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This document illustrates the results of the work of two interdisciplinary and multistakeholder panels (researchers, public institutions, and industry representatives) on drug territorialization and digitalization, organized as part of a residential seminar held on 30 September and 1st October 2021. Arising from some considerations about the demand for health and the provisions of the National Recovery and Resilience Plan (PNRR), the discussion touched various aspects of managing the transition from current to future management models. The importance of identifying criteria for prioritizing interventions in the area emerged: different methods of drug delivery, scientific information and measurement, re-evaluation of pathologies that can be managed in this area. Finally, the role of digitization within this change was explored. The opinions provided by the experts move towards making the most of the opportunities arising from PNRR, in terms of investments in healthcare and data application, with a view to improve health system efficiency, patient care and related outcomes.

This document illustrates the results of the work of two interdisciplinary and multistakeholder panels (researchers, public institutions, and industry representatives) on drug territorialization and digitalization, organized as part of a residential seminar held on 30 September and 1st October 2021. Arising from some considerations about the demand for health and the provisions of the National Recovery and Resilience Plan (PNRR), the discussion touched various aspects of managing the transition from current to future management models. The importance of identifying criteria for prioritizing interventions in the area emerged: different methods of drug delivery, scientific information and measurement, re-evaluation of pathologies that can be managed in this area. Finally, the role of digitization within this change was explored. The opinions provided by the experts move towards making the most of the opportunities arising from PNRR, in terms of investments in healthcare and data application, with a view to improve health system efficiency, patient care and related outcomes.

Maintenance strategies such as condition-based maintenance and predictive maintenance of machines have gained importance in industrial automation firms as key concepts in Industry 4.0. As a result, online condition monitoring of electromechanical systems has become a crucial task in many industrial applications. Motor current signature analysis (MCSA) is an interesting noninvasive alternative to vibration analysis for the condition monitoring and fault diagnosis of mechanical systems driven by electric motors. The MCSA approach is based on the premise that faults in the mechanical load driven by the motor manifest as changes in the motor’s current behavior. This paper presents a novel data-driven, MCSA-based CM approach that exploits autoregressive (AR) spectral estimation. A multiresolution analysis of the raw motor currents is first performed using the discrete wavelet transform with Daubechies filters, enabling the separation of noise, disturbances, and variable torque effects from the current signals. AR spectral estimation is then applied to selected wavelet details to extract relevant features for fault diagnosis. In particular, a reference AR power spectral density (PSD) is estimated using data collected under healthy conditions. The AR PSD is then continuously or periodically updated with new data frames and compared to the reference PSD through the Symmetric Itakura–Saito spectral distance (SISSD). The SISSD, which serves as the health indicator, has proven capable of detecting fault occurrences through changes in the AR spectrum. The proposed procedure is tested on real data from two different scenarios: (i) an experimental in-house setup where data are collected during the execution of electric cam motion tasks (imbalance faults are emulated); (ii) the Korea Advanced Institute of Science and Technology testbed, whose data set is publicly available (bearing faults are considered). The results demonstrate the effectiveness of the method in both fault detection and isolation. In particular, the proposed health indicator exhibits strong detection capabilities, as its values under fault conditions exceed those under healthy conditions by one order of magnitude.

BackgroundSince 2012, Associating Liver Partition and Portal vein ligation for Staged hepatectomy (ALPPS) has encountered several modifications of its original technique. The primary endpoint of this study was to analyze the trend of ALPPS in Italy over a 10-year period. The secondary endpoint was to evaluate factors affecting the risk of morbidity/mortality/post-hepatectomy liver failure (PHLF). MethodsData of patients submitted to ALPPS between 2012 and 2021 were identified from the ALPPS Italian Registry and evaluation of time trends was performed.ResultsFrom 2012 to 2021, a total of 268 ALPPS were performed within 17 centers. The number of ALPPS divided by the total number of liver resections performed by each center slightly declined (APC = − 2.0%, p = 0.111). Minimally invasive (MI) approach significantly increased over the years (APC = + 49.5%, p = 0.002). According to multivariable analysis, MI completion of stage 1 was protective against 90-day mortality (OR = 0.05, p = 0.040) as well as enrollment within high-volume centers for liver surgery (OR = 0.32, p = 0.009). Use of interstage hepatobiliary scintigraphy (HBS) and biliary tumors were independent predictors of PHLF. ConclusionsThis national study showed that use of ALPPS only slightly declined over the years with an increased use of MI techniques, leading to lower 90-day mortality. PHLF still remains an open issue.

The aim of the present study was to test an explanatory model for individual and social wellbeing which incorporates the advantages of using digital technologies during the COVID-19 pandemic. The study was carried out in Italy, one of the countries that has been most severely affected by the pandemic worldwide. The study was designed to include variables that might be specifically pertinent to the uniqueness of the restrictions imposed by the pandemic. Adults living in Italy (n = 1412) completed an online survey during the lockdown period in March 2020. Results showed two distinct digital interaction processes highlighted by the facilitating use of online emotions (“e-motions”) and online social support (“e-support”). In short, e-motions were positively related to posttraumatic growth, which in turn was positively associated with positive mental health and higher engagement in prosocial behaviors. Moreover, individuals who perceived themselves as having greater e-support were characterized by higher levels of positive mental health, which it turn was positively associated with prosocial behaviors. Collectively, these two digital interaction processes suggest that digital technologies appear to be critical resources in helping individuals cope with difficulties raised by the COVID-19 pandemic.

In recent years, the expansion of the illicit market for Novel Psychoactive Substances (NPS) has resulted in the emergence of numerous synthetic recreational drugs specifically designed to evade legal control and analytical detection. Among these, nitazenes represent one of the most potent classes of new synthetic opioids, although information regarding their toxicological properties remains limited. The present study aimed to assess the genotoxic potential of four nitazenes: clonitazene, etonitazene, isotonitazene and metonitazene in human lymphoblastoid TK6 cells using a flow cytometric version of the In Vitro Mammalian Cell Micronucleus Test, following OECD Guideline No. 487. Cells were exposed to concentrations ranging from 12.5 to 100 μM, and cytotoxicity, cytostasis, and apoptosis were evaluated to identify appropriate doses for micronucleus frequency assessment. Vinblastine, a well-established mutagen, was included as positive control. Our findings demonstrated that clonitazene and isotonitazene exhibit mutagenic potential, suggesting an increased long-term risk of developing chronic degenerative diseases. Furthermore, the results revealed that structurally related molecules can induce markedly different cellular effects, underscoring the importance of compound-specific toxicological evaluations to achieve a comprehensive understanding of the risks associated with their illicit use-risks often presumed to involve only addiction or acute toxicity.

Three fentanyl analogues Acrylfentanyl, Ocfentanyl and Furanylfentanyl are potent, rapid-acting synthetic analgesics that recently appeared on the illicit market of new psychoactive substances (NPS) under the class of new synthetic opioids (NSO). Pharmacotoxicological data on these three non-pharmaceutical fentanyl analogues are limited and studies on their genotoxicity are not yet available. Therefore, the aim of the present study was to investigate this property. The ability to induce structural and numerical chromosomal aberrations in human lymphoblastoid TK6 cells was evaluated by employing the flow cytometric protocol of the in vitro mammalian cell micronucleus test. Our study demonstrated the non-genotoxicity of Fentanyl, i.e., the pharmaceutical progenitor of the class, while its illicit non-pharmaceutical analogues were found to be genotoxic. In particular, Acrylfentanyl led to a statistically significant increase in the MNi frequency at the highest concentration tested (75 μM), while Ocfentanyl and Furanylfentnyl each did so at both concentrations tested (150, 200 μM and 25, 50 μM, respectively). The study ended by investigating reactive oxygen species (ROS) induction as a possible mechanism linked to the proved genotoxic effect. The results showed a non-statistically significant increase in ROS levels in the cultures treated with all molecules under study. Overall, the proved genotoxicity raises concern about the possibility of serious long-term consequences.

Since 2019, a growing number of structurally diverse, non-Fentanyl-related novel synthetic opioids (NSOs) have emerged, but little is still known on the toxic profile of several of the molecules belonging to this class. Regarding long-term toxicity, few studies have investigated the genotoxic potential of NSOs, and no genotoxic data at all are available for the subclass of Brorphine-like benzimidazolone opioids. To deepen and broaden our understanding of their toxicological profile, this study was aimed at evaluating the genotoxicity of Brorphine and four of its analogues (Orphine, Fluorphine, Chlorphine and Iodorphine) on human lymphoblastoid TK6 cells employing a flow cytometric protocol of the “In Vitro Mammalian Cell Micronucleus (MN) test”. The results show a statistically significant MNi increase for Fluorphine, Chlorphine and Iodorphine, but not for Brorphine and Orphine, demonstrating for the first three the ability to induce chromosomal damage. Afterwards, Brorphine and Orphine were tested on TK6 cells also in the presence of an exogenous metabolic activation system (S9 mix) to consider the possible genotoxic hazard posed by their metabolites as well. Also, under this experimental condition, no statistically significant increase in the MNi frequency was detected.

Thermal comfort has become a topic issue in building performance assessment as well as energy efficiency. Three methods are mainly recognized for its assessment. Two of them based on standardized methodologies, face the problem by considering the indoor environment in steady-state conditions (PMV and PPD) and users as active subjects whose thermal perception is influenced by outdoor climatic conditions (adaptive approach). The latter method is the starting point to investigate thermal comfort from an overall perspective by considering endogenous variables besides the traditional physical and environmental ones. Following this perspective, the paper describes the results of an in-field investigation of thermal conditions through the use of nearable and wearable solutions, parametric models and machine learning techniques. The aim of the research is the exploration of the reliability of IoT-based solutions combined with advanced algorithms, in order to create a replicable framework for the assessment and improvement of user thermal satisfaction. For this purpose, an experimental test in real offices was carried out involving eight workers. Parametric models are applied for the assessment of thermal comfort; IoT solutions are used to monitor the environmental variables and the users’ parameters; the machine learning CART method allows to predict the users’ profile and the thermal comfort perception respect to the indoor environment.