AKT1 Overexpression in Endothelial Cells Leads to the Development of Cutaneous Vascular Malformations In Vivo
Bibliographic record
Abstract
BACKGROUND: Vascular malformations are clinical disorders in which endothelial cells fail to remodel and/or undergo programmed cell death, leading to abnormal persistence of blood vessels. The abnormal persistence of vessels makes therapy difficult because these lesions are resistant to interventions that are effective against hemangiomas. Akt1 is a serine-threonine protein kinase, which is a key mediator of resistance to programmed cell death. Our objective was to determine whether sustained activation of Akt1 could lead to vascular malformation in mice. OBSERVATIONS: We examined the effect of constitutive activation of Akt1 in murine endothelial cells (MS1 cells). Overexpression of active AKT1 in MS1 cells led to the development of vascular malformations, characterized by wide endothelial lumens and minimal investment of smooth muscle surrounding the vessels. The histologic features of these vascular malformations is distinct from ras-transformed MS1 cells (angiosarcoma) and suggest that differing signal abnormalities give rise to human vascular malformations vs malignant vascular tumors. CONCLUSIONS: Inhibition of Akt signaling may be useful in the treatment of vascular malformations. Examination of problematic hemangiomas and vascular malformations for the presence of activated Akt or downstream targets of Akt, such as mammalian target of rapamycin (mTOR), may predict response to treatment with Akt inhibitors or rapamycin. This study provides a potential rationale for the systemic and topical use of these inhibitors for vascular malformations and hemangiomas.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".