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Item type:Item, The Efficacy of Adenoidectomy on Otitis Media with Effusion According to the Age of Child(Springer, 2026) Valenta, Tomáš; Skoloudik, Lukas; Nema, Kristyna; Haviger, Jiří; Chrobok, Viktor; Vesely, Miroslav; Gal, BretislavAim: The study compares the efficacy of adenoidectomy and myringotomy on the treatment of otitis media with effusion (OME) in children depending on the size of adenoids (AV) and age. Material and methods: A total of 509 children with OME lasting at least 3 months, who underwent endoscopic adenoidectomy (AT) and myringotomy without the insertion of a ventilation tube in the years 2010–2019 were enrolled in the study. The efficacy of AT and myringotomy on OME was evaluated with regard to the size of the adenoids, their relationship to the tubal tori, the viscosity of middle ear effusion and the age of the child. To evaluate the effect of age, children were divided into two groups—up to 4 years and over 4 years. Results: The efficacy of AT and myringotomy in OME treatment was demonstrated in both groups. Within the entire group, a significantly greater benefit of AT and myringotomy was found in children with adenoids in contact with the tubal tori (grade B) or compressing them (grade C) (p = 0.039). Other parameters were not statistically significant. Neither the size of the adenoids relative to the choanae, nor the children age, nor the type of middle ear effusion (serous, mucous) had an effect on the treatment. Conclusion: AT and myringotomy is an effective surgical procedure in the treatment of OME not only in children older than 4 years, but also in younger children. The effect of the surgery is affected by the contact of adenoids to tubal tori. © The Author(s) 2025.Item type:Item, Meta-classification of one-class classification models using ranking correlation and nearest neighbor(ELSEVIER, 2026) Hayashi, Toshitaka; Fujita, Hamido; Cimr, Dalibor; Cimler, Richard; Kühnová, JitkaMeta-learning is a technique to learn the learning process. In which, classification of machine learning (ML) models are underexplored direction. One important question is how to generalize various ML models as an input to a meta-classifier. To address this question, this paper advocates treating all ML models as one-class classification(OCC) models, and proposes a meta-classification algorithm of OCC models. The proposal represents OCC models as normality rankings and classifies them using nearest-neighbor and ranking-correlation metrics. The experiment classifies OCC models, where classes correspond to training datasets, algorithms, and hyperparameters. Meta-classification showed high accuracy for distinguishing 1) OCC models trained from dissimilar datasets and 2) four OCC algorithms trained from similar datasets. In addition, the discussion highlights that the classification of OCC models is essentially the classification of datasets that treats multiple samples as a single input. This function is exploratory analyzed using sleeping records. The proposed method can provide a unified solution for classifying OCC models, datasets, and rankings. Source code is uploaded to the public repository https://github.com/ToshiHayashi/ClassOCC.Item type:Item, The Ateni Sioni Church: Sacred Architecture and the Languages of Power in Medieval Iberia(Universidad Complutense Madrid, 2026) Khakhanova, MargaritaThis article explores the medieval Iberian Ateni Sioni Church, built in the 7th century and renowned for its comprehensive mural cycle dated to the 12th century. It draws particular attention to the church’s architectural, topographical, and decorative parallels with a significant Georgian sacred site, the Church of the Holy Cross in Jvari. Rather than viewing these similarities as exclusively formal, the article offers an alternative perspective, focusing especially on the church’s topography and exterior decoration. It examines the donor reliefs on the facade in relation to their spatial context and considers how pilgrims and worshippers engaged with them as well as their possible symbolic significance at the time of their creation. The study demonstrates that, in the medieval period, Ateni Sioni served not only as a religious monument but also as a powerful political statement – a visual embodiment of authority and faith. © 2026 Universidad Complutense Madrid. All rights reserved.Item type:Item, In silico studies into the therapeutic potential of organophosphorus compounds against Alzheimer's disease(KeAi publishing, 2026) de Castro, Alexandre A.; Silva, Henrique F.; Militani, Isabela A.; Mesquita, A.P.L.; da Cunha, Elaine F.F.; Homem-de-Mello, Paula; de Castro Ramalho, TeodoricoBackground Alzheimer’s disease (AD) is the most prevalent form of dementia in the elderly and is characterized by a progressive decline in cognitive function. Therapeutic strategies for AD have largely been guided by the cholinergic hypothesis, which has driven the investigation of acetylcholinesterase inhibitors, including trichlorfon, as potential therapeutic agents. Objectives This study aims to propose novel organophosphorus (OP) compounds as Hss AChE inhibitors, based on the cholinergic hypothesis. Methods A diverse array of OP compounds was systematically evaluated using advanced computational approaches, along with rigorous statistical analyses. The most promising candidates were further investigated through drug-likeness assessments, binding interaction analyses, and mechanistic simulations. Results In particular, inhibitors 12 , 13 , 20 , and 28 emerged as promising candidates for the treatment of AD. However, following a detailed safety analysis, inhibitor 12 was identified as the least toxic inhibitor, exhibiting favorable interactions with the molecular target. Molecular dynamics simulations indicated that inhibitor 12 is more energetically stabilized in the Hss AChE active site than the reference compound trichlorfon. QM/MM calculations further revealed a lower energy barrier for the Hss AChE inhibition mechanism. Conclusions These findings may facilitate the development of more effective and safer therapeutic options for the treatment of AD derived from this class of compounds.Item type:Item, Error-Based Metrics for Fingerprint Vector Similarity Assessment in Clustering-Based Fingerprinting Systems(Institute of Electrical and Electronics Engineers Inc., 2026) Yaro, Abdulmalik Shehu; Malý, Filip; Pražák, PavelIn clustering-based fingerprinting systems, theaccuracy of similarity determination between fingerprint vectors directly affects how well the vectors are clustered in the database. The metric used to assess this similarity is known as the similarity metric. Magnitude-aware metrics such as Euclidean and Manhattan distances are the most commonly used. Despite their popularity, magnitude-aware metrics have several limitations, including sensitivity to differences in vector magnitudes, vulnerability to outliers, and constraints imposed by geometric assumptions. As an alternative, this paper investigates two error-based metrics, mean absolute error (MAE) and mean squared error (MSE), for fingerprint vector similarity determination. The MAE-based metric is proportional to Manhattan distance, and the MSE-based metric is proportional to squared Euclidean distance for a fixed vector dimension. However, their interpretation as average prediction errors offers a different perspective that emphasizes robustness and interpretability. The investigation was conducted using k-means and k-medoids clustering algorithms across four publicly available fingerprint databases with varying properties: SEUG_IndoorLoc, MSI_IndoorLoc, JKKNIUIndoorLoc, and PIEP_UM. The magnitude-aware metrics considered for comparison are Euclidean distance, Manhattan, Minkowski L3, Chebyshev, and Canberra distance. Similarity determination performance was evaluated using mean silhouette scores obtained from 50 independent iterations. Results indicate that the MSE-based metric, despite satisfying only three of the four metric axioms, consistently produces clusters with the highest silhouette scores across all databases considered. The MAE-based metric, while satisfying all four metrics, axioms generally underperform compared to the magnitude-aware metrics considered. These findings indicate that the MSE-based metric is a viable alternative for clustering fingerprint vectors with both k-means and k-medoids algorithms.