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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.

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