I4L – Yearly Meeting

Our yearly meeting on November 14th was joined by an incredible number of participants. Nearly all registered PIs and scientists logged in to our virtual work breakfast. Also Ms Spiegel an Ms Spahn from the Klaus Tschira Stiftung signed in to hear about the progress in the subprojects and new ideas in the Hot Topics.

We thank everyone for the successful event and the great appreciation of our surprise.

The workshop “Towards the I4L Data Experience: From Data to Information” which was postponed for time reasons will be made up on December 8th.

Safe the date!

 

 

4th Lecture Series

At our 4h Lecture Series last week, Prof. Alfio Quarteroni gave an interesting talk on „The mathematical heart: a computational model for the simulation of the heart function“.

Mathematical models based on first principles can describe the interaction between electrical, mechanical and fluid-dynamical processes occurring in the heart, as well as the coupling with the external circulation. This is a classical multi-physics problem featuring multi-scale solutions in space and time. Appropriate numerical strategies need to be devised to allow for an accurate and computationally effective simulation of these processes in both physiological and pathological regimes. The was well-attended presentation addressed some of these issues and a few representative applications of clinical relevance.

Deep Characterization of circular RNAs from human cardiovascular cell models and cardiac tissue

Our new study was undertaken to help to uncover potential functions of circular RNAs (circRNAs) that are relevant in cardiovascular disease (CVD) in cardiac model systems and organisms. The study defines a strongly conserved core set of circRNAs in human heart tissue, human induced pluripotent stem cell-derived cardiac myocytes (hiPSC-CMs), as well as mouse and pig heart. These circRNAs are promising potential targets for further studies because their function is likely conserved and therefore may be important in development and progression of CVD.

We employed a specialized deep sequencing approach to generate comprehensive maps of circRNA exon composition in hiPSC-CMs, human umbilical vein cells (HUVECs), and human hearts, which are required for more confident functional studies.

We identified shared circRNAs across all samples, as well as model-specific circRNA signatures and moreover identified a core set of positionally conserved and expressed circRNAs in human, pig, and mouse hearts. Furthermore, we found that the sequence of circRNAs can deviate from the sequence derived from the genome sequence, an important factor in assessing potential functions. Integration of additional data yielded evidence for m6A-modification of circRNAs, potentially linked to translation as well as circRNAs overlapping with potential Agonaut2 binding sites, indicating potential association with the RISC complex.

Moreover, we describe, for the first time in cardiac model systems, a sub class of circRNAs containing the start codon of their primary transcript (AUG circRNAs) and observe an enrichment for m6A-modifications and ribosome association.

Link: www.mdpi.com/2073-4409/9/7/1616

Digitalization against social isolation – patients and doctors at Heidelberg University Hospital benefit from digital solutions

“Due to my previous illness, this is not my first stay in hospital, but the current situation makes it very difficult for me to keep my hopes up alive,” said Isolde Peters (name changed), 92-year-old heart patient who has been at Heidelberg University Hospital for seven days. Due to the nationwide COVID-19 protection measures, she is largely isolated from the social world outside. “I hardly see my children and my grandchildren live in the US. I worry every day.”

On initiative of the Heidelberg research network Informatics for Life the “#Hearts-against-Covid” campaign was launched bringing patients and their relatives virtually together. Thanks to a generous donation from the Klaus Tschira Foundation, tablet computers for video telephony were purchased enabling patients without smartphones to access virtual communication. “Before, I could not have imagined this, but when you are isolated in hospital, the greatest joy of my day is to see my grandchildren smile at me and to see that everyone is doing well,” says Isolde Peters visibly relieved.

“We have created a new way of communication for patients during these difficult times. Seeing their family is more than just entertainment. It is medicine,” said Benjamin Meder, member of the board of directors of Informatics for Life. “In the meantime we have not only created new possibilities for our patients, but we are also conducting all physician training courses via the digital channel.” Heidelberg medical students are also involved in the project helping patients to use the new technology and providing them with advice and support.

Professor Meder’s team also views the digital approaches as a long-term solution to various issues. For example, virtual meetings can help to conserve valuable resources and to reduce greenhouse gases in the future.

Foto: Medical student from Heidelberg is taking part in a virtual training course at the Department of Cardiology, Angiology and Pneumology (Director: Hugo A. Katus). The students are also very committed to explaining the new technology to patients.

Prognoses with the help of artificial intelligence

How long should a patient stay hospitalized after surgery, what conditions affect the recovery and how can we care for our patients in an optimal way?

With the help of Artificial Intelligence and a specially programmed algorithm, our assistant physician, Dr. Bruna Gomes, addresses the questions in her current research project on patients with heart valve diseases after a Transfemoral Catheter Assisted Aortic Valve Replacement (TAVI).

Computer programs with artificial intelligence (AI) are initially “fed” with numerous patient data sets and search this mass of digital data for predictive factors in order to predict certain events (e.g. complications or mortality).

While traditional statistical methods and classical risk scores take into account only about 10 characteristics of a patient in this probability calculation, the algorithm developed by Dr. Gomes – through Machine Learning – captures about 80 characteristics, learns continuously and gives a much more differentiated picture of the patient. “You can compare it to a photo that traditionally has 500 pixels, but our algorithm now allows us to display this image with 4000 pixels, giving us a much higher resolution image,” Dr. Bruna Gomes explains.

With this highly-differentiated image of the patient, Gomes and her colleagues in Prof. Benjamin Meder’s research team hope to improve the assessment of each individual patient in order to enable the best possible prediction of the course of our patients’ disease according to TAVI.

Currently, the algorithm is based on the already evaluated findings of a patient, but the goal is to evaluate imaging diagnostics (CT, MRI, Echocardiography) also by AI, to integrate them into the algorithm and to combine all available information.

AI in the emergency department to improve diagnosis of heart attacks

When patients are admitted to the hospital with chest pain, it is crucial to determine as quickly as possible whether or not a heart attack has occurred.

By combining artificial intelligence and RNA molecules, scientists at the German Centre for Cardiovascular Research (DZHK) have developed a method by which unstable angina pectoris, a condition that can result in myocardial infarction, could in future be detected earlier and more reliably.

To the report by the German Center for Cardiovascular Research: bit.ly/389Az7Q

3rd Lecture Series

At our third Lecture Series last week, Prof. Till Bärnighausen gave an interesting talk on „Integrating causal inference – from correlation to causation in ‘big data’ in health care and policy“.

The past 30 years have seen a revolution in causal inference in epidemiology and health policy research. New approaches to measurement of health-related data, such as sensors and cell phone data, as well as data linkage across many sources are massively increasing opportunities for causal evaluation of health policies and real-life interventions. The well-attended lecture by Prof. Bärnighausen described emerging methodological and data opportunities to establish causal impacts of health policies and interventions.

PI-Meeting

Last Saturday Prof. Katus welcomed the principal investigators at the annual meeting to present their progresses in the respective sub-projects. Also members of the Klaus Tschira Foundation joined the meeting to inform themselves about the development process.

Many Sub-projects have already networked and found approaches for joint projects and further developments, which will now be further specified and processed in the coming year.

The first publications are already in process.