Effects of neratinib on angiogenesis and the early stage of the embryo using chicken embryo as a model
Bibliographic record
Abstract
Angiogenesis is the process of forming new blood capillaries from pre-existing vessels. Even though it is essential during normal development, it plays a major role in cancer progression. Neratinib is a pan-human epidermal growth factor receptor (HER) inhibitor that has recently been approved for the treatment of HER2-positive breast cancer. However, its effects on angiogenesis and embryogenesis remain unknown. This study examined the antiangiogenic effects of neratinib using the chorioallantoic membrane (CAM) of chicken embryos. We also evaluated neratinib's toxicity during the early stages of normal development using the chicken embryos, primary embryonic fibroblasts (EFBs), and human umbilical vein endothelial cells (HUVEC). Our findings revealed that neratinib significantly inhibited the CAM angiogenesis compared to controls by reducing vessel percentage area and the average vessel length. Furthermore, neratinib downregulated vascular endothelial growth factor (VEGF), a key mediator of angiogenesis. At lower concentrations, neratinib was well-tolerated during early stages of normal development. Additionally, EFBs treated with neratinib showed no morphological or viability changes when compared to controls. However, at the highest concentration tested, neratinib treatment reduced HUVEC cell viability. This effect may be associated with the dysregulation of key apoptotic genes, including caspase-3, caspase-8, caspase-9, and the B-cell lymphoma 2 (Bcl2) gene. Our findings indicate a novel potential application of neratinib as an antiangiogenic agent, exhibiting tolerable toxicity in the early stages of embryogenesis.
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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) | 0.001 | 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".