The ultimate goal we seek in ASTHMAPOC project is to develop a “ready to the market” point-of-care disposable device for self-patient management of chronic asthma. According to WHO statistics more than 235 million people suffer from asthma worldwide. As for all other chronic diseases, effective management of asthma is full of challenges because of the difficulties in determining the appropriate treatment programs and the low adherence to control medications. This typically results in many patients non-effectively prevented from asthma exacerbations and the consequent unnecessary too often visits to emergencies. ASTHMAPOC aims providing a low-cost affordable solution to chronic asthmatic patients for self-management of their disease. Patients who self-test can have a better control over their chronic condition, and make therapeutic, behavioral, and environmental adjustments in accordance with advice from healthcare professionals. Our approach to achieve ASTHMAPOC goals is to develop a point-of-care device based on disposable electrochemical biosensors capable to determine the concentration of FENO (fractional exhaled nitric oxide). FENO emerged in the last decade as an important biomarker for asthma assessment and management. The clinical utility of FENO has been already validated to the extent that currently there are approved standards in US and EU (further details will be provided in section 1.3). However, only few point-of-care devices have been developed for clinical use (section 1.4), while there is no single product for self-patient management in the market. This niche market is the one we seek exploring with ASTHMAPOC, but in order to be successful in this market we need first to carry out an in-depth feasibility study.

Domain specialisation (e.g., AI) coupled with skyrocketing manufacturing costs of chips led to a paradigm shift towards chiplet-based computing. However, the current Network-on-Package (NoP) is not suited for the increasing chiplet (CPUs, AI accelerators, etc) count and diverse communication needs. As a result, communication has become the main bottleneck to computing advances. Wireless transmission can enable one-hop communication with low latency parallelism. The wireless links can be reconfigured dynamically for demand-specific services. Finally, the native broadcast capability of wireless transmission will provide natural support for scalability. However wireless communication is limited by the bandwidth. Hence, the ERC Starting Grant WINC explored via simulation combining wireless communication with the high bandwidth of wired connections, proving potential for >5× speedups. In EWiC we aim, for the first time, to emulate and demonstrate wireless communication among chiplets inside a computing system. The goal is to experimentally validate our simulation results, which have shown high speedups in multi-chiplet systems. Successful results will generate interest in the industry for further research and application in various domains, such speeding up computation in pharma for new drug design. EWiC will thus help fully realise the trillion-euro potential of advanced computing, alongside immense social benefits, and establishing EU leadership in this field. The EWiC project will thus assess the commercialization potential of our technology, engaging with key stakeholders such as chipmakers and end-users. Exploitation options including licensing, startup creation, or joint ventures, will be explored based on prototyping results and market entry considerations. Our IPR strategy will involve a thorough analysis to identify and protect the novel results of the project. We will additionally carry out a Freedom to Operate (FTO) analysis to ensure exploitability.

The CHARM project aims to radically transform the cancer diagnosing process and bring the emerging field of digital histopathology to the next level, introducing a novel technology for tissue analysis, capable to measure the molecular composition of the patient tissue samples and to recognize and classify the tumor in a completely label/stain-free way. The instrument, integrated with artificial intelligence (AI), will offer to histopathologists a reliable, fast and low-cost Clinical Decision Support System (CDSS) for cancer diagnosis and personalized cancer therapy. We will develop a Class C, (IVDR, In-Vitro Diagnostic Regulation) medical device consisting of a turnkey low-cost broadband Coherent Raman Scattering (CRS) microscope (enabled by our patented graphene-based fiber laser technology), named the Chemometric Pathology System (CPS), integrating an AI module based on deep learning, statistics and machine learning. The CPS will be capable of automatically analyzing unstained tissues, providing fast and accurate tumour identification (differentiating normal vs neoplastic tissues) with accuracy >98% and final tumour diagnosis prediction (differentiating and grading histologic subtypes) with accuracy >90%, thus offering to the histopathologist a decision tree compatible with existing clinical protocols but with biomolecular-based objectivity and reduced time to result (TRL6). We will develop a robust business case for the application and ensure the project continuation to higher TRLs and the final market entrance. This proposal builds on the results of the ERC POC project GSYNCOR.