CD151 Gene Delivery after Myocardial Infarction Promotes Functional Neovascularization and Activates FAK Signaling
Bibliographic record
Abstract
Our previous studies showed that tetraspanin CD151 promotes neovascularization in rat hindlimb and myocardial ischemia models. This study is to assess whether CD151 induces arteriogenesis and promotes functional neovascularization in a pig myocardial infarction model, and to determine the signaling pathways involved. CD151 cDNA and antiCD151 sequence were constructed into a recombinant adeno-associated virus (rAAV) vector. All 26 pigs used either were subjected to coronary artery ligation or did not undergo surgery. Eight wks after viral administration, the expression of CD151 protein was measured by Western blot. The densities of capillaries and arterioles were determined using immunohistochemistry. Regional myocardial perfusion and other myocardial functions were evaluated by (13)N-labeled NH(3) positron emission computed tomography ((13)N-NH(3) PET) and echocardiography. Western blot was performed for assessing the signaling mechanisms. Overexpression of CD151 markedly increased the densities of capillaries and arterioles, significantly enhanced the regional myocardial perfusion, reduced myocardial ischemia, and improved the myocardial contraction, wall motion, and wall thickness. Conversely, antiCD151 gene delivery reversed the above changes. In addition, CD151 activated focal adhesion kinase (FAK), extracellular signal-regulated kinase (ERK), c-Jun N-teminal kinase (JNK), phosphatidylinositol-3 kinase (PI3K), protein kinase B (Akt), and endothelial nitric-oxide synthase (eNOS), and increased nitric oxide (NO) level. These findings demonstrate a robust role of CD151 in inducing and/or upregulating neovascularization. CD151-dependent neovascularization correlates with the activations of FAK, mitogen activated protein kinases (MAPKs), and PI3K signaling, suggesting that CD151 may promote neovascularization via MAPKs and PI3K pathways.
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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".