Dasatinib and PD-L1 inhibitors provoke toxicity and inhibit angiogenesis in the embryo
Bibliographic record
Abstract
Dasatinib is a targeted cancer therapy, while programmed death ligand 1 (PD-L1) inhibitors are a form of immune checkpoint therapy used to treat various types of cancers. Several studies showed the potential efficacy of these drugs in the management of triple-negative breast cancer- an aggressive subtype of breast cancer, which can develop during pregnancy. Nevertheless, side effects of Dasatinib (DA) and PD-L1 drugs during pregnancy, especially in the early stages of embryogenesis are not explored yet. The aim of this study is to assess the individual and combined toxicity of DA and PD-L1 inhibitors during the early stages of embryogenesis and to evaluate their effect(s) on angiogenesis using the chorioallantoic membrane (CAM) model of the embryo. Our results show that embryos die at greater rates after exposure to DA and PD-L1 inhibitors as compared to their matched controls. Moreover, treatment with these drugs significantly inhibits angiogenesis of the CAM. To further elucidate key regulator genes of embryotoxicity induced by the actions of PD-L1 and DA, an RT-PCR analysis was performed for seven target genes that regulate cell proliferation, angiogenesis, and survival (ATF3, FOXA2, MAPRE2, RIPK1, INHBA, SERPINA4, and VEGFC). Our data revealed that these genes are significantly deregulated in the brain, heart, and liver tissues of exposed embryos, compared to matched control tissues. Nevertheless, further studies are necessary to evaluate the effects of these anti breast cancer drugs and elucidate their role during pregnancy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".