Angiogenesis within the duodenum of patients with cirrhosis is modulated by mechanosensitive <scp>K</scp>ruppel‐like factor 2 and micro<scp>RNA</scp>‐126
Bibliographic record
Abstract
BACKGROUND: The mechanism involved in neovascularization in splanchnic circulation and the main trigger that induces angiogenesis in patients with cirrhosis are not fully recognized. AIMS: To explore the involvement of flow sensitive lung Kruppel-like factor (KLF2), microRNA-126 (miR-126), angiopoietin-2 (Ang-2) and heme oxygenase-1 (HO-1) in modulation of vascular endothelial growth factor (VEGF) signalling that have a critical effect on growth of new blood vessels. METHODS: Duodenal biopsies from 22 patients with cirrhosis and 10 controls were obtained during routine endoscopy. The process of angiogenesis was evaluated by a measurement of CD31 concentration, immunodetection of CD34 protein and estimation of capillary densities. Messenger RNA (mRNA) and protein expressions were analysed by real-time PCR, Western blot or ELISA respectively. RESULTS: Markers of angiogenesis (both, CD31 and CD34) were significantly enhanced in cirrhotic patients. In comparison to healthy controls, levels of Ang-2 and KLF-2 mRNAs as well as Ang-2, KLF-2, HO-1, VEGF protein expressions were considerably increased. Levels of sCD163, a surrogate marker of portal hypertension, correlated with levels of Ang-2, (P = 0.021) and VEGF (P = 0.009). The expression of miR-126, a KLF2-mediated regulator of the VEGF signalling was enhanced in cirrhotic patients. CONCLUSIONS: Our results demonstrate, for the first time in humans, that neovascularization is induced in duodenal tissue of patients with cirrhosis and proangiogenic factors such as KLF-2, Ang-2, miR-126 and VEGF can contribute to the angiogenesis induced by hemodynamic forces. Thus, cirrhosis-induced blood flow and pressure within splanchnic vessels may be important hemodynamic triggers that initiate the angiogenic signalling cascade.
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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 | 0.001 |
| 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".