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Training in the Occupational Safety and Health (OSH) sector is crucial for minimizing workplace hazards and ensuring employee well-being. Virtual Reality (VR) emerges as a training tool that can enhance learning outcomes and simulate hazardous scenarios safely. However, several aspects must be taken into consideration when implementing VR-based training solutions. The paper investigates how to effectively design, develop, integrate, and validate a VR OSH training tool. To this aim, a comprehensive guideline of 9 key elements articulated in 29 items is proposed. Every element and item is retrieved from analyzing the existing literature on the topic using a systematic approach. The result is a comprehensive guide to consider all these aspects from the outset of design, in a cohesive, complete, and tailored manner. This formalization is intended to facilitate the advancement of research and implementation of these solutions, which to date have been largely confined to prototypes or lack real practical application.

The gut microbiota (GM) plays a crucial role in human health. The bidirectional interaction between GM and the central nervous system may occur via the microbiota–gut–brain axis, possibly regulating the sleep/wake cycle. Recent reports highlight associations between intestinal dysbiosis and sleep disorders, suggesting that probiotics could ameliorate this condition. However, data are poor and inconsistent. The aim of this quantitative metanalytic study is to assess the GM composition in sleep disturbances and evaluate probiotics’ effectiveness for managing sleep disorders. A systematic review was carried out until July 2022 in online databases, limiting the literature research to human studies and English language articles. No significant GM diversity between patients with sleep disturbances versus healthy controls was found, revealed by α-diversity, while β-diversity is missing due to lack of proper reporting. However, probiotics supplementation significantly reduced the self-assessed parameter of sleep quality and disturbances Pittsburgh Sleep Quality Index (PSQI) score compared with the placebo. No difference in the Epworth Sleepiness Scale (ESS) score was found. While available data suggest that GM diversity is not related to sleep disturbances, probiotics administration strongly improves sleep quality as a subjective perception. However, heterogeneity of data reporting in the scientific literature should be considered as a limitation.

Tirzepatide (TZP) is a new anti-obesity drug, and little is currently known about its effect on body composition (BC) in people with overweight or obesity. The aim of this study is to conduct a systematic review on the impact of TZP on BC compartments in this population during weight loss programs. Literature searches, study selection, method development, and quality appraisal were performed. The data were synthesized using a narrative approach, in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. Of the 1379 papers retrieved, 6 randomized controlled trials met the inclusion criteria and were reviewed, revealing the following findings. Firstly, TZP was shown to result in a significant reduction in total fat mass (FM), visceral adipose tissue (VAT) and waist circumference (WC) between baseline and short as well as intermediate follow-ups. Compared to other anti-obesity medications (e.g., dulaglutide and semaglutide) taken over the same duration, TZP showed a superior decrease in body fat compartments (i.e., total FM, VAT and WC). Finally, the effect of TZP on fat-free mass (FFM) is still uncertain because the findings remain inconclusive. In conclusion, TZP appears to be an effective strategy for achieving significant improvements in body fat and its distribution, but additional investigations are still needed to determine the impact of TZP on lean mass in this population.

Abstract Purpose: To assess the safety and efficacy of subthreshold micropulse laser (SML) photo-stimulation in the management of persistent subfoveal fluid (PSF) after surgery for rhegmatogenous retinal detachment (RRD). Methods: In this pilot study, 11 eyes of 11 patients (8 men, 3 women) with long-lasting (12–18 months) PSF after surgery for RRD were evaluated before and after photostimulation with subthreshold micropulse yellow laser. Ophthalmic examination included best-corrected visual acuity (BCVA), Amsler grid test, ophthalmoscopy, autofluorescence (AF), and optical coherence tomography (OCT) with measurement of central point foveal thickness (CPFT). Primary outcome was subfoveal fluid resolution and secondary outcome was BCVA improvement. Results: The mean CPFT and BCVA were, respectively, 436.8 ± 28.8 μm and 0.25 ± 0.1 µm decimal equivalent (DE) before photostimulation and 278 ± 54.4 μm and 0.57 ± 0.2 µm DE after photostimulation, a statistically significant difference (P < 0.001). Nine (81.8%) eyes showed improved BCVA, disappearance of macular detachment on ophthalmoscopy, reduced retinal pigment epithelium distress on AF, and restored macular profile with no neuroretinal alterations on OCT scans. Conclusion: Although PSF after RRD surgery is often a self-limiting disease, our results suggest that SML photostimulation may be effective and safe in patients with clinically significant long-lasting PSF. Larger case–control studies are necessary to confirm these results.

Kabuki syndrome (KS) is a rare multisystemic disease due to mutations in the KMT2D or KDM6A genes, which act as epigenetic modulators of different processes, including immune response. The syndrome is characterized by anomalies in multiple organ systems, and it is associated with autoimmune and inflammatory disorders, and an underlying immunological phenotype characterized by immunodeficiency and immune dysregulation. Up to 17% of KS patients present with immune thrombocytopenia characterized by a severe, chronic or relapsing course, and often associated to other hematological autoimmune diseases including autoimmune hemolytic anemia, eventually resulting in Evans syndrome (ES). A 23-year-old woman, clinically diagnosed with KS and presenting from the age of 3 years with ES was referred to the Rare Diseases Centre of our Pediatric Department for corticosteroid-induced hyperglycemia. Several ES relapses and recurrent respiratory infections in the previous years were reported. Severe hypogammaglobulinemia, splenomegaly and signs of chronic lung inflammation were diagnosed only at the time of our observation. Supportive treatment with amoxicillin-clavulanate prophylaxis and recombinant human hyaluronidase-facilitated subcutaneous immunoglobulin replacement were immediately started. In KS patients, the failure of B-cell development and the lack of autoreactive immune cells suppression can lead to immunodeficiency and autoimmunity that may be undiagnosed for a long time. Our patient's case is paradigmatic since she presented with preventable morbidity and severe lung disease years after disease onset. This case emphasizes the importance of suspecting immune dysregulation in KS. Pathogenesis and immunological complications of KS are discussed. Moreover, the need to perform immunologic evaluations is highlighted both at the time of KS diagnosis and during disease follow-up, in order to allow proper treatment while intercepting avoidable morbidity in these patients.

Abstract Background COVID toes or chilblain-like skin lesions have been widely reported during COVID-19 pandemic. Most cases were described in patients with negative microbiological tests for SARS-CoV-2, therefore the possible relationship with SARS-CoV-2 infection, as well as with the nowadays broadly available mRNA-based vaccination, has not been fully elucidated. Case presentation We here describe the case of a 14-year-old male who developed chilblain-like skin eruptions during SARS-CoV-2 infection despite two mRNA-based vaccine doses and review the clinical and epidemiological characteristics of chilblain-like lesions as a cutaneous presentation of COVID-19 in children. Conclusions Most children and adolescent with COVID toes have a mild or asymptomatic SARS-CoV-2 infection. Our report aims to highlight the possible onset of these skin lesions in vaccinated children, if infection has occurred, and the potential use of systemic corticosteroids as a first line treatment. Additional evidence is required to better understand SARS-CoV-2 infection and cutaneous manifestations in children and determine the relationship between chilblain-like lesions and COVID-19 vaccination.

Background: Endogenous and exogenous factors impact the operational characteristics of supply chains, affecting wholesale warehouses. The survival of a warehouse is often threatened by disruptive events that alter infrastructure and performance. The emergence of COVID-19 exemplified the need for adaptability in retail goods supply chains, emphasizing the necessity for responding to external shocks. Methods: The MARLIN (Method wArehouse ResiLience dIstruptioN) method, founded on theories and models of resilience engineering is introduced. MARLIN is a practical tool designed to identify key areas requiring intervention in response to disruptive events. An empirical test was conducted in an Italian warehouse. Results: The conducted test yielded tangible results, demonstrating the efficacy of the method. It successfully pinpointed areas necessitating intervention and identified Key Performance Indicators (KPIs) associated with disruptions. The study not only underscores the importance of data collection but also highlights the often-overlooked significance of warehouse management. Conclusions: The study establishes MARLIN as a valuable asset for stakeholders involved in disruption management. Its application has proven instrumental in recognizing areas of intervention and identifying KPIs related to disruptions. Ongoing research endeavors to broaden its applicability across diverse supply chain scenarios, aiming to enhance situational awareness and enable proactive risk assessment through what-if analysis.

The accessibility of advanced Artificial Intelligence-based tools, like ChatGPT, has made Large Language Models (LLMs) readily available to students. These LLMs can generate original written content to assist students in their academic assessments. With the rapid adoption of LLMs, exemplified by the popularity of OpenAI's ChatGPT, there is a growing need to explore their application in education. Few studies examine students' use of LLMs as learning tools. This paper focuses on the application of ChatGPT in engineering higher education through an in-depth case study. It investigates whether engineering students can generate high-quality university essays with LLMs assistance, whether existing LLMs identification systems can detect essays produced with LLMs, and how students perceive the usefulness and acceptance of LLMs in learning. The research adopts a deductive/inductive approach, combining conceptualization and empirical evidence analysis. The study involves mechanical and management engineering students, who compose essays using LLMs. The essay assessment showed good results, but some recommendations emerged for teachers and students. Thirteen LLMs detectors were tested without achieving satisfactory results, suggesting to avoid LLMs ban. In addition, students were administered a questionnaire based on constructs and items that follow the technology acceptance models available in the literature. The results contribute to qualitative evidence by highlighting possible future research and educational practices.

Objective: Early diagnosis of syndromic monogenic diabetes allows for proper management and can lead to improved quality of life in the long term. This report aimed to describe 2 genetically confirmed cases of Wolfram syndrome, a rare endoplasmic reticulum disorder characterized by insulin-dependent diabetes mellitus, optic nerve atrophy, and progressive neurodegeneration. Case Report: A 16-year-old Caucasian male patient and a 25-year-old Caucasian female patient with a history of diabetes mellitus and optic nerve atrophy presented at our medical center. Both patients were initially diagnosed with type 1 diabetes but negative for islet autoantibodies. Their body mass indexes were under 25 at the diagnosis. Their history and presentation were highly suspicious for Wolfram syndrome. Discussion: The genetic tests revealed a known Wolfram syndrome 1 (WFS1) pathogenic variant (homozygous) in the 16-year-old male patient and 2 known WFS1 pathogenic variants (compound heterozygous) in the 25-year-old female patient with diabetes mellitus and optic nerve atrophy, confirming the diagnosis of Wolfram syndrome. The first patient had a moderate form, and the second patient had a milder form of Wolfram syndrome. Conclusion: Providers should consider monogenic diabetes genetic testing, including WFS1 gene, for patients with early-onset diabetes who are negative for islet autoantibodies and lean. Two patients described in this article could have been diagnosed with Wolfram syndrome before they developed optic nerve atrophy. Genetic testing is a valuable tool for the early detection of Wolfram syndrome, which leads to proper management and improved quality of life in patients with this rare medical condition.

Although it represents a valid alternative for patients with type 1 diabetes (T1DM) with insufficient glycemic control and/or with recurrent hypoglycemia, insulin pump therapy (Continuous Subcutaneous Insulin Infusion, CSII) in Italy is nowadays still relatively uncommon. The following review aims to investigate the barriers that limit the transition to CSII and the reasons behind the widespread cases of drop-out, particularly evident in younger patients. Among the interventions that could contribute to the solution of the problem, catheter-less insulin pumps (or patch-pumps) will be described: a tool potentially able to reduce, if not eliminate, some of the main obstacles encountered by patients. In particular, a new opportunity has become available today thanks to the evolution of the generation of catheter-less insulin pumps represented by Accu-Chek® Solo (Roche Diabetes Care GmbH), therefore three clinical cases of patients with T1DM undergoing therapy with the Accu-Chek® Solo insulin pump will be presented and discussed herein.

In the industrial panorama, many processes operate under time-varying conditions. Adapting high-performance diagnostic techniques under these relatively more complex situations is urgently needed to mitigate the risk of false alarms. Attention is being paid to fault anticipation, requiring an in-depth study of prediction techniques. Predicting remaining life before the occurrence of faults allows for a comprehensive maintenance management protocol and facilitates the wear management of the machine, avoiding faults that could permanently compromise the integrity of such machinery. This study focuses on canonical variate analysis for fault detection in processes operating under time-varying conditions and on its contribution to the diagnostic and prognostic analysis, the latter of which was performed with machine learning techniques. The approach was validated on actual datasets from a granulator operating in the pharmaceutical sector.

We hypothesized that assessment of brain connectivity may shed light on the underpinnings of ocular hypertension (OHT), characterized by raised intraocular pressure (IOP) and no typical glaucomatous findings. OHT carries a risk for future glaucoma development, thus representing a model of presymptomatic condition. In previous studies on glaucoma, we showed altered brain connectivity since the early stage and in case of normal IOP. In this pilot study, we used a multimodal MRI approach by modeling voxelwise measures of gray matter volume, anatomical connectivity along white matter(WM) tracts, and large-scale functional connectivity in OHT subjects (n = 18, age: 58.3 ± 9.8 years) and demographically matched normal controls (n = 29). While OHT brain had no structural alterations, it showed significantly decreased functional connectivity in key cognitive networks [default mode network, frontoparietal working memory network (WMN), ventral attention network (VAN), and salience network (SN)] and altered long-range functional connectivity, which was decreased between default mode and SNs and increased between primary and secondary visual networks (VN). Overall, such findings seem to delineate a complex neuroplasticity in the OHT brain, where decreased functional connectivity in non-visual networks may reflect a type of temporarily downregulated functional reserve while increased functional connectivity between VN may be viewed as a very early attempt of adaptive functional reorganization of the visual system.

Background: Resilience engineering is a paradigm for safety management that focuses on coping with complexity to achieve success, even considering several conflicting goals. Modern sociotechnical systems have to be resilient to comply with the variability of everyday activities, the tight-coupled and underspecified nature of work, and the nonlinear interactions among agents. At organizational level, resilience can be described as a combination of four cornerstones: monitoring, responding, learning, and anticipating. Methods: Starting from these four categories, this article aims at defining a semiquantitative analytic framework to measure organizational resilience in complex sociotechnical systems, combining the resilience analysis grid and the analytic hierarchy process. Results: This article presents an approach for defining resilience abilities of an organization, creating a structured domain-dependent framework to define a resilience profile at different levels of abstraction, and identifying weaknesses and strengths of the system and potential actions to increase system's adaptive capacity. An illustrative example in an anesthesia department clarifies the outcomes of the approach. Conclusion: The outcome of the resilience analysis grid, i.e., a weighed set of probing questions, can be used in different domains, as a support tool in a wider Safety-II oriented managerial action to bring safety management into the core business of the organization. Keywords: Complex system, Resilience, Resilience engineering, Safety management, Sociotechnical system

In the field of industrial process monitoring, scholars and practitioners are increasing interest in time-varying processes, where different phases are implemented within an unknown time frame. The measurement of process parameters could inform about the health state of the production assets, or products, but only if the measured parameters are coupled with the specific phase identification. A combination of values could be common for one phase and uncommon for another phase; thus, the same combination of values shows a high or low probability depending on the specific phase. The automatic identification of the production phase usually relies on clustering techniques. This is largely due to the difficulty of finding training fault data for supervised models. With these two considerations in mind, this contribution proposes the Latent Dirichlet Allocation as a natural language-processing technique for reviewing the topic of clustering applied in time-varying contexts, in the maintenance field. Thus, the paper presents this innovative methodology to analyze this specific research fields, presenting the step-by-step application and its results, with an overview of the theme.

Air Traffic Management (ATM) is a continuously evolving process, where many current system elements derive from a time when ATM characteristics were very different from today. Nowadays, the provision of ATM services has to design new solutions and adapt to new scenarios. Although ESARR 4 and EU Regulation 1035/2011 define the need of evaluating ATM system risks before implementing any change, they do not define a practical tool to support the decision-makers. The aim of this paper is to fill this gap, proposing a systematic methodology; the Preliminary System Safety Assessment Tool (PSSA-T) capable of helping the decision makers in evaluating safety implications due to system changes. PSSA-T relies on the definition of two Indexes, which have been built according to the Aerospace Performance Factor (APF) methodology, and allow safety assessment of any proposed change. In detail, the former Index compares the evolutionary scenario with the current one and the latter evaluates the evolutionary scenario in which there is a failure of intervention, in the hypothesis the system change has been implemented already. A preliminary study about the change from Flight Progress Strip (FPS) to the Electronic-FPS clarifies the outcome of the study.

The water purification process is becoming increasingly important to ensure the continuity and quality of subsequent production processes, and it is particularly relevant in pharmaceutical contexts. However, in this context, the difficulties arising during the monitoring process are manifold. On the one hand, the monitoring process reveals various discontinuities due to different characteristics of the input water. On the other hand, the monitoring process is discontinuous and random itself, thus not guaranteeing continuity of the parameters and hindering a straightforward analysis. Consequently, further research on water purification processes is paramount to identify the most suitable techniques able to guarantee good performance. Against this background, this paper proposes an application of kernel principal component analysis for fault detection in a process with the above-mentioned characteristics. Based on the temporal variability of the process, the paper suggests the use of past and future matrices as input for fault detection as an alternative to the original dataset. In this manner, the temporal correlation between process parameters and machine health is accounted for. The proposed approach confirms the possibility of obtaining very good monitoring results in the analyzed context.

Granulators play a key role in many pharmaceutical processes because they are involved in the production of tablets and capsule dosage forms. Considering the characteristics of the production processes in which a granulator is involved, proper maintenance of the latter is relevant for plant safety. During the operational phase, there is a high risk of explosion, pollution, and contamination. The nature of this process also requires an in-depth examination of the time-dependence of the process variables. This study proposes the application of canonical variate analysis (CVA) to perform fault detection in a granulation process that operates under time-varying conditions. Beyond this, a different approach to the management of process non-linearities is proposed. The novelty of the study is in the application of CVA in this kind of process, because it is possible to state that the actual literature on the theme shows some limitations of CVA in such processes. The aim was to increase the applicability of CVA in variable contexts, with simple management of non-linearities. The results, considering process data from a pharmaceutical granulator, showed that the proposed approach could detect faults and manage non-linearities, exhibiting future scenarios for more performing and automatic monitoring techniques of time-varying processes.

This paper presents an extensive literature review on the field of condition-based maintenance (CBM). The paper encompasses over 4000 contributions, analysed through bibliometric indicators and meta-analysis techniques. The review adopts Factor Analysis as a dimensionality reduction, concerning the metric of the co-citations of the papers. Four main research areas have been identified, able to delineate the research field synthetically, from theoretical foundations of CBM; (i) towards more specific implementation strategies (ii) and then specifically focusing on operational aspects related to (iii) inspection and replacement and (iv) prognosis. The data-driven bibliometric results have been combined with an interpretative research to extract both core and detailed concepts related to CBM. This combined analysis allows a critical reflection on the field and the extraction of potential future research directions.

Besides large-scale space missions, the spread of CubeSats for a variety of applications is increasingly requiring the development of systematic approaches for risk management. Being these applications are based on components with low TRL (Technology Readiness Level) or with limited performance data, it is required to define approaches which ensure a systematic perspective. This paper aims to present a reliability engineering approach based on FMECA (Failure Mode, Effects, and Criticality Analysis) to manage CubeSat reliability data and prioritize criticalities early in the design phase. The approach firstly proposes an alpha-numeric coding system to support the identification and labeling of failure modes for typical CubeSats’ items. Subsequently, each FMECA coefficient (i.e., Severity, Occurrence, Detectability) has been linked to the CubeSat’s structural properties, reducing subjectivity by means of techno-centric proxy indicators. The approach has been validated in the design phases of a 6-Units university CubeSat for the observation of M-Dwarf stars and binary systems. The performed analysis supported the design process and allowed to identify the major criticalities of the CubeSat design, as demonstrated in the extended case study included in the paper. The formalized method could be applied to design procedures for nano-satellites, as well as being expanded for research and development in a variety of space missions.

The bullwhip effect is defined as the distortion of demand information as one moves upstream in the supply chain, causing severe inefficiencies in the whole supply chain. Although extensive research has been conducted to study the causes of the bullwhip effect and seek mitigation solutions with respect to several demand processes, less attention has been devoted to the impact of seasonal demand in multi-echelon supply chains. This paper considers a simulation approach to study the effect of demand seasonality on the bullwhip effect and inventory stability in a four-echelon supply chain that adopts a base stock ordering policy with a moving average method. The results show that high seasonality levels reduce the bullwhip effect ratio, inventory variance ratio, and average fill rate to a great extent; especially when the demand noise is low. In contrast, all the performance measures become less sensitive to the seasonality level when the noise is high. This performance indicates that using the ratios to measure seasonal supply chain dynamics is misleading, and that it is better to directly use the variance (without dividing by the demand variance) as the estimates for the bullwhip effect and inventory performance. The results also show that the supply chain performances are highly sensitive to forecasting and safety stock parameters, regardless of the seasonality level. Furthermore, the impact of information sharing quantification shows that all the performance measures are improved regardless of demand seasonality. With information sharing, the bullwhip effect and inventory variance ratios are consistent with average fill rate results.

Chiari type I malformation (CMI) is a congenital hindbrain anomaly characterized by downward displacement of the cerebellar tonsils through the foramen magnum. Chiari type I malformation often presents with a complex clinical picture and can be sporadic or linked to a variety of genetic conditions. We report on a girl in whom Chiari type I malformation was associated with hypoglycemia, headache, vertigo, syncope and hepatic steatosis. We hypothesize that these symptoms are primarily a consequence of Chiari type I malformation.