High‐throughput investigation of endothelial‐to‐mesenchymal transformation (EndMT) with combinatorial cellular microarrays
Bibliographic record
Abstract
In the developing heart, a specific subset of endocardium undergoes an endothelial-to-mesenchymal transformation (EndMT) thus forming nascent valve leaflets. Extracellular matrix (ECM) proteins and growth factors (GFs) play important roles in regulating EndMT but the combinatorial effect of GFs with ECM proteins is less well understood. Here we use microscale engineering techniques to create single, binary, and tertiary component microenvironments to investigate the combinatorial effects of ECM proteins and GFs on the attachment and transformation of adult ovine mitral valve endothelial cells to a mesenchymal phenotype. With the combinatorial microenvironment microarrays, we utilized 60 different combinations of ECM proteins (Fibronectin, Collagen I, II, IV, Laminin) and GFs (TGF-β1, bFGF, VEGF) and were able to identify new microenvironmental conditions capable of modulating EndMT in MVECs. Experimental results indicated that TGF-β1 significantly upregulated the EndMT while either bFGF or VEGF downregulated EndMT process markedly. Also, ECM proteins could influence both the attachment of MVECs and the response of MVECs to GFs. In terms of attachment, fibronectin is significantly better for the adhesion of MVECs among the five tested proteins. Overall collagen IV and fibronectin appeared to play important roles in promoting EndMT process. Great consistency between macroscale and microarrayed experiments and present studies demonstrates that high-throughput cellular microarrays are a promising approach to study the regulation of EndMT in valvular endothelium. Biotechnol. Bioeng. 2016;113: 1403-1412. © 2015 Wiley Periodicals, Inc.
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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 | 0.000 |
| 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".