In today’s world, scientists of different fields need to work together – for their own success as well as for patients’ well-being. PhD programs, which provide an existing network, can facilitate this. Students in Zurich tell us what they think the future holds for them.
Photos & Video: Raphael Zubler
Validating diagnostic stainings with samples from 16 institutions
“In the molecular biology program in Latvia, we had maybe 60 students,” Undine Rulle explains, “By the time we got to PhD level, we had five candidates.” Although this is in contrast with the program at ETH Zurich, she says: “In Latvia, we could go to the three biggest institutes for help and we knew all of our professors well. It’s a different style.”
“Here in Zurich, I am part of a collaboration between 16 clinical centers in Europe and the U.S. which aims to validate diagnostic stainings.” The group collected over 2,500 tissue samples across the institutions. She says that they are doing diagnostics on the diagnostics: “You need to evaluate whether or not the protocols are working.“
Rulle also collaborated with various institutions beta-testing for a medical company. “I aided in the development of a portable diagnostic device. The company needed to know whether the machine was able to stain potential cancer tissue any better than the one that is now used. So, we helped them test it. We used computerized methods to validate the results. We began five years ago, and we are currently working with the model that they are going to produce. It gives you a slide in 15 or 20 minutes approximately. The one that they use now can take up to two hours to stain tissue.”
Rulle concludes: “The company received information from the group on how to make a better product, and we got to use something that is a lot faster and as good, or even better, than what we would normally have. It’s a win-win situation.”
- The statements by Siemens Healthineers customers described herein are based on results that were achieved in the customer’s unique setting. Since there is no “typical” hospital and many variables exist (e.g., hospital size, case mix, level of IT adoption) there can be no guarantee that other customers will achieve the same results.