https://hdl.handle.net/2072/453069 2026-04-04T13:53:02Z 2026-04-04T13:53:02Z Black, Lauren E. Palarea-Albaladejo, Javier Pontes Chiebao, Daniela Hamilton, Claire Bartley, Paul M. Burrells, Alison Underwood, Clare Katzer, Frank Chianini, Francesca https://hdl.handle.net/10256/28470 2026-03-20T07:49:29Z 2026-01-15T00:00:00Z

Pathology and parasite distribution in mice challenged with Toxoplasma gondii from different geographical origins Black, Lauren E.; Palarea-Albaladejo, Javier; Pontes Chiebao, Daniela; Hamilton, Claire; Bartley, Paul M.; Burrells, Alison; Underwood, Clare; Katzer, Frank; Chianini, Francesca Toxoplasma gondii (T. gondii), a zoonotic parasite, can cause severe disease in warm-blooded animals. Pathological changes in murine tissues infected with different T. gondii isolates were studied to establish factors influencing lesion severity and parasite burden. In Study A, mice were orally inoculated with genotype #3, #6 or #8 oocysts. In Study B, mice were inoculated intraperitoneally with genotype #1, #3, #6, #13, #141 or #265 tachyzoites. Mice were euthanised serially and tissues processed for histopathology. In Study A, genotype #6 caused pathology in the liver, brain, lung, intestine and kidney, predominantly associated with tachyzoites, while #8 caused mainly moderate pathology in the brain, lung and liver, usually associated with tissue pseudocysts/cysts. Genotype #3 occasionally caused mild pathology, but the parasite was not visible in examined tissues. In Study B, genotypes #13 and #6 caused systemic infections associated with tachyzoites. Genotypes #3, #141 and #265 caused moderate pathology associated with pseudocysts/cysts in the brain and tachyzoites in peripheral organs. Genotype #1 caused mild pathology associated with pseudocysts/cysts in organs assessed. Comparison of genotype #6 between studies showed parasite stage and inoculation method did not affect the severity of pathology, but for #3, pathology was more severe when mice were inoculated intraperitoneally with tachyzoites compared to those inoculated orally with oocysts. This study confirmed route of infection, T. gondii strain, life stage and dose influence infection outcome and ultimately contributes to the refinement of T. gondii pathogenesis knowledge, which is fundamental for toxoplasmosis management and treatment; This research received no specific grant from any funding agency, commercial or not-for-profit sectors. LEB’s bench fees were funded by Student Awards Agency Scotland (SAAS). Tissue provided for Study A was collected under funding from CAPES (Higher Education Improvement Coordination; DPC, grant number 0382/14-0). Tissue provided for Study B was collected under funding from Ross University School of Veterinary Medicine, the Moredun Research Institute and the Scottish Government (Rural and Environment Science and Analytical Services Division). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript; 3

2026-01-15T00:00:00Z Xiberta, Pau Ruiz Altisent, Marc Vila, Marius Julià i Juanola, Adrià Puig Alcántara, Josep Vilanova, Joan Carles Boada, Imma https://hdl.handle.net/10256/28404 2026-03-12T06:56:52Z 2026-03-02T00:00:00Z

A Native Strategy for Integrating Deep-Learning Models for Segmentation into a Radiological Viewer Xiberta, Pau; Ruiz Altisent, Marc; Vila, Marius; Julià i Juanola, Adrià; Puig Alcántara, Josep; Vilanova, Joan Carles; Boada, Imma The use of deep-learning (DL) models to support and automate medical imaging diagnostic procedures has become an ongoing focus of research and development. Despite advances in the subject, the integration of such solutions into clinical diagnostic workflows remains challenging. Especially focused on end users, the integration of image-based diagnostic functionalities and access to DL models in a single framework is key to ensuring clinical adoption and usability. This paper proposes a native integration strategy that enables the direct use of DL segmentation models within a CE-marked open-source DICOM viewer without relying on external software, containerised environments, or complex APIs. Unlike previous approaches, which often require technical expertise or infrastructure overhead, the proposed method embeds the model execution pipeline directly into the viewer via a dedicated DL module, maintaining compatibility with clinical standards and allowing model parameters to be set directly from the interface or via a configuration file. To validate the feasibility and versatility of this native integration strategy, two use cases are implemented using models trained in different DL libraries: vertebral bodies segmentation and liver segmentation. The approach proves compatible with heterogeneous model architectures, requires minimal user interaction, and preserves clinical usability without disrupting existing workflows. A new DL integration methodology is presented that combines simplicity, flexibility, and clinical readiness. The proposed framework represents a significant step towards standardised, viewer-native deployment of DL tools, facilitating their adoption in regulated healthcare environments and enabling efficient sharing and reuse of DL models across institutions; This work was supported by the Catalan Government (Agència de Gestió d’Ajuts Universitaris i de Recerca, grant number 2021SGR00622), and the Spanish Government (Ministerio de Ciencia e Innovación, grant number PID2022-137647OB-I00); Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature

2026-03-02T00:00:00Z Vila, Marius Julià i Juanola, Adrià Xiberta, Pau Ruiz Altisent, Marc Bermudo, Raquel Fariñas, Oscar Boada, Imma https://hdl.handle.net/10256/28403 2026-03-12T06:56:52Z 2026-03-09T00:00:00Z

Replicating Expert Bone Measurements Automatically: A Case Study with Femoral Allografts Vila, Marius; Julià i Juanola, Adrià; Xiberta, Pau; Ruiz Altisent, Marc; Bermudo, Raquel; Fariñas, Oscar; Boada, Imma Precise morphological assessment of bone allografts is essential for successful graft-recipient matching in tissue banking. Manual measurement protocols, while standard, are labor-intensive, time-consuming, and prone to inter- and intra-observer variability. To overcome these limitations, this work presents an automated system integrated into the BeST-Graft Viewer for performing femoral allograft measurements. The proposed method replicates expert measurement strategies by defining anatomical search zones and reference planes directly within the 3D model of the femur. Reference points are automatically detected based on geometric conditions, and the corresponding anatomical parameters are computed using predefined formulas. The system was validated against manual measurements performed by three experienced experts, using 3D reconstructions of femoral grafts. Manual measurements demonstrated excellent intra- and interrater reliability, with ICC values mostly above 0.98 and all exceeding 0.89, validating their use as a reference standard. The automated system showed near-perfect agreement with the expert average (Pearson’s r = 0.99997), with no significant systematic differences observed. Moreover, the automated process required less than 15 seconds per femur with a single user interaction, in contrast to the several minutes and multiple steps needed for manual annotation. The automated measurement system provides accurate, reproducible, and highly efficient assessment of femoral allografts. It eliminates user variability and reduces measurement time by orders of magnitude. The method offers a reliable and scalable solution for integration into routine clinical workflows, and has potential for extension to other anatomical graft types; Project PID2022–137647OB–I00 funded by MCIN/AEI/10.13039/501100011033 and by ERDF, EU; Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature

2026-03-09T00:00:00Z Boyraz, Asli Pawlowsky-Glahn, Vera Egozcue, Juan José Acar, Aybar Can https://hdl.handle.net/10256/28121 2026-01-19T03:23:50Z

Principal microbial groups: compositional alternative to phylogenetic grouping of microbiome data Boyraz, Asli; Pawlowsky-Glahn, Vera; Egozcue, Juan José; Acar, Aybar Can Statistical and machine learning techniques based on relative abundances have been used to predict health conditions and to identify microbial biomarkers. However, high dimensionality, sparsity and the compositional nature of microbiome data represent statistical challenges. On the other hand, the taxon grouping allows summarizing microbiome abundance with a coarser resolution in a lower dimension, but it presents new challenges when correlating taxa with a disease. In this work, we present a novel approach that groups Operational Taxonomical Units (OTUs) based only on relative abundances as an alternative to taxon grouping. The proposed procedure acknowledges the compositional data making use of principal balances. The identified groups are called Principal Microbial Groups (PMGs). The procedure reduces the need for user-defined aggregation of s and offers the possibility of working with coarse group of s, which are not present in a phylogenetic tree. PMGs can be used for two different goals: (1) as a dimensionality reduction method for compositional data, (2) as an aggregation procedure that provides an alternative to taxon grouping for construction of microbial balances afterward used for disease prediction. We illustrate the procedure with a cirrhosis study data. PMGs provide a coherent data analysis for the search of biomarkers in human microbiota. The source code and demo data for PMGs are available at: https://github.com/asliboyraz/PMGs; The work was supported by The Scienti c and Technological Research Council of Turkey [1059B141601395]. JJE and VPG were supported by the Spanish Ministry of Science, Innovation and Universities and the European Regional Development Fund through grant RTI2018-095518-B-C21 (C22) (MCIU/AEI/FEDER)