Differences between common endothelial cell models (primary human aortic endothelial cells and EA.hy926 cells) revealed through transcriptomics, bioinformatics, and functional analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Endothelial cells (ECs) are involved in various physiological process. Both primary human ECs and immortal endothelial cells are used in various studies. Available genomic or transcriptomic information for difference in ECs is deficient. Therefore, in this study we aim to reveal the difference between primary human aortic ECs (HAECs) and immortal EA.hy926 cells. We identified 529 differentially expressed genes (DEGs) between HAECs and EA.hy926 cells. Gene Ontology (GO), KEGG Pathway and GSEA enrichment analysis suggest that DEGs highly expressed in HAECs are distributed in Rap1 signaling pathway and Ras signaling pathway, which are contributing to the endothelial barrier function and endocytosis, among other functions. We also established long non-coding (lncRNA)-miRNA-mRNA ceRNA network, and further set up protein–protein interaction (PPI) network. High-density lipoprotein (HDL) cellular association experiments were verified that HAECs have stronger response to HDL cellular binding and endocytosis compared to EA.hy926 cells. This study identified DEGs between HAECs and EA.hy926 cells, and found enrichment of the Ras signaling pathway and Rap1 signaling pathway in HAECs, established ceRNA network and suggested that HAECs may have a stronger response to endothelial binding and endocytosis compared to EA.hy926 cells. This work provides a genomic basis to choose suitable EC model to reach respective research goals.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 it