Search Results (319)

Inherited epidermolysis bullosa (EB) is a heterogeneous clinical entity that includes over 30 phenotypically and/or genotypically distinct inherited disorders, characterized by mechanical skin fragility and bullae formation. Junctional EB (JEB) is an autosomal recessive disease characterized by an intermediated cleavage level within the skin layers, commonly at the “lamina lucida”. Laryngo-onycho-cutaneous syndrome (LOC) is an extremely rare variant of JEB, characterized by granulation tissue formation in specific body sites (skin, larynx, and nails). Although most cases of JEB are caused by pathogenic variants occurring in the genes encoding for classical components of the lamina lucida, such as laminin 332 (LAMA3, LAMB3, LAMC2), integrin α6β4 (ITGA6, ITGB4), and collagen XVII (COL17A1), other variants have also been described. We report the case of a 4-month-old male infant who presented with recurrent bullous and erosive lesions from the first month of life. At the first dermatological evaluation, the patient was agitated and exhibited hoarse breathing, a clinical sign suggestive of laryngeal involvement. Multiple polygonal skin erosions were observed on the cheeks, along with similar isolated, roundish lesions on the scalp and legs. Notably, nail dystrophy and near-complete anonychia were evident on the left first and fifth toes. Due to the coexistence of skin erosions and nail dystrophy in such a young infant, a congenital bullous disorder was suspected, prompting molecular analysis of all potentially involved genes. In the patient’s DNA, clinical exome sequencing (CES) identified a pathogenic variant, apparently in homozygosity, in the exon 1 of the LAMA3 gene (18q11.2; NM_000227.6): c.47G > A;p.Trp16*. The presence of this variant was confirmed, in heterozygosity, in the genomic DNA of the patient’s mother, while it was absent in the father’s DNA. Subsequently, trio-based SNP array analysis was performed, revealing a paternally derived pathogenic microdeletion encompassing the LAMA3 locus (18q11.2). To our knowledge, this is the first reported case of JEB with a LOC-like phenotype caused by a maternally inherited monoallelic nonsense mutation in LAMA3, unmasked by an almost complete deletion of the paternal allele. The combined use of exome sequencing and SNP array is proving essential for elucidating autosomal recessive diseases with a discordant segregation. This is pivotal for providing accurate genetic counseling to parents regarding future pregnancies. Full article

The upcoming deployment of sixth-generation (6G) wireless networks promises to significantly outperform 5G in terms of data rates, spectral efficiency, device densities, and, most importantly, latency and security. To cope with the increasingly complex network traffic, Network Traffic Classification (NTC) will be essential to ensure the high performance and security of a network, which is necessary for advanced applications. This is particularly relevant in the Internet of Things (IoT), where resource-constrained platforms at the edge must manage tasks like traffic analysis and threat detection. In this context, balancing classification accuracy with computational efficiency is key to enabling practical, real-world deployments. Traditional payload-based and packet inspection methods are based on the identification of relevant patterns and fields in the packet content. However, such methods are nowadays limited by the rise of encrypted communications. To this end, the research community has turned its attention to statistical analysis and Machine Learning (ML). In particular, Convolutional Neural Networks (CNNs) are gaining momentum in the research community for ML-based NTC leveraging statistical analysis of flow characteristics. Therefore, this paper addresses CNN-based NTC in the presence of encrypted communications generated by the rising Quick UDP Internet Connections (QUIC) protocol. Different models are presented, and their performance is assessed to show the trade-off between classification accuracy and CNN complexity. In particular, our results show that even simple and low-complexity CNN architectures can achieve almost 92% accuracy with a very low-complexity architecture when compared to baseline architectures documented in the existing literature. Full article

Kallmann syndrome (KS) is a form of hypogonadotropic hypogonadism (HH) characterized by gonadotropin-releasing hormone (GnRH) deficiency and anosmia due to defective neuronal migration. While traditionally considered irreversible, cases of spontaneous improvement of HH have been reported, suggesting residual GnRH neuronal function in some individuals. We present a case of a 29-year-old man with KS who exhibited spontaneous recovery of endogenous testosterone production following the cessation of long-term androgen therapy without the use of alternative hormonal agents. After ceasing testosterone therapy for several months, the patient’s total testosterone levels normalized (407–424 ng/dL), accompanied by increased secondary sexual characteristics, stable gonadotropin levels, and normal testicular volume. Persistent anosmia was noted, suggesting that restoration of reproductive endocrine function can occur independently of olfactory recovery. Genetic testing identified heterozygous mutations in PCSK1 and HS6ST1, genes implicated in GnRH regulation and KS pathogenesis. This case highlights the potential role of genetic variation in spontaneous HH improvement and underscores the need for individualized management strategies, including periodic reassessment of gonadal function and fertility potential. Further research is needed to elucidate the mechanisms driving spontaneous HH improvement, identify predictive biomarkers of reversibility, and explore therapeutic strategies that may promote endogenous GnRH activity in select patients with KS. Full article

Gender-specific cardiology has gained increasing recognition in recent years, emphasizing the need for tailored management strategies for women with cardiovascular disease. Among these, cardiomyopathies—dilated, arrhythmogenic, hypertrophic, and restrictive—pose unique challenges throughout a woman’s reproductive life, affecting contraception choices, pregnancy outcomes, and breastfeeding feasibility. Despite significant advances in cardiovascular care, there is still limited guidance on balancing maternal safety and neonatal well-being in this complex setting. This review provides a comprehensive overview of the current evidence on reproductive counseling, pregnancy management, and postpartum considerations in women with cardiomyopathies. We discuss the cardiovascular risks associated with each cardiomyopathy subtype during pregnancy, highlighting risk stratification tools and emerging therapeutic strategies. Additionally, we address the safety and implications of breastfeeding, an often overlooked but increasingly relevant aspect of postpartum care. A multidisciplinary approach involving cardiologists, gynecologists, obstetricians, and anesthesiologists is crucial to optimizing maternal and fetal outcomes. Improved risk assessment, tailored patient counseling, and careful management strategies are essential to ensuring safer reproductive choices for women with cardiomyopathy. From now on, greater attention is expected to be given to bridging existing knowledge gaps, promoting a more personalized and evidence-based approach to managing these patients throughout different stages of reproductive life. Full article

The exploitation of renewable energy is essential for mitigating climate change and reducing fossil fuel emissions. Wind energy, the most mature technology, is highly dependent on wind speed, and the accurate prediction of the latter substantially supports wind power generation. In this work, various artificial neural networks (ANNs) were developed and evaluated for their wind speed prediction ability using the ERA5 historical reanalysis data for four potential Offshore Wind Farm Organized Development Areas in Greece, selected as suitable for floating wind installations. The training period for all the ANNs was 80% of the time series length and the remaining 20% of the dataset was the testing period. Of all the ANNs examined, the hybrid model combining Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks demonstrated superior forecasting performance compared to the individual models, as evaluated by standard statistical metrics, while it also exhibited a very good performance at high wind speeds, i.e., greater than 15 m/s. The hybrid model achieved the lowest root mean square errors across all the sites—0.52 m/s (Crete), 0.59 m/s (Gyaros), 0.49 m/s (Patras), 0.58 m/s (Pilot 1A), and 0.55 m/s (Pilot 1B)—and an average coefficient of determination (R²) of 97%. Its enhanced accuracy is attributed to the integration of the LSTM and GRU components strengths, enabling it to better capture the temporal patterns in the wind speed data. These findings underscore the potential of hybrid neural networks for improving wind speed forecasting accuracy and reliability, contributing to the more effective integration of wind energy into the power grid and the better planning of offshore wind farm energy generation. Full article

This paper presents field experiments of mineral deposition on steel, induced by cathodic polarization in natural seawater, as a sustainable strategy for the life extension of marine steel structures. Although this approach is quite well known, the ability of the mineral deposit to both protect steel from corrosion in the absence of a cathodic current, thus operating as an inorganic coating, and provide an effective substrate for colonization by microorganisms still needs to be fully explained. To this end, two identical steel structure prototypes were installed at a depth of 20 m: one was submitted to cathodic polarization, while the other was left under free corrosion for comparison. After 6 months, the current supplied to the electrified structure was interrupted. A multidisciplinary approach was used to analyze the deposits on steel round bars installed in the prototypes over time, in the presence and in the absence of a cathodic current. Different investigation techniques were employed to provide the following information on the deposit: the composition in terms of elements, compounds and macro-biofouling; the morphology; the thickness and the degree of protection estimated by electrochemical impedance spectroscopy (EIS). The results showed that under cathodic polarization, the thickness of the deposit increased to 2.5 mm and then remained almost constant after the current was interrupted. Conversely, the surface impedance decreased from 3 kΩ cm² to about 1.5 kΩ cm² at the same time, and the aragonite–brucite ratio also decreased. This indicates a deterioration in the protection performance and soundness of the deposit, respectively. Considering the trends in thickness and impedance together, it can be concluded that the preformed mineral deposit does not undergo generalized deterioration after current interruption, which would result in a reduction in thickness, but rather localized degradation. This phenomenon was attributed to the burrowing action of marine organisms, which created porosities and/or capillary pathways through the deposit. Therefore, the corrosion protection offered by the mineral deposit without a cathodic current is insufficient because it loses its protective properties. However, the necessary current can be quite limited in the presence of the deposit, which in any case provides a suitable substrate for sustaining the colonization and growth of sessile marine organisms, thus promoting biodiversity. Full article

Air pollution is a major problem in the modern world; although it particularly impacts developing countries, which are experiencing fast and often uncontrolled industrialization, its effects constitute a global burden on the environment and health. At the same time, the costs of effective air quality monitoring programs are prohibitive for emerging economies, thus making any correction difficult to assess. Emerging technologies, such as distributed networks of sensors organized in the Internet of Things, are under the lens of scientific and industrial communities as a valuable, low-cost alternative to standard techniques. In this paper, we report a review of current approaches to distributed air quality monitoring. Specifically, we (1) emphasize the role of crowdsensing in leveraging sensor-enabled mobile devices for large-scale environmental data collection and (2) discuss criticalities, open challenges, and future perspectives in enforcing data security when such approaches are deployed in real application scenarios. Full article

The Impact Monitor Project is a European initiative designed to develop an impact assessment toolbox and framework, targeting the European aviation sector. The proposed framework is not only aimed at the environment, economics, and operations but also the societal impacts of new technologies and aircraft configurations. The toolbox works by setting out the key steps in the impact assessment cycle and presenting guidance, tips, and best practices. Led by DLR, the consortium includes research institutions and universities that have contributed their expertise and tools to develop the collaborative assessment toolbox and framework. The project defines three use cases by considering three assessment levels: aircraft, airport, and air transport system. This article focuses on Use Case 2 on continuous descent operations (CDOs) at the aircraft and airport levels. It describes the workflow proposal, along with the tools involved. The collaborative approach showcases integrating these tools and using collaborative strategies enabled by CPACS (Common Parametric Aircraft Configuration Schema) and RCE (remote component environment). The list of tools includes Scheduler (DLR; flight schedule simulation), AirTOp (NLR; TMA simulation), Dynamo/Farm (UPC; trajectory simulation and assessment), LEAS-iT (NLR; emissions simulation), Tuna (NLR; noise simulation), AECCI (ONERA; emissions simulation), TRIPAC (NLR; third-party risk simulation), and SCBA (TML; social and economic impact assessment). Interactions with other use cases of the project will be demonstrated via new aircraft configurations stemming from the use case at the aircraft level of the project. The results demonstrate the workflow’s feasibility, the cooperation among the tools to obtain and refine the outcomes, as well as the analysis of the operational scenario of a generic airport, CAEPport, which has been extensively used in previous Clean Sky 2 projects. Full article

The growing population of space debris poses significant risks to operational satellites and future space missions, necessitating innovative and efficient tracking solutions. Ground-based radar for space surveillance has been a central area of research since the early Space Age, with recent advancements emphasizing the use of bistatic radar systems that incorporate sensitive radio telescopes as receivers. This approach offers a cost-effective and scalable solution for monitoring space debris. Preliminary observations demonstrated the viability of employing radio telescopes in bistatic configurations for effective debris tracking. This review provides a comprehensive analysis of experiments utilizing radio telescopes as bistatic receivers, highlighting key advancements, challenges, and potential applications in space surveillance systems. By detailing the progress in this field, this study underscores the critical role of bistatic radar systems in mitigating the growing threat of space debris. Full article

Synthetic aperture radar (SAR) recognition systems often need to collect new data and update the network accordingly. However, the network faces the challenge of catastrophic forgetting, where previously learned knowledge might be lost during the incremental learning of new data. To improve the applicability and sustainability of SAR target classification methods, we propose a multi-stage regularization-based class-incremental learning (CIL) method for SAR targets, called SCF-CIL, which addresses catastrophic forgetting. This method offers three main contributions. First, for the feature extractor, we fuse the convolutional neural network features with the scattering center features using a cross-attention feature fusion structure, ensuring both the plasticity and stability of the extracted features. Next, an overfitting training strategy is applied to provide clustering space for unseen classes with an acceptable trade-off in the accuracy of the current classes. Finally, we analyze the influence of training with imbalanced data on the last fully connected layer and introduce a multi-stage regularization method by dividing the calculation of the fully connected layer into three parts and applying regularization to each. Our experiments on SAR datasets demonstrate the effectiveness of these improvements. Full article

The integration of Internet of Things (IoT) technology into the agricultural sector enables the collection and analysis of large amounts of data, facilitating greater control over internal processes, resulting in cost reduction and improved quality of the final product. One of the main challenges in designing an IoT system is the need for interoperability among devices: different sensors collect information in non-homogeneous formats, which are often incompatible with each other. Therefore, the user of the system is forced to use different platforms and software to consult the data, making the analysis complex and cumbersome. The solution to this problem lies in the adoption of an IoT standard that standardizes the output of the data. This paper first provides an overview of the standards and protocols used in precision farming and then presents a system architecture designed to collect measurements from sensors and translate them into a standard. The standard is selected based on an analysis of the state of the art and tailored to meet the specific needs of precision agriculture. With the introduction of a connector device, the system can accommodate any number of different sensors while maintaining the output data in a uniform format. Each type of sensor is associated with a specific connector that intercepts the data intended for the database and translates it into the standard format before forwarding it to the central server. Finally, examples with real sensors are presented to illustrate the operation of the connectors and their role in an interoperable architecture, aiming to combine flexibility and ease of use with low implementation costs. Full article

Background: The glomerular filtration rate (GFR) is a universal clinical measure central to assessing kidney function and to the management of kidney disorders. Several formulas for the estimation of GFR are in use. The European Kidney Function Consortium (EKFC) formula has been reported to more accurately estimate the GFR as compared to the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formula and its recent version (REFIT equation) in European and African populations. However, validation of the EKFC equation in diverse U.S. populations, especially the Black subpopulation, is needed. Methods: Data from the electronic medical records of 75,442 individuals presenting to a large safety net county hospital with measurements of serum creatinine and/or iohexol clearance studies were used to calculate the estimated GFR (eGFR) and to determine CKD stage using the various reported eGFR formulas. The correlation between eGFR and measured GFR was determined for each equation. Results: The median eGFR for Black participants using the CKD-EPI, REFIT, and EKFC formulas was 130.6 mL/min/1.73 m², 82.0 mL/min/1.73 m², and 80.6 mL/min/1.73 m² (p < 0.001), respectively. For White participants, the median eGFR using the CKD-EPI, REFIT, and EKFC formulas was 145.3 mL/min/1.73 m², 105.6 mL/min/1.73 m², and 99.2 mL/min/1.73 m², respectively (p < 0.001). The REFIT equation underestimates the mGFR in Black individuals at eGFR < 80 mL/min per 1.73 m² and in White individuals at eGFR > 20 mL/min per 1.73 m². In comparison, the EKFC equation underestimates the mGFR at eGFR > 20 mL/min per 1.73 m² in both Black and White individuals. The REFIT equation had the least absolute median bias as compared to EKFC and CKD-EPI in both Black and White participants (p < 0.0001). The P30 of the REFIT and EKFC equations was not statistically different for either Black or White participants (p = 0.16, p = 0.37). Conclusions: Although the accuracies (P30) of the EKFC and REFIT equations are not statistically significant (p = 0.16 and 0.37, Black and White individuals, respectively), adopting the EKFC formula in Americans requires the evaluation of each subpopulation. Both the EKFC and REFIT formulas underestimate the mGFR at a lower eGFR, which may have a direct impact on CKD classification for Black and White patients, with potentially significant implications for clinical management. Full article

Background/Objectives: Food insecurity (FI) is the ‘limited or uncertain availability of nutritionally adequate and safe foods’. Although the literature suggests a strong association between FI, socioeconomic status (SES), and health, the nature of their relationship is not well specified in vulnerable population groups such as university students. To address this gap, this study aimed to assess the prevalence of FI among university students, examine its association with various health outcomes, and explore the potential mediating effect of SES. Methods: This cross-sectional survey included a convenience sample of 394 participants from the University of the Basque Country UPV/EHU (Spain). Data on SES, demographic and lifestyle factors, and health outcomes were collected between December 2021 and January 2022, using a questionnaire developed by the Food Insecurity among European University Students during the COVID-19 Pandemic (FINESCOP) consortium. Internal consistency of the questionnaire was assessed at the UPV/EHU using Cronbach’s α. FI was measured using the Food Insecurity Experience Scale (FIES) from the FAO, which was validated through testing of Rasch model assumptions. Results: Overall, 19% of university students were food insecure, with 2.5% experiencing moderate and 0.8% experiencing severe FI. Adjusted linear regression models showed that FI was associated with a higher body mass index (BMI), poorer self-rated health, and worsening health during the pandemic. SES mediated the relationship between FI and health outcomes, with the strongest mediation observed for BMI (indirect association: B = 0.25, 95% CI = −0.17–0.75; total association: B = 1.85, 95% CI = 0.14–3.56; 15.3% of mediation). Conclusions: FI is prevalent among UPV/EHU students, and it is associated with multiple negative health outcomes, partly explained by SES. To effectively address FI, higher education institutions should consider implementing comprehensive strategies. For future research, longitudinal studies would be recommended to systematically monitor FI and examine causal relationships. Full article

Background/Objectives: SARS-CoV-2 has strained healthcare systems, emphasizing the need for biomarkers to predict disease severity. Recent studies suggest that soluble urokinase plasminogen activator receptor (suPAR) is a promising marker for COVID-19 pneumonia, though its utility alongside the CURB-65 score remains unstudied. This study evaluates the prognostic value of suPAR in comparison to leukocyte count and CURB-65, and its potential for enhancing risk stratification in a combined CURB-65 model. Methods: Biomarkers and CURB-65 scores were obtained for 240 immunocompetent patients hospitalised with COVID-19 pneumonia. Intensive care unit admission and in-hospital mortality were assessed using receiver operating characteristic (ROC) curves and Kaplan–Meier analysis. Additionally, a Net Reclassification Improvement (NRI) analysis was performed to evaluate the predictive value of suPAR combined with the CURB-65 score for risk stratification. Results: suPAR demonstrated strong diagnostic accuracy, outperforming lymphocyte count and showing greater precision than the CURB-65 score for ICU admission. Notably, no patient with suPAR < 4 ng/mL experienced the studied outcomes. NRI analysis revealed a significant improvement in risk classification when suPAR was combined with CURB-65. Conclusions: The addition of the suPAR biomarker to the CURB-65 score represents a substantial improvement in the risk classification of patients with COVID-19 pneumonia, with a potential impact on daily clinical practice. Full article

Acute coronary syndrome (ACS) and stroke are interconnected conditions that often share risk factors such as atherosclerosis, thrombosis, and systemic inflammation. When these events occur simultaneously, they present unique diagnostic and therapeutic challenges. This review explores the pathophysiological mechanisms linking ACS and stroke, including common pathways like plaque instability, cardioembolism, and endothelial dysfunction, while highlighting the distinct features of ischemic and hemorrhagic strokes. The manuscript provides an overview of diagnostic strategies, emphasizing the role of biomarkers, advanced neuroimaging, and risk stratification tools in guiding acute management. Furthermore, the review delves into treatment approach, emphasizing the need to balance reperfusion therapies for ACS with thrombolysis or thrombectomy for ischemic stroke while carefully managing the challenges posed by anticoagulation in cases complicated by bleeding. Long-term strategies for secondary prevention are examined, including antithrombotic regimens tailored to the dual risk of thrombosis and bleeding, as well as lipid-lowering and blood pressure management. Future perspectives highlight the potential of novel pharmacological agents, neuroprotective therapies, and AI-driven tools to enhance patient outcomes. This review underscores the importance of integrated, multidisciplinary care and identifies key areas for future research to optimize the management of these high-risk patients. Full article