cells (HUVECs) cultured under high (25 mmol/L) or normal (5 mmol/L) glucose conditions for 24 h were profiled with the Arraystar Human LncRNA Expression Microarray V3.0. Of the 30 586 lncRNAs screened, 100 were significantly upregulated and 186 appreciably downregulated (P < 0.05) in response to high-glucose exposure. In the same HUVEC samples, 133 of the 26 109 mRNAs screened were upregulated and 166 downregulated. Of these 299 differentially expressed mRNAs, 26 were significantly associated with 28 differentially expressed long intergenic non-coding RNAs (P < 0.05). Bioinformatics analyses indicated that the mRNAs most upregulated are primarily enriched in axon guidance signaling pathways; those most downregulated are notably involved in pathways targeting vascular smooth muscle cell contraction, dopaminergic signaling, ubiquitin-mediated proteolysis, and adrenergic signaling. This is the first lncRNA and mRNA transcriptome profile of high-glucose-mediated changes in human endothelial cells. These observations may prove novel insights into novel regulatory molecules and pathways of hyperglycemia-related endothelial dysfunction and, accordingly, diabetes-associated vascular disease.
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 | 0.001 | 0.001 |
| 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".