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Enhanced tick species identification in a tertiary care hospital using MALDI–TOF MS: The role of peak numbers
No full textIntroduction: Tick bites are a growing public health concern as ticks act as vectors for various pathogens. Accurate tick species identification is vital to assess disease exposure and determine prophylactic measures. MALDI–TOF MS has emerged as a promising tool for precise tick identification. This study evaluates the performance of MALDI–TOF MS in clinical tick identification, focusing on how the number of peaks present in the reference and sample spectra influences the accuracy of the identification process.
Methods: Between April 2022 and March 2024, 42 tick specimens sent to our hospital were identified using MALDI–TOF MS. The reference spectrum was created with 70 peaks and expanded to include versions with 40, 100, and 130 peaks using Compass Biotyper Explorer v4.1.1. Spectra were analyzed with Flex Analysis v3.4 software. Identification was performed by querying sample spectra against these libraries, with a log score value (LSV) ≥ 1.70 considered accurate for species identification.
Results: Libraries with 40, 100, and 130 peaks improved identification scores for several species, though the degree varied. The highest scores were achieved in 64.3% of specimens. Combining all libraries as a single database yielded LSVs above the 1.70 threshold for all specimens.
Conclusions: The study highlights the species-specific nature of peak importance in spectra and underscores the potential of MALDI–TOF MS as a rapid and accurate tool for tick identification in clinical settings. Enhanced spectral libraries could further improve this technique, aiding timely clinical decisions and effective management of tick bites.
Introducción: Las picaduras de garrapatas son una preocupación en salud pública, ya que las garrapatas son vectores de diversos patógenos. La identificación precisa de especies es esencial para evaluar la exposición a enfermedades y determinar medidas profilácticas. MALDI-TOF MS es una herramienta prometedora para la identificación de garrapatas. Evaluamos el rendimiento de MALDI-TOF MS en la identificación clínica de garrapatas, determinando la influencia del número de picos en los espectros de referencia y de las muestras en la precisión del proceso de identificación.
Métodos: Entre abril de 2022 y marzo de 2024 se identificaron mediante MALDI-TOF MS 42 especímenes de garrapatas enviadas a nuestro hospital. El espectro de referencia se creó con 70 picos y se amplió para incluir versiones con 40, 100 y 130 picos utilizando el software Compass Biotyper Explorer v4.1.1. Los espectros se analizaron con el software Flex Analysis v3.4. La identificación se realizó comparando los espectros de las muestras con estas bibliotecas, considerando un valor de puntuación logarítmica (LSV) ≥ 1,70 como identificación precisa de especie.
Resultados: Bibliotecas con 40, 100 y 130 picos mejoraron las puntuaciones en varias especies, aunque el grado de mejora varió. En el 64,3% de los especímenes se lograron las puntuaciones más altas. Combinando todas las bibliotecas en una única, todos los especímenes obtuvieron valores de LSV por encima del umbral de 1,70.
Conclusiones: La naturaleza específica de la especie es importante en los picos espectrales. MALDI-TOF MS es una herramienta rápida y precisa para la identificación de garrapatas en entornos clínicos. Las bibliotecas espectrales mejoradas podrían optimizar más esta técnica, facilitando decisiones clínicas y el manejo de las picaduras de garrapatas
Use of methanol as a promoter for ammonia combustion
Full text linkThis work aims to study the oxidation of ammonia and methanol mixtures (NH3/CH3OH). For this purpose, laboratory experiments were conducted using a quartz flow reactor at atmospheric pressure, in a temperature range of 875–1425 K. The oxygen excess ratio (λ) and the NH3/CH3OH ratio were modified during the experiments. The experimental results have been simulated with a literature-based kinetic mechanism. The results show that the presence of CH3OH and the oxygen excess ratio affect the conversion of NH3, shifting its oxidation to lower temperatures as these variables increase. The oxidation of both fuels was slightly boosted with increasing CH3OH concentration. The λ study showed that the fuel-lean conditions accelerate NH3 oxidation at lower temperatures whereas do not have the same effect on CH3OH oxidation. The H radical concentration significantly influences fuel consumption, especially in reactions involving CH3OH and NH2, and it is also key for inhibition processes. CH3OH was found to initiate NH3 reactions, with strong competition for OH radicals between the two fuels. Nevertheless, methanol helps reduce ammonia's oxidation temperature. CH2OH was identified as the predominant species following H-abstraction from CH3OH. In the NH3/CH3OH ratio studies, increasing methanol concentration lowered the oxidation temperature of both fuels, with a temperature difference of up to 150 K observed for NH3/CH3OH ratios from 0.6 to 10. Increasing methanol concentration for a given NH3 value also shifted the prominence of secondary reaction pathways, further influencing the overall oxidation process
Integrating radiological and clinical data for clinically significant prostate cancer detection with machine learning techniques
Full text linkIn prostate cancer (PCa), risk calculators have been proposed, relying on clinical parameters and magnetic resonance imaging (MRI) enable early prediction of clinically significant cancer (CsPCa). The prostate imaging–reporting and data system (PI-RADS) is combined with clinical variables predominantly based on logistic regression models. This study explores modeling using regularization techniques such as ridge regression, LASSO, elastic net, classification tree, tree ensemble models like random forest or XGBoost, and neural networks to predict CsPCa in a dataset of 4799 patients in Catalonia (Spain). An 80–20% split was employed for training and validation. We used predictor variables such as age, prostate-specific antigen (PSA), prostate volume, PSA density (PSAD), digital rectal exam (DRE) findings, family history of PCa, a previous negative biopsy, and PI-RADS categories. When considering a sensitivity of 0.9, in the validation set, the XGBoost model outperforms others with a specificity of 0.640, followed closely by random forest (0.638), neural network (0.634), and logistic regression (0.620). In terms of clinical utility, for a 10% missclassification of CsPCa, XGBoost can avoid 41.77% of unnecessary biopsies, followed closely by random forest (41.67%) and neural networks (41.46%), while logistic regression has a lower rate of 40.62%. Using SHAP values for model explainability, PI-RADS emerges as the most influential risk factor, particularly for individuals with PI-RADS 4 and 5. Additionally, a positive digital rectal examination (DRE) or family history of prostate cancer proves highly influential for certain individuals, while a previous negative biopsy serves as a protective factor for others
Hereditary Pancreatic Cancer: Advances in Genetic Testing, Early Detection Strategies, and Personalized Management
Full text linkBackground: Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy with a five-year survival rate of approximately 13% for advanced stages. While the majority of PDAC cases are sporadic, a significant subset is attributable to hereditary and familial predispositions, accounting for approximately 25% of cases. This article synthesizes recent advancements in the understanding, detection, and management of hereditary pancreatic cancer (PC). Results: Our review highlights the critical role of genetic testing (GT) in identifying high-risk individuals (HRIs), with germline pathogenic variants (PVs) found in up to 20% of hereditary PDAC cases. Since the implementation of next-generation sequencing (NGS) panels in 2014, detection capabilities have been significantly enhanced. HRIs can be included in screening programs that facilitate the early detection of PDAC. Early detection strategies, including the use of microribonucleic acid (miRNAs) signatures and novel imaging techniques like hyperpolarized 13C-magnetic resonance spectroscopy (MRS) have shown promising results. The identification of germline pathogenic variants (PVs) or mutations in homologous recombination (HR) genes plays a predictive role in the response to various treatments, prolonging patient survival. Discussion: Universal germline testing for PDAC, as recommended by the National Comprehensive Cancer Network (NCCN), is now a standard practice, facilitating the identification of at-risk individuals and enabling targeted surveillance and intervention. Multidisciplinary management, integrating genetic counseling, imaging, and gastrointestinal services, is essential for optimizing outcomes. Conclusions: Advances in genetic testing and biomarker research are transforming the landscape of hereditary PC management. Early detection and personalized treatment strategies are pivotal in improving survival rates. Ongoing multi-institutional research efforts are crucial for validating biomarkers and developing preventive measures, ultimately aiming to reduce the burden of this aggressive cancer
Shrub Control by Burning and Clearing in the Southern Pyrenees: Effects on Soils After Two Years of Treatment
Full text linkPrescribed burns and selective shrub clearing are widely implemented as management strategies to stop the shrub encroachment of grasslands, decrease fuel loads and fire risks, and improve biodiversity and ecosystem functionality in mountain environments. While the short-term effects of burns on soil have been extensively studied, the impact of mechanical treatments on soil has received comparatively less attention. This study aims to: i) evaluate the physical, chemical, and biological characteristics of subalpine soils influenced by prescribed burns and selective clearing, and ii) assess the effectiveness of these interventions by examining the changes in vegetation cover 2 years after implementation. The research was conducted in the Central Pyrenees, where three plots were selected according to their management type: a prescribed burn plot (B), a clearing plot (CL), and a shrubland control plot (C). The results highlight how both treatments increased soil pH and reduced other properties (EC, BD, moisture, GLU) after 2 years of study, with burned and cleared plots showing similar trends in all cases. The carbon source utilization patterns of soil microbial communities (CLPP) remained unchanged by either treatment, which may indicate the short-term resilience of microbial communities. However, differences in soil microbial activity, as measured by basal soil respiration (bSR), were observed. An increase in bSR was found with shrub removal via mechanical clearing, as evidenced by the constants of the single-compartment model and the average residence time (ART) of organic matter. These changes were primarily driven by the indirect effects of vegetation cover alteration. Shrub cover remained low 2 years after the application of both methods, although prescribed burning resulted in more bare soil and lower plant diversity compared to the cleared plot
An Institutional Theory Framework for Leveraging Large Language Models for Policy Analysis and Intervention Design
Full text linkThis study proposes a comprehensive framework for integrating data-driven approaches into policy analysis and intervention strategies. The methodology is structured around five critical components: data collection, historical analysis, policy impact assessment, predictive modeling, and intervention design. Leveraging data-driven approaches capabilities, the line of work enables advanced multilingual data processing, advanced statistics in population trends, evaluation of policy outcomes, and the development of evidence-based interventions. A key focus is on the theoretical integration of social order mechanisms, including communication modes as institutional structures, token optimization as an efficiency mechanism, and institutional memory adaptation. A mixed methods approach was used that included sophisticated visualization techniques and use cases in the hospitality sector, in global food security, and in educational development. The framework demonstrates its capacity to inform government and industry policies by leveraging statistics, visualization, and AI-driven decision support. We introduce the concept of “institutional intelligence”—the synergistic integration of human expertise, AI capabilities, and institutional theory—to create adaptive yet stable policy-making systems. This research highlights the transformative potential of data-driven approaches combined with large language models in supporting sustainable and inclusive policy-making processes
The effect of a swivel seat on performance, kinematics and body rotation during maximal intensity on-ergometer kayaking
No full textIn recent years, physiological investigations suggested that a kayak seat able to rotate in the horizontal plane (swivel seat) may improve performance, but kinematic data are limited. The aim of this study was to investigate the effect of the swivel seat on kinematics and performance during sprint paddling on an ergometer. Nine experienced kayakers volunteered for this study and each completed two maximal trials of 30 s on the ergometer, one with the swivel seat and the other with a fixed seat. Three-dimensional motion analysis and performance data were collected at 200 Hz during the central 10 s of each trial. The use of the swivel seat was observed to improve performance through a significant increase in peak fly-wheel RPM (p = 0.033) and paddle antero-posterior displacement (p = 0.015) and a significant decrease in right side paddle recovery time (p = 0.043). In conclusion, the use of the swivel seat was associated with kinematic changes that improved performance and decreased the risk of excessive spine rotation. These results offer new insights into understanding the implications of swivel seat use for the dynamics of kayaking
High-frequency gravitational waves detection with the BabyIAXO haloscopes
Full text linkWe present the first analysis using RADES-BabyIAXO cavities as detectors of high-frequency gravitational waves (HFGWs). In particular, we discuss two configurations for distinct frequency ranges of HFGWs: cavity 1, mostly sensitive at a frequency range of 252.8–333.2 MHz, and cavity 2, at 2.504–3.402 GHz, which is a scaled down version of cavity 1. We find that cavity 1 will reach sensitivity to strains of the HFGWs of order h1∼10−21, while cavity 2 will reach h2∼10−20. These represent the best estimations of the RADES-BabyIAXO cavities as HFGW detectors, showing how this setup can produce groundbreaking results in axion physics and HFGWs simultaneously
Mensaje para EINA
No full textLa exposición Mensaje para EINA es el reflejo de una comunidad en constante diálogo. A través de estos carteles, el estudiantado del Grado en Ingeniería en Diseño Industrial y Desarrollo de Producto comparte su visión sobre los valores, las actividades y el espíritu que definen la Escuela de Ingeniería y Arquitectura de la Universidad de Zaragoza. Cada pieza presentada es fruto de un ejercicio de expresión libre dentro de la asignatura Diseño Gráfico Aplicado a Producto. Sin restricciones temáticas más allá de la conexión con EINA, esta libertad ha permitido a quienes han participado explorar y comunicar mensajes que consideran esenciales para su entorno académico. Este proceso no solo fomenta la creatividad, sino que también fortalece el sentido de pertenencia y la capacidad de análisis crítico sobre el contexto en el que se desarrollan como profesionales y como personas. Escuchar lo que el estudiantado tiene que decir es clave para comprender mejor sus inquietudes, aspiraciones y propuestas. En este espacio, el diseño gráfico se convierte en un canal de comunicación que trasciende lo visual para invitarnos a reflexionar sobre la identidad y el futuro de nuestra Escuela
A POD-NN methodology to determine in vivo mechanical properties of soft tissues. Application to human cornea deformed by Corvis ST test
No full textThe interaction between optical and biomechanical properties of the corneal tissue is crucial for the eye’s ability to refract and focus light. The mechanical properties vary among individuals and can change over time due to factors such as eye growth, ageing, and diseases like keratoconus. Estimating these properties is crucial for diagnosing ocular conditions, improving surgical outcomes, and enhancing vision quality, especially given increasing life expectancies and societal demands. Current ex-vivo methods for evaluating corneal mechanical properties are limited and not patient-specific. This study aims to develop a model to estimate in real-time the mechanical properties of the corneal tissue in-vivo. It is composed both by a proof of concept and by a clinical application. Regarding the proof of concept, we used high-fidelity Fluid-Structure Interaction (FSI) simulations of Non-Contact Tonometry (NCT) with Corvis ST® (OCULUS, Wetzlar, Germany) device to create a large dataset of corneal deformation evolution. Proper Orthogonal Decomposition (POD) was applied to this dataset to identify principal modes of variation, resulting in a reduced-order model (ROM). We then trained a Neural Network (NN) using the reduced coefficients, intraocular pressure (IOP), and corneal geometry derived from Pentacam® (OCULUS, Wetzlar, Germany) elevation data to predict the mechanical properties of the corneal tissue. This methodology was then applied to a clinical case in which the mechanical properties of the corneal tissue are estimated based on Corvis ST results. Our method demonstrated the potential for real-time, in-vivo estimation of corneal biomechanics, offering a significant advancement over traditional approaches that require time-consuming numerical simulations. This model, being entirely data-driven, eliminates the need for complex inverse analyses, providing an efficient and accurate tool to be implemented directly in the Corvis ST device