Tumor burden as assessed by 18 F FDG PET scan as predictive biomarker for immune checkpoint blockers in advanced NSCLC - a multi centric study

Introduction Only a proportion of patients with advanced NSCLC benefit from Immune checkpoint blockers (ICBs). No biomarker is validated to choose between ICBs monotherapy or in combination with chemotherapy (Chemo-ICB) when PD-L1 expression is above 50%. The aim of the present study is to validate the biomarker validity of total Metabolic Tumor Volume (tMTV) as assessed by 2-deoxy-2-[18F]fluoro-d-glucose positron emission tomography ([18F]FDG-PET) Material and methods This is a multicentric retrospective study. Patients with advanced NSCLC treated with ICBs, chemotherapy plus ICBs and chemotherapy were enrolled in 12 institutions from 4 countries. Inclusion criteria was a positive PET scan performed within 42 days from treatment start. TMTV was analyzed at each center based on a 42% SUVmax threshold. High tMTV was defined ad tMTV>median Results 493 patients were included, 163 treated with ICBs alone, 236 with chemo-ICBs and 94 with CT. No correlation was found between PD-L1 expression and tMTV. Median PFS for patients with high tMTV (100.1 cm3) was 3.26 months (95% CI 1.94–6.38) vs 14.70 (95% CI 11.51–22.59) for those with low tMTV (p=0.0005). Similarly median OS for pts with high tMTV was 11.4 months (95% CI 8.42 – 19.1) vs 33.1 months for those with low tMTV (95% CI 22.59 – NA), p .00067. In chemo-ICBs treated patients no correlation was found for OS (p = 0.11) and a borderline correlation was found for PFS (p=0.059). Patients with high tMTV and PD-L1 ≥ 50% had a better PFS when treated with combination of chemotherapy and ICBs respect to ICBs alone, with 3.26 months (95% CI 1.94 – 5.79) for ICBs vs 11.94 (95% CI 5.75 – NA) for Chemo ICBs (p = 0.043). Conclusion tMTV is predictive of ICBs benefit, not to CT benefit. tMTV can help to select the best upfront strategy in patients with high tMTV.

Thermal and air quality modeling of an electric vehicle cabin with low-cost sensors and reinforcement learning

The design process of any electric vehicle system has to be oriented towards the best energy efficiency, together with the constraint of maintaining comfort in the vehicle cabin. Main aim of this study is to research the best thermal management solution in terms of HVAC efficiency without compromising occupant’s comfort and internal air quality. An Arduino controlled Low Cost System of Sensors was developed and compared against reference instrumentation (average R-squared of 0.92) and then used to characterise the vehicle cabin in real parking and driving conditions trials. Data on the energy use of the HVAC was retrieved from the car On-Board Diagnostic port. Energy savings using recirculation can reach 30 %, but pollutants concentration in the cabin builds up in this operating mode. Moreover, the temperature profile appeared strongly nonuniform with air temperature differences up to 10° C. Optimisation methods often require a high number of runs to find the optimal configuration of the system. Fast models proved to be beneficial for these task, while CFD-1D model are usually slower despite the higher level of detail provided. In this work, the collected dataset was used to train a fast ML model of both cabin and HVAC using linear regression. Average scaled RMSE over all trials is 0.4 %, while computation time is 0.0077 ms for each second of simulated time on a laptop computer. Finally, a reinforcement learning environment was built in OpenAI and Stable-Baselines3 using the built-in Proximal Policy Optimisation algorithm to update the policy and seek for the best compromise between comfort, air quality and energy reward terms. The learning curves show an oscillating behaviour overall, with only 2 experiments behaving as expected even if too slow. This result leaves large room for improvement, ranging from the reward function engineering to the expansion of the ML model.

Development of machine learning methods for multi-modal biomarkers detection and integration

In medicine, innovation depends on a better knowledge of the human body mechanism, which represents a complex system of multi-scale constituents. Unraveling the complexity underneath diseases proves to be challenging. A deep understanding of the inner workings comes with dealing with many heterogeneous information. Exploring the molecular status and the organization of genes, proteins, metabolites provides insights on what is driving a disease, from aggressiveness to curability. Molecular constituents, however, are only the building blocks of the human body and cannot currently tell the whole story of diseases. This is why nowadays attention is growing towards the contemporary exploitation of multi-scale information. Holistic methods are then drawing interest to address the problem of integrating heterogeneous data. The heterogeneity may derive from the diversity across data types and from the diversity within diseases. Here, four studies conducted data integration using customly designed workflows that implement novel methods and views to tackle the heterogeneous characterization of diseases. The first study devoted to determine shared gene regulatory signatures for onco-hematology and it showed partial co-regulation across blood-related diseases. The second study focused on Acute Myeloid Leukemia and refined the unsupervised integration of genomic alterations, which turned out to better resemble clinical practice. In the third study, network integration for artherosclerosis demonstrated, as a proof of concept, the impact of network intelligibility when it comes to model heterogeneous data, which showed to accelerate the identification of new potential pharmaceutical targets. Lastly, the fourth study introduced a new method to integrate multiple data types in a unique latent heterogeneous-representation that facilitated the selection of important data types to predict the tumour stage of invasive ductal carcinoma. The results of these four studies laid the groundwork to ease the detection of new biomarkers ultimately beneficial to medical practice and to the ever-growing field of Personalized Medicine.

Morte cardiaca improvvisa in Emilia-Romagna: caratteristiche della rete multidisciplinare e risultati dei primi quattro anni

Introduzione: dal 2018 è attiva in Emilia-Romagna una rete multidisciplinare per i casi di morte cardiaca improvvisa (MCI). In questo studio sono riportate le caratteristiche della rete e i risultati dei primi quattro anni di attività. Materiali e metodi: sono inclusi i casi di MCI avvenuti in Emilia-Romagna dal 2018 in soggetti con età > 1 anno e ≤55 anni. L’autopsia è stata eseguita secondo le raccomandazioni internazionali ed il cuore inviato all’Unità di Patologia Cardiovascolare del Policlinico di Sant’Orsola. A seconda degli scenari sono state eseguite analisi genetiche, tossicologiche e microbiologiche. In caso di patologie geneticamente determinate o nelle morti sine materia è stato avviato lo screening familiare. Risultati: nei primi quattro anni di attività sono pervenuti 83 casi (età media 37 anni). In tutti i casi è stato eseguito un esame cardio-patologico completo e in 55 soggetti (66%) l’analisi genetica. Tra i 75 casi completati, è stata identificata una causa certa/altamente probabile di decesso in 66 (88%). Le patologie coronariche sono la patologia più frequentemente diagnostica (20 casi, 27%) seguita dalle cardiomiopatie (21%), mentre in 9 soggetti è stata riscontrata una malattia infiammatoria. L’indagine genetica è stata completata in 42 casi, identificando in 8 una mutazione causativa o una variante verosimilmente patogena (materiale inidoneo in 9). Successivamente, è stato eseguito lo screening in 14 famiglie di probandi deceduti per patologie non acquisite identificando sei soggetti di altrettante famiglie con un fenotipo positivo o dubbio. L’analisi genetica ha permesso di individuare quattro parenti con la stessa mutazione/variante verosimilmente patogena del probando. Complessivamente, in quattro soggetti è stato impiantato un defibrillatore per la prevenzione primaria della MCI. Conclusioni: la rete multidisciplinare della MCI in Emilia-Romagna ha permesso di identificare una causa di decesso in quasi nove casi su dieci, diagnosticare diversi parenti affetti e approntare strategie preventive per la MCI.

Development of multicomponent models for the prediction of lubricant oil-fuel dilution in internal combustion engines

The current environmental crisis is forcing the automotive industry to face tough challenges for the Internal Combustion Engines development in order to reduce the emissions of pollutants and Greenhouse gases. In this context, in the last decades, the main technological solutions adopted by the manufacturers have been the direct injection and the engine downsizing, which led to the rising of new concerns related to the fuel-cylinder walls physical interaction. The fuel spray possibly impacts the cylinder liner wall, which is wetted by the lubricant oil thus causing the derating of the lubricant properties, increasing the oil consumption, and contaminating the lubricant oil in the crankcase. Also, concerning hydrogen fuelled internal combustion engines, it is likely that the high near-wall temperature, which is typical of the hydrogen flame, results in the evaporation of a portion of the lubricant oil, increasing its consumption. With regards on the innovative combustion systems and their control strategies, optical accessible engines are fundamental tools for experimental investigations on such combustion systems. Though, due to the optical measurement line, optical engines suffer from a high level of blow-by, which must be accounted for. In light of the above, this thesis work aims to develop numerical methodologies with the aim to build useful tools for supporting the design of modern engines. In particular, a one-dimensional modelling of the lubricant oil-fuel dilution and oil evaporation has been performed and coupled with an optimization algorithm to achieve a lubricant oil surrogate. Then, a quasi-dimensional blow-by model has been developed and validated against experimental data. Such model, has been coupled with CFD 3D simulations and directly implemented in CFD 3D. Finally, CFD 3D simulations coupled with the VOF method have been performed in order to validate a methodology for studying the impact of a liquid droplet on a solid surface.

Modelling, simulations and monitoring for sustainable plant and animal production systems through precision technique

Protected crop production is a modern and innovative approach to cultivating plants in a controlled environment to optimize growth, yield, and quality. This method involves using structures such as greenhouses or tunnels to create a sheltered environment. These productive solutions are characterized by a careful regulation of variables like temperature, humidity, light, and ventilation, which collectively contribute to creating an optimal microclimate for plant growth. Heating, cooling, and ventilation systems are used to maintain optimal conditions for plant growth, regardless of external weather fluctuations. Protected crop production plays a crucial role in addressing challenges posed by climate variability, population growth, and food security. Similarly, animal husbandry involves providing adequate nutrition, housing, medical care and environmental conditions to ensure animal welfare. Then, sustainability is a critical consideration in all forms of agriculture, including protected crop and animal production. Sustainability in animal production refers to the practice of producing animal products in a way that minimizes negative impacts on the environment, promotes animal welfare, and ensures the long-term viability of the industry. Then, the research activities performed during the PhD can be inserted exactly in the field of Precision Agriculture and Livestock farming. Here the focus is on the computational fluid dynamic (CFD) approach and environmental assessment applied to improve yield, resource efficiency, environmental sustainability, and cost savings. It represents a significant shift from traditional farming methods to a more technology-driven, data-driven, and environmentally conscious approach to crop and animal production. On one side, CFD is powerful and precise techniques of computer modeling and simulation of airflows and thermo-hygrometric parameters, that has been applied to optimize the growth environment of crops and the efficiency of ventilation in pig barns. On the other side, the sustainability aspect has been investigated and researched in terms of Life Cycle Assessment analyses.

Two stroke cycle, novel combustion concepts and electrification for a new generation of internal combustion engines

The pursuit of decarbonization and increased efficiency in internal combustion engines (ICE) is crucial for reducing pollution in the mobility sector. While electrification is a long-term goal, ICE still has a role to play if coupled with innovative technologies. This research project explores various solutions to enhance ICE efficiency and reduce emissions, including Low Temperature Combustion (LTC), Dual fuel combustion with diesel and natural gas, and hydrogen integration. LTC methods like Dual fuel and Reactivity Controlled Compression Ignition (RCCI) show promise in lowering emissions such as NOx, soot, and CO2. Dual fuel Diesel-Natural Gas with hydrogen addition demonstrates improved efficiency, especially at low loads. RCCI Diesel-Gasoline engines offer increased Brake Thermal Efficiency (BTE) compared to standard diesel engines while reducing specific NOx emissions. The study compares 2-Stroke and 4-Stroke engine layouts, optimizing scavenging systems for both aircraft and vehicle applications. CFD analysis enhances specific power output while addressing injection challenges to prevent exhaust short circuits. Additionally, piston bowl shape optimization in Diesel engines running on Dual fuel (Diesel-Biogas) aims to reduce NOx emissions and enhance thermal efficiency. Unconventional 2-Stroke architectures, such as reverse loop scavenged with valves for high-performance cars, opposed piston engines for electricity generation, and small loop scavenged engines for scooters, are also explored. These innovations, alongside ultra-lean hydrogen combustion, offer diverse pathways toward achieving climate neutrality in the transport sector.