Search Results (424)

Background: Anti-synthetase Syndrome (ASyS) is an idiopathic inflammatory myopathy characterized by muscle weakness and inflammatory infiltrates in muscles. Sjogren’s disease (SD) is an autoimmune condition primarily affecting exocrine glands. Both these conditions may present lung involvement. We describe a female patient with anti-synthetase/SD overlap syndrome and review the literature to identify published cases describing this overlap, aiming to better define its clinical, radiological, and serological features. Methods: The case description was based on a retrospective collection of clinical, laboratory, and imaging data related to the patient’s diagnostic process and clinical course. Data were anonymized and handled in accordance with the competent territorial Ethics Committee. A literature review was performed using the MEDLINE and Scopus databases by combining the keywords “Anti-Synthetase syndrome”, “Sjögren disease”, “Sjögren syndrome”, “Myositis”, and “Interstitial lung disease” (ILD). Published cases were selected if they met the 2016 EULAR/ACR criteria for SD and at least one of the currently proposed classification criteria for ASyS. Results: The described case concerns a 68-year-old woman with rapidly progressive ILD. The diagnosis of anti-synthetase/SD overlap syndrome was based on clinical, serological (anti-Ro52 and anti-PL7 antibodies), histological, and radiological findings. Despite immunosuppressive and antifibrotic treatment, the clinical course worsened, leading to a poor outcome. In addition, six relevant cases were identified in the literature. Clinical presentations, autoantibody profiles, radiological findings, and outcomes were highly heterogeneous. Among the reported cases, no standardized treatment protocols were adopted, reflecting the lack of consensus in managing this rare condition. Conclusions: In anti-synthetase/SD overlap syndrome, ILD may follow a rapidly progressive course. Early recognition can be challenging, especially in the absence of muscular involvement. This case-based review highlights the need for more standardized approaches to the diagnosis and management of this rare and complex overlap syndrome. Full article

Background/Objectives: The transjugular intrahepatic portosystemic shunt (TIPS) is an established treatment for complications of portal hypertension in patients with liver cirrhosis. While its use has increased and indications have broadened in recent years, recent comprehensive data on patient characteristics, trends, and in-hospital mortality in Germany are lacking. This study aimed to evaluate current clinical patterns and mortality outcomes associated with TIPS. Methods: This nationwide cross-sectional study used anonymized hospital data from the German InEK database between 2019 and 2023. TIPS procedures were identified using relevant OPS codes. Patient demographics, liver cirrhosis stage (Child–Pugh), hepatic encephalopathy grade, comorbid conditions, and in-hospital mortality were analyzed descriptively. Analyses were conducted using SAS 9.4. Results: A total of 12,905 TIPS procedures were documented. Annual case numbers rose from 2180 in 2019 to 2954 in 2023. Most patients were male (66.3%) and aged 60–74 years. Ascites (68.6%) was the most frequent associated diagnosis, followed by variceal bleeding (16.4%) and hepatorenal syndrome (14.9%). The average hospital stay decreased from 19.6 to 16.8 days. Overall in-hospital mortality was 8.5%, increasing with age (13.0% in ≥75 years), Child–Pugh C cirrhosis (14.9%), PCCL grade 4 (17.6%), hepatorenal syndrome (16.7%), and grade 4 hepatic encephalopathy (56.1%). Conclusions: TIPS usage in Germany has increased over the past five years, with a shift toward earlier disease stages. Higher in-hospital mortality in clinically complex patients underscores the importance of careful patient selection and tailored management strategies in high-risk groups. Full article

Introduction: Radiomics is the extraction of non-invasive and reproducible quantitative imaging features, which may yield mineable information for clinical practice implementation. Quantification of lung function through radiomics could play a role in the management of patients with pulmonary lesions. The aim of this study is to test the capability of radiomic features to predict pulmonary function parameters, focusing on the diffusing capacity of lungs to carbon monoxide (DL_CO). Methods: Retrospective data were retrieved from electronical medical records of patients treated with Stereotactic Body Radiation Therapy (SBRT) at a single institution. Inclusion criteria were as follows: (1) SBRT treatment performed for primary early-stage non-small cell lung cancer (ES-NSCLC) or oligometastatic lung nodules, (2) availability of simulation four-dimensional computed tomography (4DCT) scan, (3) baseline spirometry data availability, (4) availability of baseline clinical data, and (5) written informed consent for the anonymized use of data. The gross tumor volume (GTV) was segmented on 4DCT reconstructed phases representing the moment of maximum inhalation and maximum exhalation (Phase 0 and Phase 50, respectively), and radiomic features were extracted from the lung parenchyma subtracting the lesion/s. An iterative algorithm was clustered based on correlation, while keeping only those most associated with baseline and post-treatment DL_CO. Three models were built to predict DL_CO abnormality: the clinical model—containing clinical information; the radiomic model—containing the radiomic score; the clinical-radiomic model—containing clinical information and the radiomic score. For the models just described, the following were constructed: Model 1 based on the features in Phase 0; Model 2 based on the features in Phase 50; Model 3 based on the difference between the two phases. The AUC was used to compare their performances. Results: A total of 98 patients met the inclusion criteria. The Charlson Comorbidity Index (CCI) scored as the clinical variable most associated with baseline DL_CO (p = 0.014), while the most associated features were mainly texture features and similar among the two phases. Clinical-radiomic models were the best at predicting both baseline and post-treatment abnormal DL_CO. In particular, the performances for the three clinical-radiomic models at predicting baseline abnormal DL_CO were AUC₁ = 0.72, AUC₂ = 0.72, and AUC₃ = 0.75, for Model 1, Model 2, and Model 3, respectively. Regarding the prediction of post-treatment abnormal DL_CO, the performances of the three clinical-radiomic models were AUC₁ = 0.91, AUC₂ = 0.91, and AUC₃ = 0.95, for Model 1, Model 2, and Model 3, respectively. Conclusions: This study demonstrates that radiomic features extracted from healthy lung parenchyma on a 4DCT scan are associated with baseline pulmonary function parameters, showing that radiomics can add a layer of information in surrogate models for lung function assessment. Preliminary results suggest the potential applicability of these models for predicting post-SBRT lung function, warranting validation in larger, prospective cohorts. Full article

Background: The longitudinal RisCoin study investigated risk factors for COVID-19 vaccination failure among healthcare workers (HCWs) and patients with inflammatory bowel disease (IBD) at a University Hospital in Germany. Since the hospital served as the study sponsor and employer of the HCW, we implemented a custom mobile application. We aimed to evaluate the implementation, adherence, benefits, and limitations of this study’s app. Methods: The app allowed secure data collection through questionnaires, disseminated serological results, and managed bidirectional communication. Access was double-pseudonymized and irreversibly anonymized six months after enrollment. Download frequency, login events, and questionnaire submissions between October 2021 and December 2022 were analyzed. Multivariable logistic regression identified factors associated with app adherence. Results: Of the 3979 participants with app access, 3622 (91%) used the app; out of these, 1016 (28%) were “adherent users” (≥12 submitted questionnaires). App adherence significantly increased with age. Among HCW, adherent users were more likely to be non-smokers (p < 0.001), working as administrators or nursing staff vs. physicians (p < 0.001), vaccinated against influenza (p < 0.001), and had not travelled abroad in the past year (p < 0.001). IBD patients exposed to SARS-CoV-2 (p = 0.0133) and those with adverse events following the second COVID-19 vaccination (p = 0.0171) were more likely adherent app users. Despite technical issues causing dropout or non-adherence, the app served as a secure solution for cohort management and longitudinal data collection. Discussion: App-based cohort management enabled continuous data acquisition and individualized care while providing flexibility and anonymity for the study team and participants. App usability, technical issues, and cohort characteristics need to be thoroughly considered prior to implementation to optimize usage and adherence in clinical research. Full article

Cybersecurity represents a critical challenge for data-sharing platforms involving multiple stakeholders, particularly within complex and decentralized systems such as livestock supply chain networks. These systems demand novel approaches, robust security protocols, and advanced data management strategies to address key challenges such as data consistency, transparency, ownership, controlled access or exposure, and privacy-preserving analytics for value-added services. In this paper, we introduced the Framework for Livestock Empowerment and Decentralized Secure Data eXchange (FLEX), as a comprehensive solution grounded on five core design principles: (i) enhanced security and privacy, (ii) human-centric approach, (iii) decentralized and trusted infrastructure, (iv) system resilience, and (v) seamless collaboration across the supply chain. FLEX integrates interdisciplinary innovations, leveraging decentralized infrastructure-based protocols to ensure trust, traceability, and integrity. It employs secure data-sharing protocols and cryptographic techniques to enable controlled information exchange with authorized entities. Additionally, the use of data anonymization techniques ensures privacy. FLEX is designed and implemented using a microservices architecture and edge computing to support modularity and scalable deployment. These components collectively serve as a foundational pillar of the development of a digital product passport. The FLEX architecture adopts a layered design and incorporates robust security controls to mitigate threats identified using the STRIDE threat modeling framework. The evaluation results demonstrate the framework’s effectiveness in countering well-known cyberattacks while fulfilling its intended objectives. The performance evaluation of the implementation further validates its feasibility and stability, particularly as the volume of evidence associated with animal identities increases. All the infrastructure components, along with detailed deployment instructions, are publicly available as open-source libraries on GitHub, promoting transparency and community-driven development for wider public benefit. Full article

Background/Objectives: Food allergy (FA) is a growing concern in pediatric care, requiring effective avoidance strategies and timely emergency responses. The role of caregivers is central to the daily management of FA. This study aimed to assess parental knowledge, preparedness, and behaviors regarding pediatric FA management, focusing on both prevention and emergency readiness. Methods: A cross-sectional survey was conducted from December 2024 to April 2025 through the SurveyMonkey^® platform, promoted by the Italian Society of Pediatric Allergology and Immunology (SIAIP). The anonymous, structured questionnaire was distributed online and in two Italian university hospitals. A total of 129 fully completed responses from caregivers of children with FA were analyzed. The survey explored self-perceived knowledge, symptom recognition, preventive actions, emergency preparedness, and communication practices. Results: Only 9.3% of parents considered themselves “very informed,” while 54.3% reported limited or no knowledge. Just 16.0% recognized all symptoms of an allergic reaction, and only 24.0% could distinguish mild reactions from anaphylaxis. Notably, 67.4% reported not knowing how to respond to anaphylaxis, and 83.7% did not possess an epinephrine auto-injector. Preventive measures at home were inconsistently applied, and 41.1% took no precautions when eating out. Communication with external caregivers was often informal or absent. Only 33% updated physicians regularly. Conclusions: The findings reveal significant gaps in parental preparedness and highlight critical areas for educational intervention. Enhanced caregiver training, standardized communication protocols, and improved clinical follow-up are essential to strengthen pediatric FA management and safety. Full article

This study aimed to evaluate and compare the performance of four advanced artificial intelligence-powered chatbots—ChatGPT-4 Omni (ChatGPT-4o), DeepSeek, Gemini Advanced, and Claude 3.7 Sonnet—in responding to questions related to traumatic dental injuries (TDIs) in the primary dentition. The assessment focused on accuracy, completeness, readability, and response time, aligning with the 2020 International Association of Dental Traumatology guidelines. Twenty-five open-ended TDI questions were submitted to each model in two separate sessions. Responses were anonymized and evaluated by four pediatric dentists. Accuracy and completeness were rated using Likert scales; readability was assessed using five standard indices; and response times were recorded in seconds. ChatGPT-4o demonstrated significantly higher accuracy than Gemini Advanced (p = 0.005), while DeepSeek outperformed Gemini Advanced in completeness (p = 0.010). Response times differed significantly (p < 0.001), with DeepSeek being the slowest and ChatGPT-4o and Gemini Advanced being the fastest. DeepSeek produced the most readable outputs relatively, though none met public readability standards. Claude 3.7 generated the most complex texts (p < 0.001). A strong correlation existed between accuracy and completeness (ρ = 0.701, p < 0.001). These findings emphasize the cautious integration of artificial intelligence chatbots into pediatric dental care due to varied performance. Clinical accuracy, completeness, and readability are critical when offering information aligned with guidelines to support decisions in dental trauma management. Full article

Background and Objectives: Physical activity represents a cornerstone in the management of diabetes mellitus; however, the majority of persons with diabetes remain insufficiently active. Current guidelines emphasise the need to educate individuals with diabetes on physical activity at each clinical visit. The aim of the study was to develop an adequate instrument and test diabetologists’ knowledge on physical activity and related guidelines in diabetes, as well as investigate their prescribing habits. Materials and Methods: The instrument was developed with four diabetologists using Delphi and RAND appropriateness methods, then distributed to diabetologist societies in 12 Southeast European countries for anonymous completion by their members. Data were analysed using Mann–Whitney U test and Pearson ${ x}^2$ test. Results: Out of 302 analysed respondents, 123 (41%) worked at university hospitals, at average four days a week in outpatient departments, mean number of patients was 16 a day; 95 (32%) had a PhD, and 105 (35%) university teaching positions. Median overall knowledge was 15 (Q1–Q3, 13–17) out of 22 maximum points. 26% diabetologists knew recommended level of physical activity for people with diabetes. Physical activity goals were discussed during check-ups in 53.0% of cases; re-education and goal adjustments were provided in 33.4% when health status changed; and clear instructions were documented in 25.5% of medical records. Conclusions: Diabetologists’ overall knowledge of physical activity was found to be at least satisfactory in 90% of participants. Knowledge of the guidelines is insufficient in 75% of participants. The prescribing habits are inadequate in half of the participants. Full article

Blockchain and smart contracts are widely used in IoT access control to create decentralized, trustworthy environments for secure access and record management. However, their application introduces a dual challenge: The transparency of blockchain and the use of addresses as identifiers can expose account privacy. To tackle this issue, this paper proposes a blockchain-based IoT access control system that enhances account anonymity and preserves privacy, particularly regarding user behavior, habits, and access records through the use of zero-knowledge proofs. The system incorporates an access control mechanism that combines access control lists with capability-based access control, enabling ownership verification of access rights without disclosing identity information. To evaluate the system’s feasibility, we conduct experiments in a smart building scenario, including both qualitative comparisons with existing methods and quantitative analyses of performance in terms of time, space, and gas consumption. The results indicate that our scheme achieves the best time efficiency in the proof generation and authorization phases, completing them in just 7 and 10 s, respectively—representing half the time required by the second-best approach. These findings underscore the system’s superior cost efficiency and enhanced security compared to existing solutions. Full article

Background: Acute Aortic Syndrome (AAS), encompassing aortic dissection (AD), intramural hematoma (IMH), and penetrating atherosclerotic ulcer (PAU), presents diagnostic challenges due to its varied manifestations and the critical need for rapid assessment. Methods: We developed a multi-stage deep learning model trained on chest computed tomography angiography (CTA) scans. The model utilizes a U-Net architecture for aortic segmentation, followed by a cascaded classification approach for detecting AD and IMH, and a multiscale CNN for identifying PAU. External validation was conducted on 260 anonymized CTA scans from 14 U.S. clinical sites, encompassing data from four different CT manufacturers. Performance metrics, including sensitivity, specificity, and area under the receiver operating characteristic curve (AUC), were calculated with 95% confidence intervals (CIs) using Wilson’s method. Model performance was compared against predefined benchmarks. Results: The model achieved a sensitivity of 0.94 (95% CI: 0.88–0.97), specificity of 0.93 (95% CI: 0.89–0.97), and an AUC of 0.96 (95% CI: 0.94–0.98) for overall AAS detection, with p-values < 0.001 when compared to the 0.80 benchmark. Subgroup analyses demonstrated consistent performance across different patient demographics, CT manufacturers, slice thicknesses, and anatomical locations. Conclusions: This deep learning model effectively detects the full spectrum of AAS across diverse populations and imaging platforms, suggesting its potential utility in clinical settings to enable faster triage and expedite patient management. Full article

The Internet of Drones (IoD) is rapidly expanding into sensitive applications, necessitating robust and efficient authentication. Traditional methods struggle against prevalent attacks, especially considering the unique vulnerabilities of the IoD, such as drone physical capture. This paper proposes Bio-2FA-IoD, a novel biometric-enhanced two-factor authentication protocol designed for secure IoD operations. Drawing on established 2FA principles and fuzzy extractor technology, Bio-2FA-IoD achieves strong mutual authentication between an operator (via an operator device), a drone (as a relay), and a ground control station (GCS), supported by a trusted authority. We detail the protocol’s registration and authentication phases, emphasizing reliable biometric key generation. A formal security analysis using BAN logic demonstrates secure belief establishment and key agreement, while a proof sketch under the Bellare–Pointcheval–Rogaway (BPR) model confirms its security against active adversaries in Authenticated Key Exchange (AKE) contexts. Furthermore, a comprehensive performance evaluation conducted using the Contiki OS and Cooja simulator illustrates Bio-2FA-IoD’s superior efficiency in computational and communication costs, alongside very low latency, high packet delivery rate, and minimal energy consumption. This positions it as a highly viable and lightweight solution for resource-constrained IoD environments. Additionally, this paper conceptually explores potential extensions to Bio-2FA-IoD, including the integration of Diffie–Hellman for enhanced perfect forward secrecy and a Sybil-free pseudonym management scheme for improved user anonymity and unlinkability. Full article

In 2020, Directive (EU) 2020/2184 was published and subsequently transposed into Italian legislation via Legislative Decree 18/23 (Lgs.D. 18/23). The Directive aims to protect public health through a proactive approach based on a site-specific risk analysis along the entire water supply chain (Water Safety Plan, WSP). Between February and November 2024, a survey was conducted in Italy to assess both hospitals’ knowledge of Lgs.D. 18/23 and the application of the WSP in these facilities. A total of 300 hospitals were asked to complete an anonymous questionnaire containing 60 questions about the characteristics of the facility and the management of the water network, including the chemical–physical and microbiological monitoring of Legionella and other microbiological parameters. A total of 102 questionnaires were sent out (response rate: 34%), but only 72 were properly completed and analyzed. The results of the chemical–physical monitoring are not presented in this manuscript. Overall, 52.8% of the hospitals were built before 2000, and most are aware of Directive (EU) 2020/2184, Lgs.D.18/23 (80.6%), in particular, Article 9 on the risk assessment and management of internal water systems and the guidelines for its implementation (77.8%). All hospitals perform annual microbiological water testing, including Legionella analysis, with a detection rate of <50%. National guidelines for the implementation of WSPs are known in 75% of the hospitals, but only 38.9% have started planning to implement them, and 13.9% organize staff training on the subject. The questionnaire responses highlight the need to train hospital staff in water system risk management and WSP planning, which will be mandatory by 2029. Full article

Background: Fever and pain are among the most frequent symptoms in pediatric care, requiring timely and appropriate management. While evidence-based guidelines are available, adherence in real-world practice remains variable. This study aimed to explore the attitudes and prescribing behaviors of Italian Primary Care Pediatricians (PCPs) in the management of fever and pain, and to assess their alignment with current clinical recommendations. Materials and Methods: An anonymous, cross-sectional survey consisting of 30 multiple-choice questions was administered to 900 PCPs between 1 July and 30 October 2024. The questionnaire assessed therapeutic preferences, dosing strategies, and perceived knowledge gaps. Invitations were distributed via pediatric scientific societies and regional professional networks. Results: A total of 244 PCPs completed the survey (response rate 27.1%). The majority were aged over 55 years (72.1%), worked in urban settings (71.3%), and had more than 20 years of clinical experience (74.6%). Most respondents (77%) reported managing pediatric fever or pain on a daily basis. Paracetamol was the preferred first-line treatment for fever (95.9%), primarily due to its perceived safety (82.4%). Ibuprofen was favored by 51.6% of those who selected it for its greater effectiveness. The alternating use of paracetamol and ibuprofen for fever was never adopted by 49.6%, while 31.6% employed this strategy, believing it to be more effective. For pain, 67.6% used paracetamol and 26.2% used ibuprofen as first-line treatments; 15.2% reported alternating the two drugs. Correct dosage practices were followed by 63.9% for both medications, although 40.2% did not differentiate dosages between fever and pain management. Conclusions: While general trends showed alignment with current guidelines, notable inconsistencies were observed in drug selection, dosage, and the use of alternating therapies. These findings highlight a pressing need to improve the dissemination and implementation of pediatric fever and pain management guidelines among PCPs in order to reduce unsafe practices, avoid therapeutic errors, and prevent unnecessary strain on emergency care services. Full article

As the Internet of Things (IoT) continues to evolve, the demand for cross-domain collaboration between devices and data sharing has grown significantly. Operations confined to a single trust domain can no longer satisfy this requirement, so cross-domain access to resources is becoming an inevitable trend in the evolution of the IIoT. Due to identity trust issues between different domains, authorized access is required before resources can be shared. However, most existing cross-domain authentication schemes face significant challenges in terms of dynamic membership management, privacy protection, and traceability. These schemes involve complex and inefficient interactions and fail to meet the dynamic and lightweight requirements of the IIoT. To address these issues, we propose a privacy-preserving and traceable cross-domain authentication scheme based on dynamic group signatures that enables efficient authentication. The scheme supports anonymous authentication via succinct proofs and incorporates a trapdoor mechanism to enable group managers to trace and revoke malicious identities. Additionally, our solution supports efficient joining and revoking of members and implements blacklist-based proof of non-membership. We formally prove the security of the proposed scheme. The experimental results demonstrate that the proposed scheme outperforms others in terms of computational cost and revocation overhead. Full article

An Identity Management System (IDMS) is responsible for managing and organizing identities and credentials exchanged between users, Identity Providers (IDPs), and Service Providers (SPs). The primary goal of IDMS is to ensure the confidentiality and privacy of users’ personal data. Traditional IDMS relies on a third party to store user information and authenticate the user. However, this approach poses threats to user privacy and increases the risk of single point of failure (SPOF), user tracking, and data unavailability. In contrast, decentralized IDMSs that use blockchain technology offer potential solutions to these issues as they offer powerful features including immutability, transparency, anonymity, and decentralization. Despite its advantages, blockchain technology also suffers from limitations related to performance, third-party control, weak authentication, and data leakages. Furthermore, some blockchain-based IDMSs still exhibit centralization issues, which can compromise user privacy and create SPOF risks. This study proposes a decentralized IDMS that leverages blockchain and smart contract technologies to address the shortcomings of traditional IDMSs. The proposed system also utilizes the Interplanetary file system (IPFS) to enhance the scalability and performance by reducing the on-chain storage load. Additionally, the proposed IDMS employs the Elliptic Curve Integrated Encryption Scheme (ECIES) to provide an extra layer of security to protect users’ sensitive information while improving the performance of the systems’ transactions. Security analysis and experimental results demonstrated that the proposed IDMS offers significant security and performance advantages compared to its counterparts. Full article