Vascular Endothelial Growth Factor Expression in Peritoneal Mesothelial Cells Undergoing Transdifferentiation
Bibliographic record
Abstract
OBJECTIVE: To analyze gene expression of localized peritoneal tissue structures in a rodent model of peritoneal fibrosis. METHODS: Female Sprague Dawley rats were treated with an intraperitoneal injection of an adenovirus expressing active transforming growth factor-beta or control adenovirus. Four and 7 days after infection, animals were sacrificed and frozen sections of parietal peritoneum were subjected to immunofluorescence-aided laser capture microdissection in order to isolate vascular, mesothelial, and submesothelial structures. RNA was extracted from microdissected tissue and gene expression was analyzed by quantitative reverse-transcript polymerase chain reaction. We analyzed genes involved in angiogenesis, epithelial-to-mesenchymal transdifferentiation, and fibrosis. Vascular endothelial growth factor and alpha-smooth muscle actin expression was analyzed with immunohistochemistry of formalin-fixed tissue. RESULTS: Transforming growth factor-beta(1) induced expression of Snail and alpha-smooth muscle actin genes in the peritoneal mesothelium. This same cell population also demonstrated increased gene expression of vascular endothelial growth factor. The distribution of this growth factor was confirmed by immunohistochemistry. The fibrogenic growth factor, connective tissue growth factor, was also strongly induced in the peritoneal mesothelium. CONCLUSIONS: Using immunofluorescence-aided laser capture microdissection, we were able to study gene expression in subcompartments of the peritoneal tissue. We demonstrated that mesothelial cells exhibiting mesenchymal transdifferentiation are associated with increased expression of genes associated with fibrosis and angiogenesis.
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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".