The excessive use of vaccines, antibiotics, and other preventive therapeutic agents in conventional broiler production has resulted in the frequent occurrence of antibiotic residues in edible tissues, posing risks to both animal and human health. These residues contribute to the global problem of antimicrobial resistance, highlighting the need for alternative approaches to poultry disease prevention. This study aimed to evaluate whether a comprehensive preventive programme encompassing biosecurity, hygiene, and an antibiotic-free prophylaxis regimen could eliminate the need for prophylactic antibiotic therapy, thereby enabling the production of broiler meat free of antibiotic residues. This approach was contrasted with the conventional production system, in which less rigorous preventive practices necessitate the prophylactic use of broad-spectrum antibiotics, ultimately resulting in detectable residues within the muscle and liver tissues of conventionally reared chickens. Ross 308 broilers were reared under field conditions. in. The control group was reared using conventional prophylactic protocols, including broad-spectrum antibiotics, vitamins, bio-stimulants, mineral supplements, and acidifiers via drinking water. The experimental group received no antibiotics; and instead were treated with hydro-soluble probiotic preparations containing vitamin C, lactose, fructose, and baker’s yeast, combined with continuous water disinfection throughout the fattening period. Microbiological analyses showed the presence of antibiotic residues in the soft tissues of conventionally reared broilers, while no residues were detected in the experimental group. Production performance indicators—mortality rate, vitality, final body weight, and overall cost—were similar to or superior to those in the antibiotic-free group, confirming that broiler meat production without antibiotics is both feasible and economically viable.

Purely cystic meningiomas are extremely rare extra-axial neoplasms that can mimic aggressive malignancies, like glioblastoma multiforme (GBM), because radiologically and intraoperatively they lack a visible solid component and demonstrate postcontrast enhancement and significant vasogenic edema. The authors present a case of a purely cystic intra-axial meningioma mimicking GBM with an accompanying systematic review of the literature. An 84-year-old female presented with expressive dysphasia. MRI revealed a 3-cm inhomogeneously enhancing intra-axial left temporal lobe mass with apparent MRI presentation of central necrosis, suggesting a high-grade glioma. Intraoperatively, the lesion appeared vascular and infiltrative like a GBM; however, histopathological and immunohistochemical analyses (somatostatin receptor 2 antigen positive, progesterone receptor positive, glial fibrillary acidic protein negative) confirmed an angiomatous meningioma (WHO grade 1). Postoperatively, the patient’s symptoms resolved, and 10 years of annual follow-up MRI studies confirmed no recurrence. Only 3 other purely cystic meningioma cases (extra-axial) were identified in the literature. Cystic meningiomas are rarely GBM “imitators.” Surgeons should consider cystic meningioma when classic radiological hallmarks are absent. Despite an aggressive imaging profile, these tumors are biologically and clinically benign. Gross-total resection remains the gold-standard treatment, offering excellent long-term prognosis and the potential for a permanent cure. This case highlights the need for histological tissue diagnosis before final treatment plans are considered. https://thejns.org/doi/10.3171/CASE26438

Materials science is at the crossroad between fundamental and applied sciences. Whether enabling clean energy, next-generation computing, or advanced manufacturing, it shapes the tools we use and the systems we build. As our societies undergo rapid digital, environmental, and technological transitions, materials science becomes even more central. It's a space where innovation can respond to practical challenges while aligning with broader social values. In the European context, that means supporting sustainability, openness, and solidarity -while also strengthening competitiveness.This roadmap explores how digital tools-especially simulation, data science, and AI-are transforming materials research, in connection with the twin digital and energy transition. The digital transition refers to the widespread adoption of digital technologies and data-driven methods across sectors, while the green (or energy) transition focuses on shifting toward sustainable, lowcarbon energy systems-together forming what is often called the twin transition, a joint effort to make economies both smarter and more sustainable.The Roadmap outlines both the technical directions and the cultural shifts needed to make this transformation inclusive and effective. Chapters span from atomic-scale simulations to advanced experimentation, from reproducibility to intelligent optimization, and from institutional reform to education for the next generation.Importantly, this isn't a single viewpoint. The document brings together a wide range of voices: researchers from different disciplines, working across length and time scales. It includes early-career scientists and senior experts. Enabled by activities supported by European Cooperation in Science and Technology (COST), it reflects a commitment to gender and geographic diversity. This plurality doesn't just enrich the content-it makes the vision more robust and relevant.We hope this collection serves not just as a guide, but as an invitation to collaborate-across fields, sectors, and borders-as we reimagine the future of materials science in a digital age.

This study evaluated the effects of different seed priming treatments on the ex situ propagation of the Balkan endemic species Silene sendtneri Boiss. Seeds were subjected to hydropriming, silicic acid, salicylic acid, proline and combined salicylic acid–proline treatments prior to in vitro cultivation. Following aseptic germination, seedlings were transferred to Murashige and Skoog medium supplemented with 1.0 mg L−1 BA and 0.2 mg L−1 IBA, and their shoot multiplication response, rooting, and acclimatization success were assessed. Seed priming significantly influenced subsequent propagation stages. Plantlets derived from seeds treated with 0.5 mM salicylic acid and 10 mM proline + 0.5 mM salicylic acid exhibited the highest multiplication rates, superior morphological quality, increased rooting frequency, and the greatest acclimatization success. Hydropriming and higher proline concentrations generally resulted in reduced propagation performance. The results indicate that seed priming can influence multiple stages of the ex situ propagation process and may represent a simple and practical addition to micropropagation protocols aimed at improving conservation-oriented plant production of endemic species.

Explainable AI (XAI) is essential for building trust in Deep Neural Networks (DNNs). SHAP (SHapley Additive exPlanations) is a well‐known XAI technique for attributing feature importance, but it struggles with exponential computational complexity as the number of features increases. Various approximation methods have been suggested, but they compromise SHAP's theoretical principles. We introduce AA‐SHAP, a novel approach that derives superpixel affinity from the explained model's internals to identify and group superpixels. AA‐SHAP constructs a relevance‐consistency affinity between superpixel interdependence, enabling much faster SHAP calculations on a reduced set of meta‐superpixels while outperforming previous methods in explanation faithfulness. Exact Shapley values are computed on the reduced meta‐superpixel game, preserving all axiomatic guarantees within the aggregated feature space. Evaluated across multiple datasets, both convolutional and transformer classification architectures show that AA‐SHAP produces more faithful attributions than competing methods while improving computational speed and maintaining SHAP's theoretical axioms. The source code is available at https://github.com/vhasic/AA‐SHAP .

<jats:p/>

Limited training data constrains deep learning models for Auditory Attention Decoding (AAD) in hearing aids (HAs). AAD uses electroencephalogram (EEG) data to decode listener's attention, enabling real-time tracking of specific sound sources. However, achieving high AAD performance with short time windows typical in HAs (<=1s) is challenging due to the scarcity of real-world speech-evoked EEG data. To address this issue, we investigate diffusion probabilistic models (DPMs) for generating synthetic speech-evoked EEG data. DPMs learn the underlying complex data structure through a denoising process and can generate realistic samples suitable for data augmentation. We evaluate the use of synthetic EEG data for augmenting datasets in locus-of-attention (LoA) classification tasks. Our experiments demonstrate that DPMs can generate realistic EEG signals and that incorporating synthetic data significantly improves AAD performance compared to models trained solely on measured EEG data (p<0.05). These results highlight the potential of diffusion-based data augmentation to mitigate training data limitations and improve the robustness of short-window AAD models in HA applications.

BACKGROUND Candida auris is a widely distributed yeast that is considered a dangerous pathogen, with reported mortality rates ranging from 30% to 60%. This yeast shows a high level of resistance to several antifungal agents commonly used to treat systemic infections. The pathogen persists on contaminated surfaces, tolerates hospital-grade disinfectants, survives desiccation and spreads easily through direct or indirect contact. It has been reported on all five continents and is increasingly prevalent in Europe. OBJECTIVE To determine the distribution and antifungal susceptibility/resistance of Candida auris isolates identified in Europe until January 2025. METHODS This systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Searches were conducted in EBSCOhost, MEDLINE/PubMed, Scopus and SciELO databases using the terms 'Candida auris' and 'Candidozyma auris', combined with the name of each European country. It was limited to English or Spanish articles published until 31 January 2025, excluding reviews, meta-analyses and book chapters. RESULTS Ninety-one articles reporting antifungal susceptibility were retrieved, covering 2191 clinical isolates of C. auris from 16 countries. Most isolates were from Spain (n = 886, 40.44%), Italy (n = 553, 25.24%), Greece (n = 214, 9.77%), the United Kingdom (n = 182, 8.31%) and Russia (n = 108, 4.93%), accounting for 88.68% of cases. The remaining 248 isolates (11.32%) were reported across 11 other countries. Fluconazole resistance was found in 90.51% (1555/1718), while resistance to amphotericin B and echinocandins was 13.17% (223/1693) and 4.57% (76/1693), respectively. CONCLUSIONS Candida auris has been predominantly detected in Southern Europe, where the majority of clinical isolates exhibit resistance to fluconazole. Consensus is essential for timely diagnosis, targeted treatment and infection control to prevent its spread. New therapeutic options must be explored to manage Candida auris.

Tumor evolution is driven by various mutational processes, ranging from single-nucleotide variants (SNVs) to large structural variants (SVs) to dynamic shifts in DNA methylation. Current short-read sequencing methods struggle to accurately capture the full spectrum of these genomic and epigenomic alterations due to inherent technical limitations. To overcome that, here we introduce an approach to identify and analyze the genomic and epigenetic events in different stages of tumoral evolution from long-read sequencing of single-cell derived sublines. We then use it to profile 23 sublines of a mouse cutaneous melanoma cell line, characterized with distinct growth phenotypes and treatment responses. We develop a computational framework for harmonization and joint analysis of different variant types in the evolutionary context. Uniquely, our framework enables detection of recurrent amplifications of putative driver genes, generated by independent SVs across different lineages, suggesting parallel evolution. In addition, our approach revealed gradual and lineage-specific methylation changes associated with aggressive clonal phenotypes. We also show our set of phylogeny-constrained variant calls along with openly released sequencing data can be a valuable resource for the development and benchmarking of computational methods.

Dementia is a progressive condition that impairs cognitive processes such as memory, decision making, and the ability to manage daily activities. Recent estimates suggest that more than half of all dementia cases could be preventable by addressing their risk factors, including disease comorbidities such as diabetes and vision loss. Yet, we lack a comprehensive molecular map of dementia comorbidities. In this work, we analyzed Austrian nationwide hospital claims data, comprising 13 million hospital stays from 2015 to 2019, to systematically assess dementia-related risk across disease comorbidity patterns, covering both their molecular relationships and their epidemiological overrepresentation. We identified disease trajectories occurring before and at the time of dementia diagnosis, revealing both sex-specific and shared comorbidity patterns. Overall, we identified 51 potential risk factors, with a prominent contribution from endocrine and metabolic disorders. While Parkinson's disease emerged as a strong molecularly related driver of dementia, we also identified emerging and previously under chracterized risk factors, including vitamin D deficiency. This integrative framework provides a comprehensive view of dementia associated disease networks and identifies novel, potentially modifiable risk factors. These results offer new opportunities for targeted prevention strategies and advance our understanding of the complex interplay between comorbidities and dementia development.

BackgroundThe comet assay is a sensitive and widely used technique for assessing DNA damage at the single-cell level. Despite its advantages, traditional manual scoring methods remain time-consuming, subjective and limited in scalability, posing challenges for high-throughput and standardized analysis.ObjectiveThis study aims to develop and evaluate a deep learning-based system for automated comet assay image classification, addressing limitations of manual and semi-automated approaches while enhancing accuracy, reproducibility and processing efficiency.MethodA YOLOv5-based object detection model was trained on a dataset of 875 annotated comet assay images, curated through a three-step expert-reviewed process. Various hyperparameters and data augmentation techniques were optimized to improve performance. The dataset was split into training, validation and test sets, and model performance was evaluated using mAP, precision, recall and confusion matrix analysis.ResultsThe model achieved strong performance, with mAP@0.5 reaching 0.98 and recall exceeding 0.8. Detailed analyses revealed robust learning behavior and generalization capacity. Visual outputs, including precision-recall curves and class-wise confusion matrices, confirmed high classification accuracy, although overlapping comet structures and class imbalance posed challenges. The model demonstrated improved scalability and processing speed compared to traditional tools, supporting its integration into web-based applications.ConclusionThe proposed YOLOv5-based system offers a scalable and accurate solution for automating comet assay analysis. It significantly enhances throughput and reduces human error, supporting its application in genotoxicity testing, biomonitoring and molecular epidemiology. Future work will focus on handling overlapping structures, benchmarking against existing tools and optimizing deployment in real-world laboratory settings.