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Abstract Background Despite the significant burden of alcohol use disorders (AUD), there is a large treatment gap, especially in settings and populations affected by armed conflict. A key barrier to care is the lack of contextually relevant interventions and adequately skilled human resources to deliver them. This paper describes the systematic development of the CHANGE intervention, a potentially scalable psychological intervention for people with co-existing AUD and psychological distress in conflict-affected populations, delivered by non-specialist workers (NSWs). Methods CHANGE was developed in sequential steps: (1) identifying potential treatment strategies through a meta-review and Delphi survey with international experts; (2) in-depth interviews (IDIs) with key stakeholders from the study settings in Uganda and Ukraine; and (3) three consultative workshops with international experts and experts from Uganda and Ukraine to develop a theoretical framework for the intervention informed by outputs of the Delphi and IDIs. Results In the Delphi survey, experts reached agreement on the acceptability, feasibility and potential effectiveness of the following components: identify high-risk situations, problem solving skills, assessment, handling drinking urges, communication skills, pros and cons of drinking, and identifying high-risk situations. From the IDIs we identified (a) causal attributions for using alcohol e.g., psychosocial stressors; (b) cultural norms related to alcohol consumption such as patriarchal stereotypes; and (c) coping strategies to deal with drinking problems such as distraction. The CHANGE intervention developed through the consultative workshops can be delivered in three sequential phases focussed on assessment, feedback, and information (Phase 1); providing the client with need-based skills for dealing with high-risk situations related to alcohol use (Phase 2), and relapse prevention and management (Phase 3). Conclusions CHANGE is a contextually relevant and potentially scalable treatment for co-existing AUD and psychological distress to be delivered by NSWs to conflict-affected populations. Effectiveness and cost-effectiveness of CHANGE will be tested in Uganda and Ukraine.

Background: Alcohol misuse is a significant health problem among forcibly displaced populations including male South Sudanese refugees in northern Uganda, yet interventions are sorely lacking. Developing an explanatory model of alcohol misuse in this population is the first step to designing appropriate and effective interventions. The aim of the current study was to examine alcohol misuse in this setting and to conceptualize an explanatory model of alcohol misuse. Method: A total of 57 interviews were conducted with male refugees (18+ years) with alcohol misuse (n = 17), their family members (n = 15), community and religious leaders (n = 15), and mental health and psychosocial support providers (n = 10) in Rhino Camp settlement in northern Uganda. A thematic analysis approach was used to inductively analyze the data. Results: Alcohol misuse was identified as a significant psychosocial problem among men in this setting. The adversity (e.g. trauma, loss, poverty), psychological distress (e.g. demoralization), perpetuators (e.g. ease of availability of alcohol, family conflict), and consequences of alcohol use (e.g. interpartner violence, exacerbated poverty) were identified. An explanatory model emerged from the data showing a reinforcing relationship in which adversity causes psychological distress, which leads to increased alcohol use via a number of personal, interpersonal, community and societal perpetuators, and over time increased alcohol use amplifies psychological distress which further increases alcohol misuse. Conclusions: Given the need to address alcohol misuse in this population, this explanatory model of alcohol misuse can be used to inform prevention and treatment interventions for alcohol misuse. These interventions should endeavor to address the identified determinants of alcohol misuse while being cognizant of the social and cultural considerations specific to males in this population.

In the proposed paper, an artificial neural network (ANN) algorithm is applied to predict the electronic circuit outputs of voltage signals in Industry 4.0/5.0 scenarios. This approach is suitable to predict possible uncorrected behavior of control circuits affected by unknown noises, and to reproduce a testbed method simulating the noise effect influencing the amplification of an input sinusoidal voltage signal, which is a basic and fundamental signal for controlled manufacturing systems. The performed simulations take into account different noise signals changing their time-domain trend and frequency behavior to prove the possibility of predicting voltage outputs when complex signals are considered at the control circuit input, including additive disturbs and noises. The results highlight that it is possible to construct a good ANN training model by processing only the registered voltage output signals without considering the noise profile (which is typically unknown). The proposed model behaves as an electronic black box for Industry 5.0 manufacturing processes automating circuit and machine tuning procedures. By analyzing state-of-the-art ANNs, the study offers an innovative ANN-based versatile solution that is able to process various noise profiles without requiring prior knowledge of the noise characteristics.

Background: Although interest in studies evaluating the outcomes of rotator cuff repair is steadily increasing, the results and tendon integrity after arthroscopic rotator cuff repair in elderly patients have only been minimally investigated. The aim of this study was to evaluate clinical outcomes and repair integrity in patients over 65 years of age who underwent arthroscopic repair of full-thickness rotator cuff tears. Methods: A retrospective study was conducted with the following inclusion criteria: (1) elective shoulder arthroscopy for rotator cuff repair for full-thickness posterosuperior tears; (2) age over 65 years at surgery; and (3) participation in 24 months of follow-up. Preoperatively, the range of motion (ROM) and the Constant-Murley Score (CMS) and at follow-up, the ROM, the 12-Item Short Form Survey, the American Shoulder and Elbow Surgeons, and the CMS were evaluated; an ultrasonographic assessment of tendon integrity was performed according to the adapted Sugaya classification. Results: The final sample consisted of 110 patients with an average age of 69.2 ± 3.5 years. The mean duration of nonoperative management before surgery was 2.6 ± 0.8 months. The mean period of preoperative physical therapy was 0.6 ± 0.9 months. ROM and CMS showed statistically significant improvement (all P

Abstract The evidence on the impacts of climate change on mental health and wellbeing is growing rapidly. The objective of this scoping review is to understand the extent and type of existing mental health and psychosocial interventions aimed at addressing the mental health and psychosocial impacts of climate change. A scoping review methodology was followed. MEDLINE, PsycINFO, and Web of Science databases were searched from inception to May 2022. Comprehensive gray literature search, including expert consultation, was conducted to identify interventions for which peer-reviewed academic literature may not yet be available. Data on intervention type, setting, climate stressor, mental health outcome, evaluation, and any other available details were extracted, and results were summarized narratively. Academic literature search identified 16 records and gray literature search identified a further 24 records. Altogether, 37 unique interventions or packages of interventions were identified. The interventions act at the levels of microsystem, mesosystem, exosystem, and macrosystem through diverse mechanisms. While most interventions have not been formally evaluated, promising preliminary results support interventions in low- and middle-income-country settings disproportionately affected by climate disasters. Interventions from multidisciplinary fields are emerging to reduce psychological distress and enhance mental health and wellbeing in the context of climate change. This scoping review details existing evidence on the interventions and summarizes intervention gaps and lessons learned to inform continued intervention development and scale-up interventions.

This paper discusses a methodology to improve the prevention processes of chronic diseases such as diabetes and strokes. The research motivation is to find a new methodological approach to design advanced Diagnostic and Therapeutic Care Pathways (PDTAs) based on the prediction of chronic disease using telemedicine technologies and machine learning (ML) data processing techniques. The aim is to decrease health risk and avoid hospitalizations through prevention. The proposed method defines a Process Mining Organization (PMO) model, managing risks using a PDTA structured to prevent chronic risk. Specifically, the data analysis is focused on stroke risk. First, we applied and compared the Random Forest (RF) and Gradient Boosted Trees (GBT) supervised algorithms to predict stroke risk, and then, the Fuzzy c-Means unsupervised algorithm to cluster information on the predicted results. The application of the proposed approach is able to increase the efficiency of healthcare human resources and drastically decrease care costs.

Background: The medium-term results of reverse shoulder arthroplasty (RSA) that has been performed by a single surgeon have been previously reported. The purpose of this study was to investigate the minimum 10-year clinical and radiographic outcomes of these patients. Methods: In this prospective cohort study, 27 patients were evaluated after RSA for massive rotator cuff tear with or without eccentric osteoarthritis (OA) or concentric OA with the Constant-Murley Score (CMS), range of motion (ROM), and a radiologic assessment. Results: At a mean 12-year follow-up, the CMS and ROM were significantly improved when compared with the baseline values (all P

Process mining organizatioQn (PMO) is an innovative approach based on artificial intelligence (AI) decision making suitable for designing healthcare processes for human resource (HR) organizations. The proposed work suggests some examples of PMO-based Business Process Modeling and Notation (BPMN) workflows by highlighting the advances in HR management and in risk decrease according to healthcare scenarios. Specifically proposed are different examples of “TO BE” process pipelines related to an upgrade of the organizational healthcare framework, including digital technologies and telemedicine. Important elements are provided to formulate HR management guidelines supporting PMO design. The proposed BPMN workflows are the result of different consulting actions in healthcare institutions based on the preliminary mapping of “AS IS” processes highlighting bottlenecks and needs in HR organization. A pilot experimental dataset is used to show how it is possible to apply AI algorithms providing organization corrective actions. The paper is mainly focused on discussing some validated BPMN models managing HR in the healthcare sector. The methodology is based on the application of the BPMN approach to deploy human resource organizational processes. The results show AI data-driven workflows adopted in healthcare and examples of AI fuzzy c-means outputs addressing organizational actions.

Malignant melanoma (MM) is the “great mime” of dermatopathology, and it can present such rare variants that even the most experienced pathologist might miss or misdiagnose them. Naevoid melanoma (NM), which accounts for about 1% of all MM cases, is a constant challenge, and when it is not diagnosed in a timely manner, it can even lead to death. In recent years, artificial intelligence has revolutionised much of what has been achieved in the biomedical field, and what once seemed distant is now almost incorporated into the diagnostic therapeutic flow chart. In this paper, we present the results of a machine learning approach that applies a fast random forest (FRF) algorithm to a cohort of naevoid melanomas in an attempt to understand if and how this approach could be incorporated into the business process modelling and notation (BPMN) approach. The FRF algorithm provides an innovative approach to formulating a clinical protocol oriented toward reducing the risk of NM misdiagnosis. The work provides the methodology to integrate FRF into a mapped clinical process.

The paper proposes an innovative model able to predict the output signals of resistance and capacitance (RC) low-pass filters for machine-controlled systems. Specifically, the work is focused on the analysis of the parametric responses in the time- and frequency-domain of the filter output signals, by considering a white generic noise superimposed onto an input sinusoidal signal. The goal is to predict the filter output using a black-box model to support the denoising process by means of a double-stage RC filter. Artificial neural networks (ANNs) and random forest (RF) algorithms are compared to predict the output of noisy signals. The work is concluded by defining guidelines to correct the voltage output by knowing the predictions and by adding further RC elements correcting the distorted signals. The model is suitable for the implementation of Industry 5.0 Digital Twin (DT) networks applied to manufacturing processes.

In conflict-affected settings, prevalence of alcohol use disorders (AUDs) can be high. However, limited practical information exists on AUD management in low-income settings. Using a theory of change (ToC) approach, we aimed to identify pathways influencing the implementation and maintenance of a new transdiagnostic psychological intervention (“CHANGE”), targeting both psychological distress and AUDs in humanitarian settings. Three half-day workshops in Uganda engaged 41 stakeholders to develop a ToC map. ToC is a participatory program theory approach aiming to create a visual representation of how and why an intervention leads to specific outcomes. Additionally, five semi-structured interviews were conducted to explore experiences of stakeholders that participated in the ToC workshops. Two necessary pathways influencing the implementation and maintenance of CHANGE were identified: policy impact, and mental health service delivery. Barriers identified included policy gaps, limited recognition of social determinants and the need for integrated follow-up care. Interviewed participants valued ToC’s participatory approach and expressed concerns about its adaptability in continuously changing contexts (e.g., humanitarian settings). Our study underscores ToC’s value in delineating context-specific outcomes and identifies areas requiring further attention. It emphasizes the importance of early planning and stakeholder engagement for sustainable implementation of psychological interventions in humanitarian settings.

Our aim was to examine mental health needs and access to mental healthcare services among Syrian refugees in the city of Leipzig, Germany. We conducted a cross-sectional survey with Syrian refugee adults in Leipzig, Germany in 2021/2022. Outcomes included PTSD (PCL-5), depression (PHQ-9), anxiety (GAD-7) and somatic symptom (SSS-8). Descriptive, regression and effect modification analyses assessed associations between selected predictor variables and mental health service access. The sampling strategy means findings are applicable only to Syrian refugees in Leipzig. Of the 513 respondents, 18.3% had moderate/severe anxiety symptoms, 28.7% had moderate/severe depression symptoms, and 25.3% had PTSD symptoms. A total of 52.8% reported past year mental health problems, and 48.9% of those participants sought care for these problems. The most common reasons for not accessing mental healthcare services were wanting to handle the problem themselves and uncertainty about where to access services. Adjusted Poisson regression models (n = 259) found significant associations between current mental health symptoms and mental healthcare service access (RR: 1.47, 95% CI: 1.02–2.15, p = 0.041) but significance levels were not reached between somatization and trust in physicians with mental healthcare service access. Syrian refugees in Leipzig likely experience high unmet mental health needs. Community-based interventions for refugee mental health and de-stigmatization activities are needed to address these unmet needs in Leipzig.

COVID-19 is having substantial impacts on research conduct, including clinical trials. However, there is limited research investigating the impact of the pandemic on the conduct of clinical trials and barriers to the delivery of interventions. The current study contributes to filling this gap by investigating the impacts of COVID-19 and related mitigation strategies in the context of five randomised controlled trials (RCTs) of lay-delivered psychological interventions for Syrian refugees in Jordan, Lebanon, the Netherlands, Switzerland, and Turkey. We conducted semi-structured interviews with purposively selected researchers (N = 14) across all five countries. Data were analysed using codebook thematic analysis. The trial researchers highlighted how COVID-19 has had pervasive impacts across different components of the trial including recruitment, assessment, intervention delivery, and supervision. These impacts were considered to influence the external and internal scientific validity of these trials, as well as some aspects of trial administration such as budgeting and the workforce. Various mitigation strategies to adapt to constraints imposed by pandemic responses were described by researchers, such as shifting to a remote intervention delivery and evaluation or adding COVID-19 measures to better understand the impacts of COVID-19 on outcome data. The current piece provides an account of the impacts of COVID-19 on the conduct of trials of lay-delivered psychological interventions for refugees in five countries. Our findings will be valuable for researchers testing similar interventions during COVID-19 and other public health emergencies.

The proposed paper introduces an innovative approach based on the implementation of a multi-level Decision Support System (DSS) modelling processes in the industry. Specifically, the work discusses a theoretical Process Mining (PM) DSS model gaining digital knowledge by means of logics that are able to select the best decisions. The PM model is applied to an open dataset simulating a working scenario and defining a possible safety control method based on the risk assessment. The application of the PM model provides automatic alerting conditions based on a threshold of values detected by sensors. Specifically, the PM model is applied to worker security systems characterized by the environment with a risk of emission of smoke and gases. The PM model is improved by Artificial Intelligence (AI) algorithms by strengthening information through prediction results and improving the risk analysis. An Artificial Neural Network (ANN) MultilaLayer Perceptron (MLP) algorithm is adopted for the risk prediction by achieving the good computational performance of Mean Absolute Error (MAE) of 0.001. The PM model is first sketched by the Business Process Modelling and Notation (BPMN) method, and successively executed by means of the Konstanz Information Miner (KNIME) open source tool, implementing the process-controlling risks for different working locations. The goal of the paper is to apply the theoretical PM model by means of open source tools by enhancing how the multi-level approach is useful for defining a security procedure to control indoor worker environments. Furthermore, the article describes the key variables able to control production and worker safety for different industry sectors. The presented DSS PM model also can be applied to industry processes focused on production quality.

The brain health and development implications of climate change are situated within a large and rapidly increasing body of evidence that addresses the physical and mental health impacts and implications of extreme and worsening environments. The costs to individuals and societies of negatively impacted brain development are profound – be it in the form of diagnosable developmental disability, reduced cognitive capacity, or areas of behavioral functioning. We have sought to describe the key risk domains that climate change presents with respect to healthy brain development, from the prenatal through to youth stages. Scoping review methods and an a priori search strategy were used to address the question: What are the major considerations of the peer-reviewed literature that address climate change as it relates to brain development and health from early development through to youth populations? Themes from the identified papers were charted, and findings were summarized through a consensus process. A total of 40 papers were identified in the search, spanning 2008–2022. Based on the thematic analysis, results are organized into the following nine themes: 1) heat extremes, 2) weather extremes and stress, 3) air pollution, 4) vector and waterborne illnesses, 5) malnutrition, 6) equity, 7) economic implications, 8) methods issues, and 9) responses. There is a clear consensus amongst the papers in this review suggesting that changing climate patterns and weather extremes have substantial and wide-ranging effects on developing brains. A range of responses are proposed with an emphasis upon early intervention and better data.

Introduction: Climate change is causing far-reaching yet underappreciated worsening of outcomes across the mental health and wellbeing spectrum. Despite increasing attention to the mental health impacts of climate change, an absence of a clear, cross-sectoral agenda for action has held back progress against the dual and interconnected challenges of supporting human and planetary health. This study aims to serve as an essential first step to address this gap. Methods: Harnessing the expertise of a diverse panel of 61 participants, representing 24 nationalities, this study developed and prioritized recommendations for action on climate change and mental health across the relevant sectors of research, policy, healthcare and the third sector, and used a Delphi-style methodology to examine their feasibility and importance. Results: Broadly, the prioritized recommendations highlighted the need to expand the evidence base, work collaboratively across sectors, and raise awareness. While broadly there was consensus on recommendation importance, there was greater variation in the reported feasibility of the recommendations, which differed across settings. Other common themes included the need for cultural and resource contextualization, raising awareness of and addressing mental health co-benefits via climate action, and working with communities with lived experience to develop and implement the findings. As there may be some interdependencies between the recommendations, further work needs to identify how best to implement them. Conclusion: The recommendations serve as a robust and evidence-based framework that can be used as a foundation to devise locally appropriate, concrete implementation strategies matching levels of need and resource. These also serve as a clear call to action for investment from leaders across sectors to ensure they are realized.

Abstract Background Adolescents in low-resource urban settings in Brazil are often exposed to high levels of trauma that can result in post-traumatic stress disorder (PTSD). However, preliminary evidence indicates that PTSD tends to be under-reported in Brazilian health services, despite the high prevalence of trauma. Additionally, little is known about the perceived applicability among clinicians of the new ICD-11 diagnosis of complex PTSD (CPTSD), despite its potential relevance for contexts of chronic trauma. The current study investigated local understandings of PTSD and CPTSD among health professionals working with adolescents in violent neighbourhoods of São Paulo city. Methods Semi-structured interviews were conducted with 58 health professionals working at both the primary care and specialized mental health levels in two areas of São Paulo city with high levels of community violence. Results Most participants knew about PTSD, but most did not know about CPTSD. There were mixed views concerning the commonality of PTSD among adolescents exposed to community violence. Many participants reported having no experience working with patients with the PTSD diagnosis. According to some, community violence was normalized by adolescents and health professionals, and did not result in PTSD. Others highlighted how they did not use psychiatric diagnoses in their practice, had critical perspectives towards psychiatric diagnoses and/or PTSD, or simply knew little about PTSD. Furthermore, many highlighted how the chronic nature of multiple traumas experienced by adolescents often resulted in complex clinical presentations characterised by many symptoms beyond PTSD. The diagnosis of CPTSD was considered appropriate to the context by many participants as it captured the complex traumatic histories and symptom presentations of adolescents exposed to community violence in Brazil. Conclusions These findings have important implications for the assessment and treatment of mental health among adolescents exposed to community violence in Brazil.

This study analyzes the determinants of the "Broadband Price Index" in Europe. The data used refers to 28 European countries between 2016 and 2021. The database used is the Digital, Economy, and Society Index-DESI of the European Commission. The data were analyzed using the following econometric techniques, namely Panel Data with Random Effects, Panel Data with Fixed Effects, Pooled OLS, WLS and Dynamic Panel. The value of the "Broadband Price Index" is positively associated with the DESI Index and "Connectivity", while it is negatively associated with "Fixed Broadband Take Up", "Fixed Broadband Coverage", "Mobile Broadband", "e-Government", "Advanced Skills and Development", "Integration of Digital Technology", "At Least Basic Digital Skills", "Above Basic Digital Skills", and "At Least Basic Software Skills". A cluster analysis was carried out below using the k-Means algorithm optimized with the Silhouette coefficient. The analysis revealed the existence of three clusters. Finally, an analysis of the machine learning algorithms was carried out to predict the future value of the "Broadband Price Index". The result shows that the most useful algorithm for prediction is the Artificial Neural Network-ANN, with an estimated value equal to an amount of 9.21%. Doi: 10.28991/HEF-2022-03-01-03 Full Text: PDF

We here investigated the anti-inflammatory activity of a polymethoxylated flavone-containing fraction (PMFF) from Citrus sinensis and of a prenylflavonoid-containing one (PFF) from Humulus lupulus, either alone or in combination (MIX). To this end, an in vitro model of inflammatory bowel disease (IBD), consisting of differentiated, interleukin (IL)-1β-stimulated Caco-2 cells, was employed. We demonstrated that non-cytotoxic concentrations of either PMFF or PFF or MIX reduced nitric oxide (NO) production while PFF and MIX, but not PMFF, also inhibited prostaglandin E2 release. Coherently, MIX suppressed both inducible NO synthase and cyclooxygenase-2 over-expression besides NF-κB activation. Moreover, MIX increased nuclear factor erythroid 2–related factor 2 (Nrf2) activation, heme oxygenase-1 expression, restoring GSH and reactive oxygen and nitrogen species (RONs) levels. Remarkably, these effects with MIX were stronger than those produced by PMFF or PFF alone. Noteworthy, nobiletin (NOB) and xanthohumol (XTM), two of the most represented phytochemicals in PMFF and PFF, respectively, synergistically inhibited RONs production. Overall, our results demonstrate that MIX enhances the anti-inflammatory and anti-oxidative effects of the individual fractions in a model of IBD, via a mechanism involving modulation of NF-κB and Nrf2 signalling. Synergistic interactions between NOB and XTM emerge as a relevant aspect underlying this evidence.

Background Climate change is having significant impacts on health and mental health across Europe and globally. Such effects are likely to be more severe in climate change hotspots such as the Mediterranean region, including Italy. Objective To review existing literature on the relationship between climate change and mental health in Italy, with a particular focus on trauma and PTSD. Methods A scoping review methodology was used. We followed guidance for scoping reviews and the PRISMA Extension for Scoping Reviews (PRISMA-ScR) checklist. We searched for literature in MEDLINE, Global Health, Embase and PsycINFO. Following screening, data was extracted from individual papers and a quality assessment was conducted. Given the heterogeneity of studies, findings were summarized narratively. Results We identified 21 original research articles investigating the relationship between climate change and mental health in Italy. Climate change stressors (heat and heatwaves in particular) were found to have several negative effects on various mental health outcomes, such as a higher risk of mortality among people with mental health conditions, suicide and suicidal behaviour and psychiatric morbidity (e.g. psychiatric hospitalization and symptoms of mental health conditions). However, there is little research on the relationship between climate change and trauma or PTSD in the Italian context. Conclusions More attention and resources should be directed towards understanding the mental health implications of climate change to prevent, promote, and respond to the mental health needs of Italy and the wider Mediterranean region. HIGHLIGHTS • Climate change stressors in Italy were found to have detrimental impacts on various mental health outcomes, such as psychiatric mortality and morbidity. • Little research on the relationship between climate change stressors and PTSD exists in Italy.

Background: Disasters can have long-lasting impacts on mental health. Intrusive memories have been found to be common and persistent in the aftermath of earthquakes. Objective: To explore, using diaries, intrusive memories’ presence, content, characteristics, and relationship with probable post-traumatic stress disorder (PTSD) in a small rural community exposed to mass destruction and loss of life. Methods: Survivors of the 2016–2017 Central Italy earthquakes (N = 104) were first interviewed to investigate the presence of intrusive memories of the disaster. Those that reported intrusive memories were subsequently asked to complete a 7-day paper-and-pen diary tracking their spontaneous memories of the earthquake events. Results: Twenty months after the earthquakes, 49% (n = 51) of the sample reported having experienced intrusive memories post-earthquake and 38% (n = 39) reported at least one intrusive memory in their diaries. Memories were rated as being distressing, vivid, and experienced as a mixture of images and thoughts. The content of intrusive memories generally focused on sensations and experiences during the earthquake. Other common categories of content were the material environment and physical objects as well as human loss & death. Several memories had a social focus. A minority of memories contained more positive content as well as content from before and after the earthquake. Some participants (28%) experienced repeated intrusive memories of the same content. Memories of participants with and without probable PTSD did not significantly differ on characteristics or content. Conclusions: Intrusive memories can be common, distressing, and persistent occurrences following disasters, even in survivors not suffering from probable PTSD. Highlights Intrusive memories were common, distressing, and vivid more than 1-year post-disaster. They captured peri-earthquake sensations, material destruction, death, and social interactions. No difference in content or characteristics was found between participants with and without probable PTSD.

This review will focus on advances in electronic and optoelectronic technologies by through the analysis of a full research and industrial application scenario. Starting with the analysis of nanocomposite sensors, and electronic/optoelectronic/mechatronic systems, the review describes in detail the principles and the models for finding possible implementations of Industry 5.0 applications. The study then addresses production processes and advanced detection systems integrating Artificial Intelligence (AI) algorithms. Specifically, the review introduces new research topics in Industry 5.0 about AI self-adaptive systems and processes in electronics, robotics and production management. The paper proposes also new Business Process Modelling and Notation (BPMN) Process Mining (PM) workflows, and a simulation of a complex Industry 5.0 manufacturing framework. The performed simulation estimates the diffusion heat parameters of a hypothesized production-line layout, describing the information flux of the whole framework. The simulation enhances the technological key elements, enabling an industrial upscale in the next digital revolution. The discussed models are usable in management engineering and informatics engineering, as they merge the perspectives of advanced sensors with Industry 5.0 requirements. The goal of the paper is to provide concepts, research topics and elements to design advanced production network in manufacturing industry.

This paper merges new research topics in Industry 5.0 using the Business Process Modeling and Notation (BPMN) approach able to integrate Artificial Intelligence (AI) in production processes. The goal is to provide an innovative approach to model production management in industry, adopting a new “proof of concept” of advanced Process Mining (PM) automatizing decisions and optimizing machine setting and maintenance interventions. Advanced electronic sensing and actuation systems, integrating supervised and unsupervised AI algorithms, are embedded in the PM model as theoretical process workflows suggested by a Decision Support System (DSS) engine enabling an intelligent decision-making procedure. The paper discusses, as examples, two theoretical models applied to specific industry sectors, such as food processing and energy production. The proposed work provides important elements of engineering management related to the digitalization of production process matching with automated control systems setting production parameters, thus enabling the self-adapting of product quality supervision and production efficiency in modern industrial systems.

Aquaporin-9 (AQP9) is a facilitator of glycerol and other small neutral solute transmembrane diffusion. Identification of specific inhibitors for aquaporin family proteins has been difficult, due to high sequence similarity between the 13 human isoforms, and due to the limited channel surface areas that permit inhibitor binding. The few AQP9 inhibitor molecules described to date were not suitable for in vivo experiments. We now describe the characterization of a new small molecule AQP9 inhibitor, RG100204 in cell-based calcein-quenching assays, and by stopped-flow light-scattering recordings of AQP9 permeability in proteoliposomes. Moreover, we investigated the effects of RG100204 on glycerol metabolism in mice. In cell-based assays, RG100204 blocked AQP9 water permeability and glycerol permeability with similar, high potency (~5 × 10−8 M). AQP9 channel blocking by RG100204 was confirmed in proteoliposomes. After oral gavage of db/db mice with RG100204, a dose-dependent elevation of plasma glycerol was observed. A blood glucose-lowering effect was not statistically significant. These experiments establish RG100204 as a direct blocker of the AQP9 channel, and suggest its use as an experimental tool for in vivo experiments on AQP9 function.

Chronic hepatitis (CH) of dysmetabolic or viral etiology has been associated with poor prognosis in patients who experienced the severe acute respiratory coronavirus virus-2 (SARS-Cov-2) infection. We aimed to explore the impact of SARS-Cov-2 infection on disease severity in a group of patients with CH. Forty-two patients with CH of different etiology were enrolled (median age, 56 years; male gender, 59%). ACE2 and TMPRSS2 were measured in plasma samples of all patients by ELISA and in the liver tissue of a subgroup of 15 patients by Western blot. Overall, 13 patients (31%) experienced SARS-Cov-2 infection: 2/15 (15%) had CHB, 5/12 (39%) had CHC, and 6/15 (46%) had non-alcoholic fatty liver disease (NAFLD). Compared to viral CH patients, NAFLD subjects showed higher circulating ACE2 levels (p = 0.0019). Similarly, hepatic expression of ACE2 was higher in subjects who underwent SARS-Cov-2 infection compared to the counterpart, (3.24 ± 1.49 vs. 1.49 ± 1.32, p = 0.032). Conversely, hepatic TMPRSS2 was significantly lower in patients who experienced symptomatic COVID-19 disease compared to asymptomatic patients (p = 0.0038). Further studies are necessary to understand the impact of COVID-19 in patients with pre-existing liver diseases.

The application of artificial intelligence (AI) algorithms in medicine could support diagnostic and prognostic analyses and decision making. In the field of dermatopathology, there have been various papers that have trained algorithms for the recognition of different types of skin lesions, such as basal cell carcinoma (BCC), seborrheic keratosis (SK) and dermal nevus. Furthermore, the difficulty in diagnosing particular melanocytic lesions, such as Spitz nevi and melanoma, considering the grade of interobserver variability among dermatopathologists, has led to an objective difficulty in training machine learning (ML) algorithms to a totally reliable, reportable and repeatable level. In this work we tried to train a fast random forest (FRF) algorithm, typically used for the classification of clusters of pixels in images, to highlight anomalous areas classified as melanoma “defects” following the Allen–Spitz criteria. The adopted image vision diagnostic protocol was structured in the following steps: image acquisition by selecting the best zoom level of the microscope; preliminary selection of an image with a good resolution; preliminary identification of macro-areas of defect in each preselected image; identification of a class of a defect in the selected macro-area; training of the supervised machine learning FRF algorithm by selecting the micro-defect in the macro-area; execution of the FRF algorithm to find an image vision performance indicator; and analysis of the output images by enhancing lesion defects. The precision achieved by the FRF algorithm proved to be appropriate with a discordance of 17% with respect to the dermatopathologist, allowing this type of supervised algorithm to be nominated as a help to the dermatopathologist in the challenging diagnosis of malignant melanoma.

Complex energy monitoring and control systems have been widely studied as the related topics include different approaches, advanced sensors, and technologies applied to a strongly varying amount of application fields. This paper is a systematic review of what has been done regarding energy metering system issues about (i) sensors, (ii) the choice of their technology and their characterization depending on the application fields, (iii) advanced measurement approaches and methodologies, and (iv) the setup of energy Key Performance Indicators (KPIs). The paper provides models about KPI estimation, by highlighting design criteria of complex energy networks. The proposed study is carried out to give useful elements to build models and to simulate in detail energy systems for performance prediction purposes. Some examples of energy complex KPIs based on the integration of the Artificial Intelligence (AI) concept and on basic KPIs or variables are provided in order to define innovative formulation criteria depending on the application field. The proposed examples highlight how modeling a complex KPI as a function of basic variables or KPIs is possible, by means of graph models of architectures.

In this work, a machine learning methodology is used to predict the progress of the glycemic values of six patients with diabetes. Eight different algorithms are compared i.e., ANN, PNN, Polynomial Regression, Gradient Boosted Trees Regression, Random Forest Regression, Simple Regression Tree, Tree Ensemble Regression, Linear Regression. The algorithms are classified based on the ability to minimize four statistical errors, namely: Mean Absolute Error, Mean Squared Error, Root Mean Squared Error, Mean Signed Difference. Following the analysis, an ordering of the algorithms by predictive efficiency is proposed. Data are collected within the “Smart District 4.0 Project” with the contribution of the Italian Ministry of Economic Development.