The care of critically ill patients requiring mechanical ventilation remains beset by the combined effects of critical illness and of the mechanical ventilation of the lung. Such effects are compounded by the lack of knowledge of the rate and time at which 'weaning' from the machine should occur. This project aims at developing an adaptive decision support system to assist ICU staff in the optimisation of ventilation and weaning processes. To help achieve this, an adaptive hybrid model which describes the patient-ventilator interaction during ventilation as well as weaning phases will be elicited. In addition to knowledge gathered through data relating to blood gases and lung expansions, the project aims at exploiting a revolutionary technique developed at sheffield, called Electrical Impedance Tomography (EIT) which consists of measuring, in a non-invasive fashion, the degree of expansion or collapse of the lungs and the effect of the ventilation strategy upon these. Two important aspects of this project relate to the inclusion of the EIT measurement technique to improve the monitoring of the patient's respiratory demands and to the use of granular computing for the hybrid model represented by the neural-fuzzy layer. The elicitation of such a model will form the basis for the design and development of an adaptive decision support system for optimal therapeutic advice on ventilator settings and weaning operation. On-line and off-line validation of the system in a series of ICU trials are envisaged.

Infections caused by resistant microorganisms often fail to respond to the standard treatment, resulting in prolonged hospital stays, higher health care expenditures, and a greater risk of death.1 Antimicrobials Resistant Organism (AMRO) or Superbugs are invisible, and can survive on surfaces for up to three days. That means that they can be transferred when one infected person simply touches another, or when the patient touches something on which the pathogen resides like a stethoscope or a TV remote control.2 Antibiotic resistance varies according to geographic locations and is directly proportional to the use and misuse of antibiotics. Active surveillance systems are in place across all European member states and Norway and are to be considered one of the main contributing factors to the reduction AMRO infections over the years. On the other hand the effort done up to now is not enough to eradicate AMRO/superbugs infections and ANTI-SUPERBUGS PCP will challenge the industry to develop solutions that in contact with resistant microorganisms can detect their presence and give real-time feedback to the user and at the same time share the information with the healthcare provider electronic record systems linking the infection with the place of the detection. The project will be developped with the support of Sara Bedin, who will provide her know-how in Procurement in Innovation.

Conditions such as long-gap oesophageal atresia (LGOA) and short bowel syndrome (SBS) are two examples of chronic paediatric cases of gastrointestinal tissue reconstruction where up to two thirds of the oesophagus and bowel, respectively, may be missing. These are among the most complex and devastating paediatric anomalies that have a life-long debilitating effect on patients. Their current treatments are not widely available, are complex, primitive, long-term, and have disputed outcome quality. Families and surgeons have long sought an effective treatment to improve these patients' quality of life. The proposed project aims to initiate an ambitious research agenda for a novel technology for the repair and reconstruction of soft tubular tissues inside the body using robotic and tissue regeneration principles. The underlying technology unifies the fields of tissue engineering, surgery and medical implants into a new concept of 'robotic implants'. The proposed robotic implants are one-size-fits-all linings for tubular tissues that enable autonomous tissue-responsive mechanical interaction with tissues to induce their growth. Based on evidence from cell biology studies and clinical practice showing how tissues respond to mechanical stimulation in vivo, the proposed robotic implant applies gentle force directly to tissues to induce growth through cell proliferation. Thus, these robotic implants deliver controlled, long-term, customisable and optimal reconstructive therapy for tissues in an unprecedented way. The proposed technology has the potential to restore patients' mobility and social activity, as well as reduce hospitalisation and post-surgery complications, treatment and costs. This proposal has a pioneering focus: to develop the design, fabrication and control of robotic implants that can physically and physiologically adapt to the changing properties of tissues and stimulate their growth. These robotic implants will consist of fundamental, compact and functional elastomeric strands that can be assembled into an architecture that can elongate with the growing tissue and apply controlled, directional mechanical stimulation to the tissue. This project is the basis of an exciting interdisciplinary research framework that will allow communities of surgeons, biologists, tissue engineers and tissue mechanics researchers to investigate basic mechanisms of tissue growth and understand the relationships among tissue strain, tissue regeneration and inflammatory responses. In particular, the technology to be developed in this project will be a precursor clinical device for LGOA and SBS. This project also launches an investigation into soft robots that physically adapt and perform inside the body, which is imperative for tissue regeneration and growth as well as for wearable technologies that need to adapt to children's developmental stages.

Valvular Heart Disease currently affects 2.5% of the population, but is overwhelmingly a disease of the elderly and consequently on the rise. It is dominated by two conditions, Aortic Stenosis and Mitral Regurgitation, both of which are associated with significant morbidity and mortality, yet which pose a truly demanding challenge for treatment optimisation. By combining multiple complex modelling components developed in recent EC-funded research projects, a comprehensive, clinically-compliant decision-support system will be developed to meet this challenge, by quantifying individualised disease severity and patient impairment, predicting disease progression, ranking the effectiveness of alternative candidate procedures, and optimising the patient-specific intervention plan. This algorithmically-driven process will dramatically improve outcomes and consistency across Europe in this fast-growing patient group, maximising individual, societal and economic outcomes.