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Hydrogen fuel is gaining particular attention in internal combustion engines. In addition to zero-carbon emissions, major advantages relate to its combustion characteristics, which allow a significant increase in thermal efficiency under ultra-lean operation and with very low NOx levels. The ignition system is one of the main technology enablers, as it determines the capability to control ultra-lean operations, avoid backfire phenomena, and/or reduce the risks of abnormal combustions. The latter results from hydrogen’s low ignition energy and it is associated with factors like high-temperature residuals, hot spots, and irregular spark plug discharge. The ACIS gen 2-Barrier Discharge Igniter excels in accelerating the initial flame growth speed by the generation of non-equilibrium low-temperature plasma, a strong ignition promoter for the combined action of kinetic and thermal effects. Moreover, its volumetric discharge facilitates combustion initiation on a wide region, in contrast to the localized ignition of traditional spark systems. In this work we present for the first time, to the best of our knowledge, experimental results showing the performance of a hydrogen engine with a low-temperature plasma discharge. Tests were conducted on a single-cylinder research engine, achieving ultra-lean conditions with cycle-to-cycle variability results below 2.5%. The analysis indicates that the H2-BDI combined solution is capable of accelerating the evolution of the flame front compared to traditional spark plugs, leading to a significant reduction in the cycle-to-cycle variability. A meticulous adjustment of the BDI control parameters further enhances igniter performance and contributes to a deeper understanding of the innovative approach proposed in this study.

Currently, conventional spark–ignition engines face challenges in meeting the ever-growing demands of customers and increasingly stringent regulations regarding pollutant emissions. A combination of innovative strategies and carbon-neutral fuels is deemed necessary in order to further reduce fuel consumption and minimize engine emissions. The present work aims to assess the performance of combustion strategies using low-carbon-content fuel, such as methanol M100, ignited by a plasma-assisted igniter (PAI) under ultra-lean conditions. The experimental campaign is conducted on a single-cylinder research engine at 1000 rpm and low loads, moving up to the engine lean stable limits. The specific purpose of this work is to determine the benefits brought by the proposed strategy, referred to as M100–PAI, which compared market gasoline E5 ignited by the PAI system and conventional spark. The synergy between M100 (methanol) and Plasma-Assisted Ignition (PAI) in internal combustion engines yielded notable benefits. This combination significantly improved combustion stability if compared to the other combinations tested, by extending the lean stable limit to λ = 2.0, reducing cycle-to-cycle variability, and facilitating faster flame front acceleration, resulting in enhanced homogeneity. These enhancements, obtained with the combination M100–PAI, contributed to higher fuel efficiency, showing a 10% efficiency gain over the combination E5–gasoline spark ignition. The findings highlight the potential of innovative combustion strategies using low-carbon fuels and advanced ignition systems to meet stringent emissions regulations while improving engine performance.

Inguinoscrotal hernia is a common pediatric disease but a rare condition in the fetus. We present a case, from our institution, of fetal inguinoscrotal hernia with possible rapid development. In addition to our case, we present a literature update on fetal inguinoscrotal hernia in order to enhance the ability to recognize it from the other scrotal masses on ultrasound. Antenatal management, differential diagnosis and postnatal management are also discussed.

Machine learning based on neural networks facilitates data-driven techniques for handling large amounts of data, either obtained through experiments or simulations at multiple spatio-temporal scales, thereby finding the hidden patterns underlying these data and promoting efficient research methods. The main purpose of this paper is to extend the capabilities of a new solver called realFluidReactingNNFoam, under development at the University of Perugia, in OpenFOAM with a neural network algorithm for replacing complex real-fluid thermophysical property evaluations, using the approach of coupling OpenFOAM and Python-trained neural network models. Currently, neural network models are trained against data generated using the Peng–Robinson equation of state assuming a mixture’s frozen temperature. The OpenFOAM solver, where needed, calls the neural network models in each grid cell with appropriate inputs, and the returned results are used and stored in suitable OpenFOAM data structures. Such inference for thermophysical properties is achieved via the “Neural Network Inference in C made Easy (NNICE)” library, which proved to be very efficient and robust. The overall model is validated considering a liquid-rocket benchmark comprised of liquid-oxygen (LOX) and gaseous-hydrogen (GH2) streams. The model accounts for real-fluid thermodynamics and transport properties, making use of the Peng–Robinson equation of state and the Chung transport model. First, the development of a real-fluid model with an artificial neural network is described in detail. Then, the numerical results of the transcritical mixing layer (LOX/GH2) benchmark are presented and analyzed in terms of accuracy and computational efficiency. The results of the overall implementation indicate that the combined OpenFOAM and machine learning approach provides a speed-up factor higher than seven, while preserving the original solver accuracy.

In recent years, immune checkpoint inhibitors have significantly changed the field of oncology, emerging as first-line treatment, either alone or in combination with other regimens, for numerous malignancies, improving overall survival and progression-free survival in these patients. However, immune checkpoint inhibitors might also cause severe or fatal immune-related adverse events, including adverse cardiovascular events. Initially, myocarditis was recognized as the main immune checkpoint inhibitor-related cardiac event, but our knowledge of other potential immune-related cardiovascular adverse events continues to broaden. Recently, preclinical and clinical data seem to support an association between immune checkpoint inhibitors and accelerated atherosclerosis as well as atherosclerotic cardiovascular events such as cardiac ischemic disease, stroke, and peripheral artery disease. In this review, by offering a comprehensive overview of the pivotal role of inflammation in atherosclerosis, we focus on the potential molecular pathways underlying the effects of immune checkpoint inhibitors on cardiovascular diseases. Moreover, we provide an overview of therapeutic strategies for cancer patients undergoing immunotherapy to prevent the development of cardiovascular diseases.

The H2-ICE project aims at developing, through numerical simulation, a new generation of hybrid powertrains featuring a hydrogen-fueled Internal Combustion Engine (ICE) suitable for 12 m urban buses in order to provide a reliable and cost-effective solution for the abatement of both CO2 and criteria pollutant emissions. The full exploitation of the potential of such a traction system requires a substantial enhancement of the state of the art since several issues have to be addressed. In particular, the choice of a more suitable fuel injection system and the control of the combustion process are extremely challenging. Firstly, a high-fidelity 3D-CFD model will be exploited to analyze the in-cylinder H2 fuel injection through supersonic flows. Then, after the optimization of the injection and combustion process, a 1D model of the whole engine system will be built and calibrated, allowing the identification of a “sweet spot” in the ultra-lean combustion region, characterized by extremely low NOx emissions and, at the same time, high combustion efficiencies. Moreover, to further enhance the engine efficiency well above 40%, different Waste Heat Recovery (WHR) systems will be carefully scrutinized, including both Organic Rankine Cycle (ORC)-based recovery units as well as electric turbo-compounding. A Selective Catalytic Reduction (SCR) aftertreatment system will be developed to further reduce NOx emissions to near-zero levels. Finally, a dedicated torque-based control strategy for the ICE coupled with the Energy Management Systems (EMSs) of the hybrid powertrain, both optimized by exploiting Vehicle-To-Everything (V2X) connection, allows targeting H2 consumption of 0.1 kg/km. Technologies developed in the H2-ICE project will enhance the know-how necessary to design and build engines and aftertreatment systems for the efficient exploitation of H2 as a fuel, as well as for their integration into hybrid powertrains.

Ambient Intelligence is a vision of daily life in which intelligent devices interact with humans to make their lives easier, and the technology is invisible. Artificial Intelligence (AI) governs this smart environment and must interact with humans to best meet their needs and demands. Although voice assistants are very popular and efficient as conversational AI, under some conditions they cannot be used. Therefore, this work proposed a complementary tactile and tangible interface to converse with AI, creating new Tactile Signs. A prototype of TactCube, a wireless cube-shaped device that can interact with AI using only the tactile sense, is presented. The hypothesis is that TactCube can be manipulated with one hand and generate a sequence of numbers that can be interpreted as a new tactile language by a neural network solution. The paper describes the initial research made to define how these sequences can be generated and demonstrates how TactCube is able to do it.

In this work, we explore the role of augmented reality as a meta-user interface, with particular reference to its applications for interactive fitting room systems and the impact on the related shopping experience. Starting from literature and existing systems, we synthesized a set of nine interaction design patterns to develop AR fitting rooms and to support the shopping experience. The patterns were evaluated through a focus group with possible stakeholders with the aim of evaluating and envisioning the effects on the shopping experience. The focus group analysis shows as a result that the shopping experience related to an AR fitting room based on the proposed patterns is influenced by three main factors, namely: the perception of the utility, the ability to generate interest and curiosity, and the perceived comfort of the interaction and environment in which the system is installed. As a further result, the study shows that the patterns can successfully support these factors, but some elements that emerged from the focus group should be more investigated and taken into consideration by the designers.

In the present numerical study, implicit large eddy simulations (LES) of non-reacting multi-components mixing processes of cryogenic nitrogen and n-dodecane fuel injections under transcritical and supercritical conditions are carried out, using a modified reacting flow solver, originally available in the open source software OpenFOAM®. To this end, the Peng-Robinson (PR) cubic equation of state (EOS) is considered and the solver is modified to account for the real-fluid thermodynamics. At high pressure conditions, the variable transport properties such as dynamic viscosity and thermal conductivity are accurately computed using the Chung transport model. To deal with the multicomponent species mixing, molar averaged homogeneous classical mixing rules are used. For the velocity-pressure coupling, a PIMPLE based compressible algorithm is employed. For both cryogenic and non-cryogenic fuel injections, qualitative and quantitative analyses are performed, and the results show significant effects of the chamber pressure on the mixing processes and entrainment rates. The capability of the proposed numerical model to handle multicomponent species mixing with real-fluid thermophysical properties is demonstrated, in both supercritical and transcritical regimes.

The new generation of internal combustion engines is facing various research challenges which often include modern fuels and different operating modes. A robust modeling framework is essential for predicting the dynamic behavior of such complex phenomena. In this article, the implementation, verification, and validation of a Eulerian multi-fluid model for spray applications within the OpenFOAM toolbox are presented. Due to its open-source nature and broad-spectrum of available libraries and solvers, OpenFOAM is an ideal platform for academic research. The proposed work utilizes advanced interfacial momentum transfer models to capture the behavior of deforming droplets at a high phase fraction. Furthermore, the WAVE breakup model is employed for the transfer of mass from larger to smaller droplet classes. The work gives detailed instructions regarding the numerical implementation, with a dedicated section dealing with the implementation of the breakup model within the Eulerian multi-fluid formulation. During the verification analysis, the model proved to give stable and consistent results in terms of the selected number of droplet classes and the selected spatial and temporal resolution. In the validation section, the capability of the developed model to predict the dynamic behavior of non-evaporating sprays is presented. It was confirmed that the developed framework could be used as a stable foundation for future fuel spray modeling.

This paper investigates, through computational fluid dynamics (CFD) simulations, the knock resistance improvements that can be obtained in a turbo-charged GDI engine with water injection. In a first step, water and gasoline injector models are validated comparing the results with experimental data from constant volume chamber tests. Then, multi-cycle simulations are performed using the G-equation turbulent combustion model focusing on spray evolution and wall film dynamics. The main intent is analyzing the effectiveness of different water injection timings and injection pressures in a port water injection (PWI) installation. Combustion rates are validated against experimental engine data, with and without water injection. Afterwards, in order to predict autoignition behavior with different spark advance (SA) timings, the extended coherent flamelet model (ECFM) combined with a tabulated kinetic ignition (TKI) dataset is used. End-gas autoignition delays are calculated using a reduced mechanism for toluene primary reference fuel (TPRF), which revealed essential for capturing actual gasoline ignition characteristics. Results indicate that the water atomization quality, i.e., injection pressure, is significant in a PWI installation allowing a reduction of the water wall film formation in the ports. Water injection timing needs also to be carefully chosen for optimized performance. As the injected water allows the SA to be increased, the overall benefits on indicated mean effective pressure and fuel consumption are quantified under the same knock safety margin, matching adequately well the available measurements.

Additive manufacturing (AM), or 3D printing processes, is introducing new possibilities in electronic, biomedical, sensor-designing, and wearable technologies. In this context, the present work focuses on the development of flexible 3D-printed polyethylene glycol diacrylate (PEGDA)- sulfonated polyaniline (PANIs) electrically conductive hydrogels (ECHs) for pH-monitoring applications. PEGDA platforms are 3D printed by a stereolithography (SLA) approach. Here, we report the successful realization of PEGDA–PANIs electroconductive hydrogel (ECH) composites produced by an in situ chemical oxidative co-polymerization of aniline (ANI) and aniline 2-sulfonic acid (ANIs) monomers at a 1:1 equimolar ratio in acidic medium. The morphological and functional properties of PEGDA–PANIs are compared to those of PEGDA–PANI composites by coupling SEM, swelling degree, I–V, and electro–chemo–mechanical analyses. The differences are discussed as a function of morphological, structural, and charge transfer/transport properties of the respective PANIs and PANI filler. Our investigation showed that the electrochemical activity of PANIs allows for the exploitation of the PEGDA–PANIs composite as an electrode material for pH monitoring in a linear range compatible with that of most biofluids. This feature, combined with the superior electromechanical behavior, swelling capacity, and water retention properties, makes PEGDA–PANIs hydrogel a promising active material for developing advanced biomedical, soft tissue, and biocompatible electronic applications.

Cuban rice cultivars INCA LP-5 and INCA LP-7 are widely distributed in Cuba and Caribbean countries. Although there are studies about rhizospheric bacteria associated with these cultivars, there are no reports about their seed-associated bacteria. This study aimed to isolate endophytic bacteria from rice seeds and select those with the greatest plant growth-promoting traits. A total of nineteen bacterial strains from the genera Pantoea, Bacillus, Paenibacillus, and Pseudomonas were isolated from the husk and endosperm of rice seeds. The strains Pantoea sp. S5-1, Pseudomonas sp. S5-38, and Pseudomonas sp. S7-1 were classified as the most promissory to increase rice growth as they demonstrated the presence of multiple plant growth-promoting traits such as the production of auxins, phosphate, and potassium solubilization, the production of siderophores, and the inhibition of the phytopathogen Pyricularia oryzae. The inoculation of strains of Pantoea sp. and Pseudomonas spp. in rice improves the height, root length, fresh weight, and dry weight of the shoot and root after 21 days post-inoculation in hydroponic assays. This study constitutes the first report on Cuban rice cultivars about the presence of endophytes in seeds and their potential to promote seedling growth. Pantoea sp. S5-1, Pseudomonas sp. S5-38, and Pseudomonas sp. S7-1 were selected as the more promising strains for the development of bio-stimulators or bio-inoculants for Cuban rice crops.

Tea Tree Oil (TTO) is an essential oil obtained from the distillation of Melaleuca alternifolia leaves and branches. Due to its beneficial properties, TTO is widely used as an active ingredient in antimicrobial preparations for topical use or in cosmetic products and contains about 100 different compounds, with terpinen-4-ol, γ-terpinene and 1,8-cineole (or eucalyptol) being the molecules most responsible for its biological activities. In this work, the antimicrobial activity of whole TTO and these three major components was evaluated in vitro against fungi, bacteria and viruses. Molecular dynamics simulations were carried out on a bacterial membrane model and a Coxsackievirus B4 viral capsid, to propose an atomistic explanation of their mechanism of action. The obtained results indicate that the strong antimicrobial activity of TTO is attributable to the induction of an altered membrane functionality, mediated by the incorporation of its components within the lipid bilayer, and to a possible ability of the compounds to bind and alter the structural properties of the viral capsid.

The increasing frequency and severity of forest fires necessitate early detection and rapid response to mitigate their impact. This project aims to design a cyber-physical system for early detection and rapid response to forest fires using advanced technologies. The system incorporates Internet of Things sensors and autonomous unmanned aerial and ground vehicles controlled by the robot operating system. An IoT-based wildfire detection node continuously monitors environmental conditions, enabling early fire detection. Upon fire detection, a UAV autonomously surveys the area to precisely locate the fire and can deploy an extinguishing payload or provide data for decision-making. The UAV communicates the fire’s precise location to a collaborative UGV, which autonomously reaches the designated area to support ground-based firefighters. The CPS includes a ground control station with web-based dashboards for real-time monitoring of system parameters and telemetry data from UAVs and UGVs. The article demonstrates the real-time fire detection capabilities of the proposed system using simulated forest fire scenarios. The objective is to provide a practical approach using open-source technologies for early detection and extinguishing of forest fires, with potential applications in various industries, surveillance, and precision agriculture.

The European Agency for Safety and Health at Work considers Smart Personal Protective Equipment as “Intelligent Protection For The Future”. It mainly consists of electronic components that collect data about their use, the workers who wear them, and the working environment. This paper proposes a distributed solution of Smart Personal Protective Equipment for the safety monitoring of Lone Workers by adopting low-cost electronic devices. In addition to the same hazards as anyone else, Lone Workers need additional and specific systems due to the higher risk they run on a work site. To this end, the Edge-Computing paradigm can be adopted to deploy an architecture embedding wearable devices, which alerts safety managers when workers do not wear the prescribed Personal Protective Equipment and supports a fast rescue when a worker seeks help or an accidental fall is automatically detected. The proposed system is a work-in-progress which provides an architecture design to accommodate different requirements, namely the deployment difficulties at temporary and large working sites, the maintenance and connectivity recurring cost issues, the respect for the workers’ privacy, and the simplicity of use for workers and their supervisors.

The demand for a wide choice of food that is safe and palatable increases every day. Consumers do not accept off-flavors that have atypical odors resulting from internal deterioration or contamination by substances alien to the food. Odor response depends on the volatile organic compounds (VOCs), and their detection can provide information about food quality. Gas chromatography/mass spectrometry is the most powerful method available for the detection of VOC. However, it is laborious, costly, and requires the presence of a trained operator. To develop a faster analytic tool, we designed a non-Faradaic impedimetric biosensor for monitoring the presence of VOCs involved in food spoilage. The biosensor is based on the use of the pig odorant-binding protein (pOBP) as the molecular recognition element. We evaluated the affinity of pOBP for three different volatile organic compounds (1-octen-3-ol, trans-2-hexen-1-ol, and hexanal) related to food spoilage. We developed an electrochemical biosensor conducting impedimetric measurements in liquid and air samples. The impedance changes allowed us to detect each VOC sample at a minimum concentration of 0.1 μM.

In this paper, we show that the evolution of artificial intelligence (AI) and its increased presence within an interactive system pushes designers to rethink the way in which AI and its users interact and to highlight users’ feelings towards AI. For novice designers, it is crucial to acknowledge that both the user and artificial intelligence possess decision-making capabilities. Such a process may involve mediation between humans and artificial intelligence. This process should also consider the mutual learning that can occur between the two entities over time. Therefore, we explain how to adapt the Human-Centered Design (HCD) process to give centrality to AI as the user, further empowering the interactive system, and to adapt the interaction design to the actual capabilities, limitations, and potentialities of AI. This is to encourage designers to explore the interactions between AI and humans and focus on the potential user experience. We achieve such centrality by extracting and formalizing a new category of AI requirements. We have provocatively named this extension: “Intelligence-Centered”. A design workshop with MsC HCI students was carried out as a case study supporting this change of perspective in design.

In recent years, studies concerning Organic Bioelectronics have had a constant growth due to the interest in disciplines such as medicine, biology and food safety in connecting the digital world with the biological one. Specific interests can be found in organic neuromorphic devices and organic transistor sensors, which are rapidly growing due to their low cost, high sensitivity and biocompatibility. This trend is evident in the literature produced in Italy, which is full of breakthrough papers concerning organic transistors-based sensors and organic neuromorphic devices. Therefore, this review focuses on analyzing the Italian production in this field, its trend and possible future evolutions.

A Disintegrin and Metalloprotease 10, also known as ADAM10, is a cell surface protease ubiquitously expressed in mammalian cells where it cuts several membrane proteins implicated in multiple physiological processes. The dysregulation of ADAM10 expression and function has been implicated in pathological conditions, including Alzheimer’s disease (AD). Although it has been suggested that ADAM10 is expressed as a zymogen and the removal of the prodomain results in its activation, other potential mechanisms for the ADAM10 proteolytic function and activation remain unclear. Another suggested mechanism is post-translational modification of the cytoplasmic domain, which regulates ADAM10-dependent protein ectodomain shedding. Therefore, the precise and temporal activation of ADAM10 is highly desirable to reveal the fine details of ADAM10-mediated cleavage mechanisms and protease-dependent therapeutic applications. Here, we present a strategy to control prodomain and cytosolic tail cleavage to regulate ADAM10 shedding activity without the intervention of small endogenous molecule signaling pathways. We generated a series of engineered ADAM10 analogs containing Tobacco Etch Virus protease (TEV) cleavage site (TEVcs), rendering ADAM10 cleavable by TEV. This strategy revealed that, in the absence of other stimuli, the TEV-mediated removal of the prodomain could not activate ADAM10. However, the TEV-mediated cleavage of the cytosolic domain significantly increased ADAM10 activity. Then, we generated ADAM10 with a minimal constitutively catalytic activity that increased significantly in the presence of TEV or after activating a chemically activatable TEV. Our results revealed a bioengineering strategy for controlling the ADAM10 activity in living cells, paving the way to obtain spatiotemporal control of ADAM10. Finally, we proved that our approach of controlling ADAM10 promoted α-secretase activity and the non-amyloidogenic cleavage of amyloid-β precursor protein (APP), thereby increasing the production of the neuroprotective soluble ectodomain (sAPPα). Our bioengineering strategy has the potential to be exploited as a next-generation gene therapy for AD.

The innate immune responses of mammals to microbial infections include strategies based on manipulating the local concentration of metals such as iron (Fe) and zinc (Zn), commonly described as nutritional immunity. To evaluate whether these strategies are also present in zebrafish embryos, we have conducted a series of heart cavity-localized infection experiments with Pseudomonas aeruginosa strains characterized by a different ability to acquire Zn. We have found that, 48 h after infection, the bacterial strains lacking critical components of the Zn importers ZnuABC and ZrmABCD have a reduced colonization capacity compared to the wild-type strain. This observation, together with the finding of a high level of expression of Zur-regulated genes, suggests the existence of antimicrobial mechanisms based on Zn sequestration. However, we have observed that strains lacking such Zn importers have a selective advantage over the wild-type strain in the early stages of infection. Analysis of the expression of the gene that encodes for a Zn efflux pump has revealed that at short times after infection, P. aeruginosa is exposed to high concentrations of Zn. At the same time, zebrafish respond to the infection by activating the expression of the Zn transporters Slc30a1 and Slc30a4, whose mammalian homologs mediate a redistribution of Zn in phagocytes aimed at intoxicating bacteria with a metal excess. These observations indicate that teleosts share similar nutritional immunity mechanisms with higher vertebrates, and confirm the usefulness of the zebrafish model for studying host–pathogen interactions.

Designing fully green materials for flexible electronics is an urgent need due to the growing awareness of an environmental crisis. With the aim of developing a sustainable, printable, and biocompatible material to be exploited in flexible electronics, the rheological, structural and charge transport properties of water-based hydroxypropyl cellulose (HPC)-detonation nanodiamond (DND) viscous dispersions are investigated. A rheological investigation disclosed that the presence of the DND affects the orientation and entanglement of cellulose chains in the aqueous medium. In line with rheological analyses, the NMR diffusion experiments pointed out that the presence of DND modifies the hydrodynamic behavior of the cellulose molecules. Despite the increased rigidity of the system, the presence of DND slightly enhances the ionic conductivity of the dispersion, suggesting a modification in the charge transport properties of the material. The electrochemical analyses, performed through Cyclic Voltammetry (CV) and Electrochemical Impedance Spectroscopy (EIS), revealed that the HPC-DND system is remarkably stable in the explored voltage range (−0.1 to +0.4 V) and characterized by a lowered bulk resistance with respect to HPC. Such features, coupled with the printability and filmability of the material, represent good requirements for the exploitation of such systems in flexible electronic applications.

Almost eighty years have passed since the publication of the studies by Arthur Schade and Leona Caroline, which we can consider as the first investigations that began to disclose the importance of metals in host–pathogen interactions [...]

Cardiovascular diseases are largely represented in patients with cancer and appear to be important side effects of cancer treatments, heavily affecting quality of life and leading to premature morbidity and death among cancer survivors. In particular, treatments for breast cancer have been shown to potentially play serious detrimental effects on cardiovascular health. This review aims to explore the available literature on breast cancer therapy-induced side effects on heart and vessels, illustrating the molecular mechanisms of cardiotoxicity known so far. Moreover, principles of cardiovascular risk assessment and management of cardiotoxicity in clinical practice will also be elucidated. Chemotherapy (anthracycline, taxanes, cyclophosphamide and 5-fluorouracil), hormonal therapy (estrogen receptor modulator and gonadotropin or luteinizing releasing hormone agonists) and targeted therapy (epidermal growth factor receptor 2 and Cyclin-dependent kinases 4 and 6 inhibitors) adverse events include arterial and pulmonary hypertension, supraventricular and ventricular arrhythmias, systolic and diastolic cardiac dysfunction and coronary artery diseases due to different and still not well-dissected molecular pathways. Therefore, cardiovascular prevention programs and treatment of cardiotoxicity appear to be crucial to improve morbidity and mortality of cancer survivors.

The RHAPS (Redox-Activity And Health-Effects Of Atmospheric Primary And Secondary Aerosol) project was launched in 2019 with the major objective of identifying specific properties of the fine atmospheric aerosol from combustion sources that are responsible for toxicological effects and can be used as new metrics for health-related outdoor pollution studies. In this paper, we present the overall methodology of RHAPS and introduce the phenomenology and the first data observed. A comprehensive physico-chemical aerosol characterization has been achieved by means of high-time resolution measurements (e.g., number size distributions, refractory chemical components, elemental composition) and low-time resolution analyses (e.g., oxidative potential, toxicological assays, chemical composition). Preliminary results indicate that, at the real atmospheric conditions observed (i.e., daily PM1 from less than 4 to more than 50 μg m−3), high/low mass concentrations of PM1, as well as black carbon (BC) and water soluble Oxidative Potential (WSOP,) do not necessarily translate into high/low toxicity. Notably, these findings were observed during a variety of atmospheric conditions and aerosol properties and with different toxicological assessments. Findings suggest a higher complexity in the relations observed between atmospheric aerosol and toxicological endpoints that go beyond the currently used PM1 metrics. Finally, we provide an outlook to companion papers where data will be analyzed in more detail, with the focus on source apportionment of PM1 and the role of source emissions on aerosol toxicity, the OP as a predictive variable for PM1 toxicity, and the related role of SOA possessing redox-active capacity, exposure-response relationships for PM1, and air quality models to forecast PM1 toxicity.

This review provides an overview of the assessment of the endocrine disrupting (ED) properties of carbon disulfide (CS2), following the methodology used at the European level to identify endocrine disruptors. Relevant in vitro, in vivo studies and human data are analyzed. The assessment presented here focuses on one endocrine activity, i.e., thyroid disruption, and two main adverse effects, neurotoxicity and cardiotoxicity. The data available on the different ED or non-ED modes of action (MoA), known to trigger these adverse effects, are described and the strength of evidence of the different MoA is weighted. We conclude that the adverse effects could be due to systemic toxicity rather than endocrine-mediated toxicity. This assessment illustrates the scientific and regulatory challenges in differentiating a specific endocrine disruption from an indirect endocrine effect resulting from a non-ED mediated systemic toxicity. This issue of evaluating the ED properties of highly toxic and reactive substances has been insufficiently developed by European guidance so far and needs to be further addressed. Finally, this example also raises questions about the capacity of the technics available in toxicology to address such a complex issue with certainty.

The PD-L1/PD-1 immune checkpoint axis is the strongest T cell exhaustion inducer. As immune dysfunction occurs during obesity, we analyzed the impact of obesity on PD-L1/PD-1 expression in white adipose tissue (WAT) in mice and in human white adipocytes. We found that PD-L1 was overexpressed in WAT of diet-induced obese mice and was associated with increased expression of PD-1 in visceral but not subcutaneous WAT. Human in vitro cocultures with adipose-tissue-derived mesenchymal stem cells (ASC) and mononuclear cells demonstrated that the presence of ASC harvested from obese WAT (i) enhanced PD-L1 expression as compared with ASC from lean WAT, (ii) decreased Th1 cell cytokine secretion, and (iii) resulted in decreased cytolytic activity towards adipocytes. Moreover, (iv) the implication of PD-L1 in obese ASC-mediated T cell dysfunction was demonstrated through PD-L1 blockade. Finally, (v) conditioned media gathered from these cocultures enhanced PD-L1 expression in freshly differentiated adipocytes, depending on IFNγ. Altogether, our results suggest that PD-L1 is overexpressed in the WAT of obese individuals during IFNγ secretion, leading to T cell dysfunction and notably reduced cytolytic activity. Such a mechanism could shed light on why adipose-tissue-infiltrating viruses, such as SARS-CoV-2, can worsen disease in obese individuals.

The ability to obtain Fe is critical for pathogens to multiply in their host. For this reason, there is significant interest in the identification of compounds that might interfere with Fe management in bacteria. Here we have tested the response of two Gram-negative pathogens, Salmonella enterica serovar Typhimurium (STM) and Pseudomonas aeruginosa (PAO1), to deferiprone (DFP), a chelating agent already in use for the treatment of thalassemia, and to some DFP derivatives designed to increase its lipophilicity. Our results indicate that DFP effectively inhibits the growth of PAO1, but not STM. Similarly, Fe-dependent genes of the two microorganisms respond differently to this agent. DFP is, however, capable of inhibiting an STM strain unable to synthesize enterochelin, while its effect on PAO1 is not related to the capability to produce siderophores. Using a fluorescent derivative of DFP we have shown that this chelator can penetrate very quickly into PAO1, but not into STM, suggesting that a selective receptor exists in Pseudomonas. Some of the tested derivatives have shown a greater ability to interfere with Fe homeostasis in STM compared to DFP, whereas most, although not all, were less active than DFP against PAO1, possibly due to interference of the added chemical tails with the receptor-mediated recognition process. The results reported in this work indicate that DFP can have different effects on distinct microorganisms, but that it is possible to obtain derivatives with a broader antimicrobial action.

The aim of the present work was to develop a green multi-platform methodology for the quantification of l-DOPA in solid-state mixtures by means of MIR and NIR spectroscopy. In order to achieve this goal, 33 mixtures of racemic and pure l-DOPA were prepared and analyzed. Once spectra were collected, partial least squares (PLS) was exploited to individually model the two different data blocks. Additionally, three different multi-block approaches (mid-level data fusion, sequential and orthogonalized partial least squares, and sequential and orthogonalized covariance selection) were used in order to simultaneously handle data from the different platforms. The outcome of the chemometric analysis highlighted the quantification of the enantiomeric excess of l-DOPA in enantiomeric mixtures in the solid state, which was possible by coupling NIR and PLS, and, to a lesser extent, by using MIR. The multi-platform approach provided a higher accuracy than the individual block analysis, indicating that the association of MIR and NIR spectral data, especially by means of SO-PLS, represents a valid solution for the quantification of the l-DOPA excess in enantiomeric mixtures.

(1) Background:The quality of neutron beams for Boron Neutron Capture Therapy (BNCT) is currently defined by its physical characteristics in air. Recommendations exist to define whether a designed beam is useful for clinical treatment. This work presents a new way to evaluate neutron beams based on their clinical performance and on their safety, employing radiobiological quantities. (2) Methods: The case study is a neutron beam for deep-seated tumors from a 5 MeV proton beam coupled to a beryllium target. Physical Figures of Merit were used to design five beams; however, they did not allow a clear ranking of their quality in terms of therapeutic potential. The latter was then evaluated based on in-phantom dose distributions and on the calculation of the Uncomplicated Tumor Control Probability (UTCP). The safety of the beams was also evaluated calculating the in-patient out-of-beam dosimetry. (3) Results: All the beams ensured a UTCP comparable to the one of a clinical beam in phantom; the safety criterion allowed to choose the best candidate. When this was tested in the treatment planning of a real patient treated in Finland, the UTCP was still comparable to the one of the clinical beam. (4) Conclusions: Even when standard physical recommendations are not met, radiobiological and dosimetric criteria demonstrate to be a valid tool to select an effective and safe beam for patient treatment.

We hereby report on a pump-probe reflectivity experiment conducted on amorphous carbon, using a 780 nm laser as a pump and a 19 nm FEL emission as probe. Measurements were performed at 50 degrees with respect to the surface normal to have an un-pumped reflectivity higher than 0.5%. A sub-10 fs time synchronization error could be obtained exploiting the nearly jitter-free capabilities of FERMI. EUV FEL-based experiments open the way to study the behaviour of a liquid carbon phase being unaffected by plasma screening.

Three different Transient Grating setups are presented, with pulsed and continuous wave probe at different wavelengths, ranging from infrared to the extreme ultra violet region. Both heterodyne and homodyne detections are considered. Each scheme introduces variations with respect to the previous one, allowing moving from classical table top laser experiments towards a new four wave mixing scheme based on free electron laser radiation. A comparison between the various setups and the first results from extreme ultra violet transient grating experiments is also discussed.

Cardiovascular diseases (CVDs) still represent the greatest burden on healthcare systems worldwide. Despite the enormous efforts over the last twenty years to limit the spread of cardiovascular risk factors, their prevalence is growing and control is still suboptimal. Therefore, the availability of new therapeutic tools that may interfere with different pathophysiological pathways to slow the establishment of clinical CVDs is important. Previously, the inhibition of neurohormonal systems, namely the renin−angiotensin−aldosterone system (RAAS) and the sympathetic nervous system, has proven to be useful in the treatment of many CVDs. Attempts have recently been made to target an additional hormonal system, that of the natriuretic peptides (NPs), which, when dysregulated, can also play a role in the development CVDs. Indeed, a new class of drug, the angiotensin receptor−neprilysin inhibitors (ARNi), has the ability to counteract the effects of angiotensin II as well as to increase the activity of NPs. ARNi have already been proven to be effective in the treatment of heart failure with reduced ejection fraction. New evidence has suggested that, in the next years, the field of ARNi application will widen to include other CVDs, such as heart failure, with preserved ejection fraction and hypertension.