Search Results (161)

The kinetics of liquid transport in textiles are determined by the thermodynamic boundary conditions and the substrate’s structure. The knitting process offers a wide range of possibilities for modifying the fabric structure, making it ideal for high-performance garments and technical applications. Given the highly complex nature of textiles’ interaction with liquids, this paper investigates how fabric structure affects combined wicking and drying behaviour. This facilitates comprehension of the underlying transport processes on the yarn and fabric scale, which is important for understanding the behaviour of the material as a whole. The presented experiment combines analysis of wicking through radial liquid spread using imaging techniques and analysis of the drying process through gravimetric measurement of evaporation. Eight samples of single jersey knitted fabrics were produced using polyester yarns of different texturization and fibre diameters on flat and circular knitting machines. The fabrics demonstrate significantly different wicking behaviours depending on their structure. The fabric’s drying time and rate are directly linked to the macroscopic spread of the liquid. Large inter-yarn pores hinder liquid spread. For the lowest liquid saturations, the yarn structure plays a critical role. Using fine, dense yarns can hinder convective drying within the yarn. Textured yarns tend to exhibit higher specific drying rates. The results offer a comprehensive insight into the interplay between the fabric’s structure and its wicking and drying behaviour, which is crucial for the development of functional fabrics in the knitting process. Full article

Introduction: Breast density is a well-recognized factor in breast cancer risk assessment, with higher density linked to increased malignancy risk and reduced sensitivity of conventional mammography. Background parenchymal enhancement (BPE), observed in contrast-enhanced imaging, reflects physiological contrast uptake in non-pathologic breast tissue. While extensively characterized in breast MRI, the role of BPE in contrast-enhanced mammography (CEM) remains uncertain due to inconsistent findings regarding its correlation with breast density and cancer risk. Unlike breast density—standardized through the ACR BI-RADS lexicon—BPE lacks a uniform classification system in CEM, leading to variability in clinical interpretation and research outcomes. To address this gap, we introduce the BPE-CEM Standard Scale (BCSS), a structured four-tiered classification system specifically tailored to the two-dimensional characteristics of CEM, aiming to improve consistency and diagnostic alignment in BPE evaluation. Materials and Methods: In this retrospective single-center study, 213 patients who underwent mammography (MG), ultrasound (US), and contrast-enhanced mammography (CEM) between May 2022 and June 2023 at the “A. Perrino” Hospital in Brindisi were included. Breast density was classified according to ACR BI-RADS (categories A–D). BPE was categorized into four levels: Minimal (< 10% enhancement), Light (10–25%), Moderate (25–50%), and Marked (> 50%). Three radiologists independently assessed BPE in a subset of 50 randomly selected cases to evaluate inter-observer agreement using Cohen’s kappa. Correlations between BPE, breast density, and age were examined through regression analysis. Results: BPE was Minimal in 57% of patients, Light in 31%, Moderate in 10%, and Marked in 2%. A significant positive association was found between higher breast density (BI-RADS C–D) and increased BPE (p < 0.05), whereas lower-density breasts (A–B) were predominantly associated with minimal or light BPE. Regression analysis confirmed a modest but statistically significant association between breast density and BPE (R² = 0.144), while age showed no significant effect. Inter-observer agreement for BPE categorization using the BCSS was excellent (κ = 0.85; 95% CI: 0.78–0.92), supporting its reproducibility. Conclusions: Our findings indicate that breast density is a key determinant of BPE in CEM. The proposed BCSS offers a reproducible, four-level framework for standardized BPE assessment tailored to the imaging characteristics of CEM. By reducing variability in interpretation, the BCSS has the potential to improve diagnostic consistency and facilitate integration of BPE into personalized breast cancer risk models. Further prospective multicenter studies are needed to validate this classification and assess its clinical impact. Full article

In recent years, the persistent emergence of novel infectious pathogens (epitomized by the global coronavirus disease-2019 (COVID-2019) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)) has propelled nucleic acid testing (NAT) into an unprecedented phase of rapid development. As a key technology in modern molecular diagnostics, NAT achieves precise pathogen identification through specific nucleic acid sequence recognition, establishing itself as an indispensable diagnostic tool across diverse scenarios, including public health surveillance, clinical decision-making, and food safety control. The reliability of NAT systems fundamentally depends on reference materials (RMs) that authentically mimic the biological characteristics of natural viruses. This critical requirement reveals significant limitations of current RMs in the NAT area: naked nucleic acids lack the structural authenticity of viral particles and exhibit restricted applicability due to stability deficiencies, while inactivated viruses have biosafety risks and inter-batch heterogeneity. Notably, pseudovirus has emerged as a novel RM that integrates non-replicative viral vectors with target nucleic acid sequences. Demonstrating superior performance in mimicking authentic viral structure, biosafety, and stability compared to conventional RMs, the pseudovirus has garnered substantial attention. In this comprehensive review, we critically summarize the engineering strategies of pseudovirus platforms and their emerging role in ensuring the reliability of NAT systems. We also discuss future prospects for standardized pseudovirus RMs, addressing key challenges in scalability, stability, and clinical validation, aiming to provide guidance for optimizing pseudovirus design and practical implementation, thereby facilitating the continuous improvement and innovation of NAT technologies. Full article

Nephrolithiasis, predominantly driven by calcium oxalate (CaOx) crystal deposition, poses a significant global health burden due to its high prevalence and recurrence rates and limited preventive/therapeutic options. Recent research has underscored a pivotal role for macrophage polarization in nephrolithiasis pathogenesis. Pro-inflammatory phenotype macrophages exacerbate crystal-induced injury and foster stone formation by amplifying crystal adhesion via an NF-κB–IL-1β positive-feedback axis that sustains ROS generation and NLRP3 inflammasome activation, whereas anti-inflammatory phenotype macrophages facilitate crystal clearance and tissue repair. We have summarized the research on treating nephrolithiasis and related renal injury by targeting macrophage polarization in recent years, including therapeutic approaches through pharmacological methods, epigenetic regulation, and advanced biomaterials. At the same time, we have critically evaluated the novel therapeutic strategies for macrophage reprogramming and explored the future development directions of targeting macrophage reprogramming for nephrolithiasis treatment, such as using single-cell/spatial omics to reveal the heterogeneity of macrophages in the stone microenvironment, chimeric antigen receptor macrophages (CAR-Ms) as a potential therapy for specific crystal phagocytosis in certain areas, and multi-omics integration to address inter-patient immune differences. This review highlights that macrophage reprogramming is a transformative frontier in nephrolithiasis management and underscores the need for further research to translate these molecular insights into effective clinical applications. Full article

Background: Psychological problems post-stroke can negatively impact stroke survivors. Although general psychological services exist (e.g., NHS Talking Therapies), access remains limited, particularly for individuals with post-stroke communication and cognitive impairments. Stroke service staff report low confidence in managing psychological distress. This study is the first to explore the barriers and facilitators to implementing a novel intervention package comprising a cross-service care pathway and staff training to enhance post-stroke psychological provision. Methods: Staff from stroke and mental health services in four UK regions, recruited through purposive sampling to ensure diversity of services and professional roles, participated in semi-structured interviews or focus groups, guided by the Theoretical Domains Framework (TDF), before and after implementation of the intervention package. Pre-implementation interviews/groups identified anticipated barriers and facilitators to implementation and training needs, informing the development of site-specific intervention packages; post-implementation interviews/groups explored experienced barriers, facilitators and perceptions of the intervention. Interviews underwent thematic analysis using the TDF. Results: Fifty-five staff participated pre-implementation and seventeen post-implementation, representing stroke (e.g., nurse, physiotherapist, consultant) and psychology (e.g., counsellor, psychological therapist) roles across acute, rehabilitation, community, and voluntary services. Challenges anticipated pre-implementation included: limited specialist post-stroke psychological support; low staff confidence; and fragmented service pathways. Post-implementation findings indicated increased staff knowledge and confidence, enhanced screening and referral processes, and stronger inter-service collaboration. Implementation success varied across sites (with some sites showing greater ownership and sustainability of the intervention) and across staff roles (with therapy staff more likely than nursing staff to have received training). Conclusions: Effective implementation of an intervention package to increase psychological provision post-stroke requires staff engagement at all levels across all services. Staff investment influenced ownership of the intervention package, beliefs about priorities and overall enhancement of service capability. Full article

This study examines the added value of territorial cooperation within the LEADER approach, a key pillar of the EU’s rural development policy. Both interterritorial and transnational cooperation projects empower Local Action Groups (LAGs) to tackle common challenges through innovative and community-driven strategies. Drawing on over 3000 projects since 1994, LEADER cooperation has proven its ability to deliver tangible results—such as joint publications, pilot projects, and shared digital platforms—alongside intangible benefits like knowledge exchange, improved governance, and stronger social capital. By facilitating experiential learning and inter-organizational collaboration, cooperation enables stakeholders to work across territorial boundaries and build networks that respond to both national and transnational development issues. The interaction among diverse actors often fosters innovative responses to local and regional problems. Using a mixed-methods approach, including case studies of Italian LAGs, this research analyses the dynamics, challenges, and impacts of cooperation, with a focus on learning processes, capacity building, and long-term sustainability. Therefore, this study focuses not only on project outcomes but also on the processes and learning dynamics that generate added value through cooperation. The findings highlight how territorial cooperation promotes inclusivity, fosters cross-border dialogue, and supports the development of context-specific solutions, ultimately enhancing rural resilience and innovation. In conclusion, LEADER cooperation contributes to a more effective, participatory, and sustainable model of rural development, offering valuable insights for the broader EU cohesion policy. Full article

Introduction: We developed a computed tomography (CT)-based tool designed for automated segmentation of deltoid muscles, enabling quantification of radiomic features and muscle fatty infiltration. Prior to use in a clinical setting, this machine learning (ML)-based segmentation algorithm requires rigorous validation. The aim of this study is to conduct shoulder expert validation of a novel deltoid ML auto-segmentation and quantification tool. Materials and Methods: A SwinUnetR-based ML model trained on labeled CT scans is validated by three expert shoulder surgeons for 32 unique patients. The validation evaluates the quality of the auto-segmented deltoid images. Specifically, each of the three surgeons reviewed the auto-segmented masks relative to CT images, rated masks for clinical acceptance, and performed a correction on the ML-generated deltoid mask if the ML mask did not completely contain the full deltoid muscle, or if the ML mask included any tissue other than the deltoid. Non-inferiority of the ML model was assessed by comparing ML-generated to surgeon-corrected deltoid masks versus the inter-surgeon variation in metrics, such as volume and fatty infiltration. Results: The results of our expert shoulder surgeon validation demonstrates that 97% of ML-generated deltoid masks were clinically acceptable. Only two of the ML-generated deltoid masks required major corrections and only one was deemed clinically unacceptable. These corrections had little impact on the deltoid measurements, as the median error in the volume and fatty infiltration measurements was <1% between the ML-generated deltoid masks and the surgeon-corrected deltoid masks. The non-inferiority analysis demonstrates no significant difference between the ML-generated to surgeon-corrected masks relative to inter-surgeon variations. Conclusions: Shoulder expert validation of this CT image analysis tool demonstrates clinically acceptable performance for deltoid auto-segmentation, with no significant differences observed between deltoid image-based measurements derived from the ML generated masks and those corrected by surgeons. These findings suggest that this CT image analysis tool has potential to reliably quantify deltoid muscle size, shape, and quality. Incorporating these CT image-based measurements into the pre-operative planning process may facilitate more personalized treatment decision making, and help orthopedic surgeons make more evidence-based clinical decisions. Full article

Peste des petits ruminants virus (PPRV) has been classified into four lineages based on the nucleocapsid and fusion genes, with lineage IV strains being the most widely distributed. In Africa, recent epidemiological data revealed that PPRV lineage IV is increasingly displacing other lineages in prevalence, suggesting a competitive advantage in viral transmission and adaptability. Moreover, a lineage IV strain was the only confirmed strain in Europe and Asia. In this study, a one-step Taqman quantitative real-time reverse transcription polymerase chain reaction (RT-qPCR) assay for lineage IV PPRV was established by targeting the hemagglutinin (H) gene. The results indicated that this method could detect approximately six copies of PPRV RNA, indicating high sensitivity. No cross-reactions with related viruses or other lineages of PPRV were observed. The results of a repeatability test indicated that the coefficient of variation values were low in both the inter-assay and intra-assay experimental groups. Detection of field samples indicated that all positive samples could be detected successfully using the developed method. This RT-qPCR assay provides a valuable tool to facilitate targeted surveillance and rapid differential diagnosis in regions with active circulation of PPRV lineage IV, enabling timely epidemiological investigations and strain-specific identification. Full article

Background: “CFTR modulators” (also named “caftor”) have been developed and introduced into clinical practice to improve the functionality of defective CFTR protein. Therapeutic drug monitoring (TDM) is not currently used for CFTR modulators in routine clinical practice and there is still much to learn about the pharmacokinetic/pharmacodynamic (PK/PD) and the safety profiles of these drugs in a real-world setting. Moreover, therapeutic ranges are not yet available for both pediatric and adult cystic fibrosis (CF) patients. Methods: A new and sensitive liquid chromatography tandem mass spectrometry (LC-MS/MS) method for contemporary quantification of ivacaftor (IVA), tezacaftor (TEZ) and elexacaftor (ELX) in plasma samples has been developed and validated. The clinical performance of our method has been tested on samples collected during the routine clinical practice from n = 25 pediatric patients (aged between 7 and 17 years) affected by cystic fibrosis. This LC-MS/MS method has been validated according to ICH (International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use) guidelines for the validation of bioanalytical methods. Results: Our method fulfilled ICH guidelines in terms of accuracy, precision, selectivity, specificity and carry-over. Intra- and inter-day accuracy and precision were ≤15%. The 9-day autosampler stability was 90–100% for TEZ and ELX; meanwhile, it fell to 76% for IVA. An injection volume of 1 µL and a wider quantification range (0.1–20 µg/mL) represent a novelty of our method in terms of sensitivity and fields of application. Finally, the evaluation of PK exposure parameters for IVA revealed strong agreement with previously published reports and with results from the summary of product characteristics (SmPCs). Conclusions: This method could be adopted to contemporarily measure ELX/TEZ/IVA plasma levels for both PK studies and monitor therapy compliance, especially in case of poor or partial responses to treatment, or to evaluate drug–drug interactions when multiple concomitant medications are required. Considering also the high cost burden of these medications to the health system, a TDM-based approach could facilitate more cost-effective patient management. Full article

The time-dependent dielectric breakdown (TDDB) degradation mechanism, governed by the synergistic interaction of multiphysics fields, plays a pivotal role in the performance degradation and eventual failure of semiconductor devices and advanced packaging back-end-of-line (BEOL) structures. This work specifically focuses on the dielectric breakdown mechanism driven by metal ion migration within inter-metal dielectric layers, a primary contributor to TDDB degradation. A SPICE-compatible modeling approach is developed to accurately capture the dynamics of this ion migration-induced degradation. The proposed model is rooted in the fundamental physics of metal ion migration and the evolution of conductive filaments (CFs) within the dielectric layer under operational stress conditions. By precisely characterizing the degradation behavior induced by TDDB, a SPICE-compatible degradation model is developed. This model facilitates accurate predictions of resistance changes across a range of operational conditions and lifetime, encompassing variations in stress voltages, temperatures, and structural parameters. The predictive capability and accuracy of the model are validated by comparing its calculated results with numerical ones, thereby confirming its applicability. Furthermore, building upon the established degradation model, the impact of line-edge roughness (LER) is incorporated through a process variation model based on the power spectral density (PSD) function. This PSD-derived model provides a quantitative characterization of LER-induced fluctuations in critical device dimensions, enabling a more realistic representation of process-related variability. By integrating this stochastic variability model into the degradation framework, the resulting lifetime prediction model effectively captures reliability variations arising from real-world fabrication non-uniformities. Validation against simulation data demonstrates that the inclusion of LER effects significantly improves the accuracy of predicted lifetime curves, yielding closer alignment with observed device behavior under accelerated stress conditions. Full article

Early detection and continuous monitoring of diseases are critical to improving patient outcomes, treatment adherence, and diagnostic accuracy. Traditional melanoma diagnosis relies primarily on visual assessment and biopsy, with reported accuracies ranging from 50% to 90% and significant inter-observer variability. Among emerging diagnostic technologies, Raman spectroscopy has demonstrated considerable promise for non-invasive disease detection, particularly in early-stage skin cancer identification. A portable, real-time Raman spectroscopy system could significantly enhance diagnostic precision, reduce biopsy reliance, and expedite diagnosis. However, miniaturization of Raman spectrometers for portable use faces significant challenges, including weak signal intensity, fluorescence interference, and inherent trade-offs between spectral resolution and the signal-to-noise ratio. Recent advances in silicon photonics present promising solutions by facilitating efficient light collection, enhancing optical fields via high-index-contrast waveguides, and allowing compact integration of photonic components. This work introduces a numerical analysis of an integrated digital Fourier transform spectrometer implemented on a silicon-nitride (SiN) platform, specifically designed for Raman spectroscopy. The proposed system employs a switch-based digital Fourier transform spectrometer architecture coupled with a single optical power meter for detection. Utilizing a regularized regression method, we successfully reconstructed Raman spectra in the 800 cm⁻¹ to 1800 cm⁻¹ range, covering spectra of both benign and malignant skin lesions. Our results demonstrate the capability of the proposed system to effectively differentiate various skin cancer types, highlighting its feasibility as a non-invasive diagnostic sensor. Full article

Efficient fruit production, quality improvement, and timely harvesting are essential in olive cultivation, which requires optimised distribution and management of fruiting sites. This study aimed to support sustainable olive crop management by analysing the morphological characteristics of five cultivars (Chemlali, Chetoui, Koroneiki, Meski, and Picholine) under semi-arid Tunisian conditions. Through a detailed architectural analysis, we investigated the relationships between branching patterns, density, distribution of inflorescence and fruit sites, biometric traits (shoot length, internode number, and shoot dimensions), and geometric variability within each cultivar. Three trees per cultivar were analysed across three architectural units. The results showed marked architectural differences, highlighting the need for cultivar-specific strategies in planting, pruning, and orchard management. The distribution of shoots across botanical orders revealed unique branching patterns: Chemlali and Koroneiki showed thinner shoots and higher shoot density, reflecting strong apical dominance and their suitability for hyper-intensive systems. In addition, nonsignificant differences in long shoots’ insertion angles between Meski, Chetoui, and Koroneiki suggest compatibility for co-cultivation, facilitating mechanised maintenance and harvesting. Emphasis on inter-cultivar compatibility and architectural coherence is crucial for orchard design. These findings provide important insights for optimising orchard management practices to improve productivity, fruit quality, and operational efficiency. Full article

Brain tumor classification poses significant challenges in medical imaging, largely due to the heterogeneity and structural complexity of tumors. With Magnetic Resonance Imaging (MRI) serving as a cornerstone for diagnosis, manual interpretation by radiologists is time-consuming and prone to inter-observer variability. Recent advances in deep learning, particularly through the application of Convolutional Neural Networks (CNNs), have transformed medical image analysis by enabling automated, high-accuracy feature extraction. Despite their promise, the performance of CNNs is highly contingent upon optimal hyperparameter tuning, a process that can be both computationally demanding and pivotal for model efficacy. In this study, we employ Simulated Annealing (SA), a probabilistic metaheuristic technique, to methodically optimize the hyperparameters of a CNN architecture designed specifically for classifying brain tumors from MRI scans. Our approach employs a direct representation of hyperparameters alongside an efficient perturbation strategy, facilitating a comprehensive exploration of the parameter space. Experimental evaluations conducted on an extensive MRI dataset (N = 7023 scans classified into glioma, meningioma, no tumor and pituitary) demonstrate that our SA-optimized CNN model achieves a validation accuracy of 98.15%, thereby affirming the potential of SA in enhancing the performance of deep learning systems in medical diagnostics. These findings underscore the critical role of advanced hyperparameter optimization techniques in improving diagnostic accuracy and robustness, ultimately contributing to the development of more reliable and efficient brain tumor classification systems in clinical settings. Full article

At the core of AI-driven electrocardiogram diagnosis lies the precise localization of the QRS complex. While QRS detection methods for multiple leads have been researched adequately in the last few decades, their multi-lead strategies still need to be designed manually. Therefore, a QRS detector that can fuse multiple leads automatically is still worth investigating. Methods: The proposed QRS detector comprises a leads-distillation module (LDM) and a QRS detection module. The LDM can distill multi-lead signals into single-lead ones. This procedure minimizes the weight proportions assigned to noisy leads, enabling the network to generate a novel signal that facilitates the recognition of QRS waves. The QRS detection module, utilizing U-Net, is capable of discerning QRS complexes from the novel signal. Results: Our method demonstrates outstanding performance with a parameter count of only 5216. It achieves an excellent F1 score of 99.83 on the MITBIHA database and 99.77 on the INCART database, specifically in the inter-patient pattern. In the cross-database pattern, our approach maintains a strong performance with an F1 score of 99.22 on the INCART database and an F1 score of 99.09 on the MITBIHA database. Conclusion: Our method provides a novel idea for universal multi-lead QRS detection. It possesses advantages, such as reduced computational parameters, enhanced precision, and heightened compatibility. Significance: Our method canceled the repeated deployment of the QRS detection function to different lead configurations in the electrocardiogram (ECG) diagnostic system. Moreover, the scaling operation may become a simple tool to decrease the computational load of the network. Full article

A certified reference material (CRM) for aflatoxins (AFTB1, AFTB2, AFTG1, AFTG2) and zearalenone (ZEN) in corn/peanut blended vegetable oil (GBW(E)100863) was developed to address the critical need for matrix-specific reference materials in mycotoxin analysis. The CRM was prepared by blending naturally contaminated corn and peanut oils, followed by homogenization, sterilization, and sub-packing. Homogeneity and stability studies were conducted using high-performance liquid chromatography isotope-dilution tandem mass spectrometry with a dilute-and-shoot pretreatment process. The CRM demonstrated excellent homogeneity and stability, with no significant degradation observed under either short-term (65 °C for 14 days) or long-term (25 °C for 12 months) storage conditions. An inter-laboratory comparison involving six authoritative laboratories confirmed the CRM’s accuracy and reliability, with recovery rates ranging from 90.3% to 97.3% and low relative standard deviations (RSDs) of 3.79% to 7.99%. The CRM provided a robust metrological tool for mycotoxin analysis in complex oil matrices. This study not only enriches the national reference materials library but also supports food safety initiatives by facilitating accurate and reliable mycotoxin detection in vegetable oils, thereby enhancing regulatory compliance and public health protection. Full article