Researchers at Stanford University School of Medicine have developed a computer model supported by artificial intelligence that simulates the immune responses of pregnant women, aiming to evaluate treatments that may help reduce premature birth. The university stated that the study was published in the journal "Science Advances" with the participation of an international team led by Professor Press Godier.
In the same context, the researchers clarified that the model, named "SIMCO," uses machine learning techniques to simulate the responses of immune cells, allowing the study of changes associated with the timing of the onset of labor. The team relied on data from 63 pregnancy cases to analyze cell responses to drugs and test nine potential treatment systems.
For his part, simulation results showed that combining aspirin and lansoprazole could be promising, as the model predicted an effect exceeding that of aspirin alone by about 15 times. The study also pointed to the possibility of folic acid affecting immune pathways associated with pregnancy, necessitating further research.
Researchers emphasized that the results do not prove the efficacy of treatments in humans, noting that the team has obtained the necessary approvals to begin a clinical trial. Premature birth is known as that which occurs before completing the thirty-seventh week of pregnancy, and babies born at this stage may require special medical care.
Source: Syrian Arab News Agency (SANA)
Comments (0)