Coordinator
Research groups
- TICs and Emotions
- Applied Biomechanics
- Signal Interpretation
- Wireless Technologies, Security and e-Health
- Biomaterials
- Power Electronics
- Smart Environments
- Ergonomics
- Mechanobiology
- Tissue Microenvironment
- Laser Technologies
- Physiotherapy
Many rare diseases cause chronic health problems or are even life-threatening. The impact on the quality of life of affected patients, of whom many are children, is significant. To date, a limited but increasing number of so-called orphan drugs (drugs for rare diseases) are reaching patients.
However, the majority of rare diseases are still without any effective treatment. The development of novel human systems for drug discovery therefore represents a major public health priority. Microfluidic technology-based “Organs-on-a-chip” represents a powerful tool for investigating rare disease mechanisms and testing new drug and treatment due to their ability to mimic tailored micro-environment architecture inspired by organ-level functions in vivo.
Projects
Argira is a system that generates information of great relevance for the generation of knowledge in the changes of different aquatic environments (seas, lakes, estuaries, etc.), in particular to measure temperature, pressure
Computational biomechanics and bioengineering 3D printing to develop a personalized regenerative biological ventricular assist device to provide lasting functional support to damaged hearts
3D printing to restore a damaged heart
To validate technically and clinically novel methods to understand, and give support to tremor diagnosis.
By developing biological ventricular assist devices (BioVADs), BRAV3 will bring a quantum leap in regenerative medicine and its translation towards the clinic, as well as impact the development of novel medical technologies whilst greatly advancing our knowledge on human heart development.
Digital transformation of clinical practice by using patient-specific cardiac digital twins for risk prediction and therapy optimization
Construction of patient-specific digital twins of the human heart by statistical and mechanistic in silico modeling, fed with extensive clinical data collected and processed digitally
Customised DNA-based nanocarriers to boost heart healing
DNABEATS aims to bring advanced materials to regenerate injured myocardium.
In vitro characterization and in vitro/in vivo simulation of the effect of hypoxia and drug dosis in glioblastoma growth
The in vitro and in silico study of glioblastoma multiforme (GBM) tumor is proposed.
Individual and Collective Migration of Immune Cellular Systems
ICoMICS aims to develop a novel virtual simulation platform to investigate how therapeutic immune cells (TICs) detect, move and interact with cancer cells and the tumour microenvironment.
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multI-discipliNary, multi-Sectoral and multi-national trainIng network on Digital biomarkErs for supraventricular arrHythmia charactErizAtion and Risk assessmenT
Establishing a multi-disciplinary network to tackle the design and the early-phase validation of digital biomarkers, specifically targeting the diagnosis of supraventricular arrhythmias (SVAs) and their associated potential for adverse risk assessment, via the joint combination of signal processing, artificial intelligence and non-clinical devices.
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COMPUTER-AIDED EFFECTIVE FRACTURE RISK STRATIFICATION OF PATIENTS WITH VERTEBRAL METASTASES FOR PERSONALISED TREATMENT THROUGH ROBUST COMPUTATIONAL MODELS VALIDATED IN CLINICAL SETTINGS
METASTRA will propose new guidelines for the stratification and management of metastatic patients. METASTRA approach is expected to cut the uncertain diagnoses from the current 60% down to 20% of cases. This will reduce patient suffering, and allow cutting expenditure by 2.4B€/year.
To improve diagnosis and monitoring of sleep apnea-hypopnea syndrome (SAHS)
ECG morphology clusters for sudden cardiac death risk stratification in coronary artery disease using genetics and human-based computational models
Integrate the information from the genetic architecture of the distinct ECG morphological clusters to understand the mechanisms underlying SCD risk.
