Project archive

Fraunhofer ITEM researchers use the ex-vivo model of the isolated perfused rat lung (IPL). In this alternative model, they studied the transition of a fluorescence-labeled substance into the perfusate, which served as blood substitute in this model. Two different methods were used to generate aerosols with different droplet sizes.

The web application Genealyzer abstracts away mathematical and programmatical details in order to enable a convenient and customizable analysis of microarray data for large-scale reproducibility studies.

The aim of an exploratory clinical study funded by the German Federal Environment Agency is to determine the respective threshold doses that trigger allergic symptoms in people allergic to ragweed or birch pollen.

In cooperation with the Hannover Medical School, Fraunhofer ITEM scientists are investigating the safety and efficacy of inhaled booster vaccination with the vector vaccine MVA-SARS-2-ST in a phase-I trial.

The project ZeroPM will establish an evidence-based multilevel framework to guide policy, technological and market incentives to minimize the use, emissions and pollution with entire groups of PM substances. ZeroPM is a research project funded by EU’s research and innovation funding programme, Horizon 2020.

The objective of the Cefic-LRI project is to develop a multi-stage assessment approach to determine potential health impacts to humans from inhalation of microplastics.

Active substances can form nitrosamines inside the body. Clustering methods based on molecular properties help ITEM researchers to identify which active substances are more reactive.

Under the EU project “imSAVAR” financed by the Innovative Medicines Initiative (IMI), experts from a number of countries and disciplines are working together to improve the prediction of undesirable effects of medicines

In the RNAuto project, seven Fraunhofer institutes are collaborating to develop automated manufacturing technologies for mRNA-based therapeutics.

The overarching goal of the DigitaLung project is to develop and validate a machine-learning-based digital auscultation system that will improve patient care in the long term.