University Ucinf

El trauma ocular severo se describe como una lesión aguda que afecta al globo ocular y a sus estructuras adyacentes, produciendo un daño tisular significativo con potencial compromiso de la función visual. Este tipo de lesión puede originarse por distintos mecanismos, como impactos contusos, heridas penetrantes u otros. Habitualmente se relaciona con traumatismos de alta energía (Accidentes de tránsito, agresión por terceros, caídas, entre otros), que comprometen el tercio medio facial. Entre las estructuras óseas o áreas de menor resistencia con mayor predisposición a fracturarse se encuentran el piso orbitario, la pared medial y el techo de la órbita, lo que puede generar alteraciones tanto estética como funcionales en el paciente. Estas anomalías suelen requerir intervención quirúrgica, en casos de fracturas desplazadas, para prevenir secuelas a largo plazo. Dentro de los estudios complementarios que orientan el diagnóstico, destaca la tomografía axial computarizada sin contraste, la cual permite identificar las los rasgos de fractura del Macizo Facial.

In Russia, Western Siberia, Anopheles from maculipennis subgroup comprises three vector species: An. messeae, An. daciae, An. beklemishevi, and the hybrid between An. messeae and An. daciae (Anopheles m-d), which exhibit complex cryptic morphological traits. Traditional morphological methods, such as egg morphology and exochorion coloration, have proven insufficient for reliably distinguishing these closely related species due to overlapping characteristics and high intra-species variability. To overcome these limitations, geometric morphometrics (GM) has emerged as a powerful tool for analyzing cryptic morphology. This article focuses on wing venation patterns, where GM provides precise, quantitative data based on defined anatomical landmarks, enabling detailed assessment of size and shape variation among species. Procrustes ANOVA, principal component analysis (PCA), and canonical variate analysis (CVA) were employed to assess shape variation and species differentiation. Centroid size and its relationship to shape variation were examined using multivariate regression. Despite significant morphological differences, the overlap observed in hybrids (An. m-d) reflects their intermediate position between the parental species. Our analyses revealed significant differences in wing shape and size among An. messeae, An. daciae, An. beklemishevi, and their hybrids, with hybrids showing intermediate morphologies. Landmarks on radial and medial veins were the most consistent contributors to species separation. No evidence of static allometry was detected, and wing shape differences were not explained by size. These findings demonstrate that wing morphometrics, combined with molecular identification, provides a reliable framework for species delimitation and surveillance of malaria vectors in temperate regions.

The study of qualitative morphological variation is essential for taxonomists and professionals involved in the identification and diagnosis of species of agricultural importance. This becomes particularly critical when quarantine decisions depend on the accurate identification of species belonging to highly diverse genera, poorly reviewed taxonomic groups, or sets of morphologically similar species that lack comprehensive identification keys. Geometric morphometrics has proven to be a powerful tool for resolving taxonomic uncertainties and distinguishing economically significant pest insects, even in the absence of formal taxonomic keys. In this study, we applied geometric morphometrics to analyze pronotum shape variation across 11 species of the genus Acanthocephala, representing nearly half of the currently recognized diversity in the genus, including several species of quarantine relevance to the United States. Our results indicate that principal component analysis accounted for 67% of the total shape variation and identified shape patterns that are useful for distinguishing between several species. Discriminate analysis further supported the differentiation among species, with significant differences confirmed through Mahalanobis distances. Although some species exhibited morphological overlaps, particularly among closely related taxa, most comparisons yielded statistically significant results. These findings demonstrate that the shape of the pronotum is a reliable and informative characteristic for species delimitation within the Acanthocephala group. We propose the use of geometric morphometrics as a reproducible, cost-effective, and robust method for species-level identification in taxonomically complex groups, which has valuable applications in quarantine inspection, pest monitoring, and agricultural biosecurity.

El dispositivo de avance mandibular (DAM) representa una alternativa terapéutica innovadora para el tratamiento de la apnea obstructiva del sueño (AOS), una condición respiratoria discapacitante con importantes implicaciones para la calidad de vida y la salud general. Esta revisión narrativa se llevó a cabo mediante una búsqueda exhaustiva en las bases de datos PubMed y Scopus, utilizando palabras clave relacionadas con el DAM y la AOS. Se seleccionaron artículos publicados en los últimos 10 años, los cuales fueron analizados críticamente con el objetivo de sintetizar la evidencia disponible sobre el uso clínico de este dispositivo. En este trabajo se aborda de manera detallada el diseño del DAM como un factor clave para predecir el éxito del tratamiento, su mecanismo de acción, sus características principales, así como las indicaciones y contraindicaciones absolutas para su uso. Los hallazgos de esta revisión buscan contribuir a una mejor comprensión del papel del DAM en el manejo de la AOS, proporcionando una perspectiva basada en la evidencia que permita optimizar su aplicación clínica.

Achieving health equity is an established goal of health organisations (e.g., World Health Organization). Over the last 50 years, racial/ethnic health inequities have persisted across various conditions and subgroups despite increased technology deployment, because the structural factors that drive them remain (e.g., income inequality, lack of access to care). Research at the intersection of health equity and communication shows that technology can be utilised to promote health, prevent disease, and manage illness. However, less work has satisfied the issues described in the OECD report Health at a Glance: Europe 2022, such as to improve population health outcomes and health equity. So, we conducted an umbrella review of health-communication intervention research with a technology component to detail what is known and remains unknown, and used a broad, interdisciplinary perspective to provide recommendations for practice and future research. In them, we champion culturally-sensitive approaches that elevate the role of community members in the design, testing, and implementation of theory-informed interventions, such as participatory design or community-based participatory research strategies. Doing so leverages technology while reducing risk of intervention-generated inequalities. We recommend systematic integration of community-level variables such as social determinants of health to reveal population level intervention strategies to mitigate health inequality.

Purpose This paper aims to examine the potential use of generative artificial intelligence (Gen AI) to enhance digital communication in libraries, with a particular emphasis on social media content creation. It seeks to identify emerging opportunities, explore key challenges and address ethical and institutional considerations related to the adoption of Gen AI in outward-facing communication practices. Design/methodology/approach The study draws on a combination of literature review and practitioner insights from a 2025 workshop hosted by the Librarian Empowerment Division of the Jakarta Public Library. It highlights how librarians are using Gen AI tools to generate posts, captions and visuals for social media, and presents selected examples and reflections from their experiences. Findings Gen AI tools enable libraries to produce social media content more efficiently, particularly in contexts with limited human or technical resources. However, their adoption also reveals challenges such as graphic inconsistencies, copyright ambiguity and ethical concerns. A key issue identified is the absence of clear institutional policies to guide responsible use. In addition, uneven levels of digital competence among staff may hinder experimentation and broader implementation. Originality/value This study contributes to the evolving discourse on AI adoption in libraries by focusing specifically on the underexplored area of Gen AI use in social media communication. It provides a contextualized account of how Indonesian libraries are beginning to apply Gen AI tools for creating public-facing content on social media platforms. By documenting early practices and institutional responses, the paper highlights both the creative possibilities and operational challenges of using AI in library outreach strategies.

Abstract The growing use of large language models for health communication raises important questions about patient preferences, trust, and satisfaction with AI-generated content. We conducted a qualitative survey comparing patient perceptions of clinician versus AI answers to questions about Alzheimer’s disease and genetics. Twenty-six participants scored responses on relevance, trustworthiness, and coherence, and additionally selected a preferred response and provided free-text comments. We found that participants generally preferred AI responses over human-written ones and rated them higher on all evaluation axes. Qualitative analysis of free-text comments identified key factors influencing patient preferences, including clarity, verbosity, certainty, empathy, and numeracy, with patients showing diverse and sometimes contradictory preferences along these dimensions. These findings suggest the AI-generated responses were well-received by patients and may have a role in triaging patients with information-seeking queries. Future work should focus on dynamically identifying patient communication preferences and tailoring communication styles accordingly.

The significant global challenges in eye care are treatment, preventive quality, rehabilitation services for eye patients, and the shortage of qualified eye care professionals. Early detection and diagnosis of eye diseases could allow vision impairment to be avoided. One barrier to ophthalmologists when adopting computer-aided diagnosis tools is the prevalence of sight-threatening uncommon diseases that are often overlooked. Earlier studies have classified eye diseases into two or a small number of classes, focusing on glaucoma, and diabetes-related and age-related vision issues. This study employed three well-established and publicly available datasets to address these limitations and enable automatic classification of a wide range of eye disorders. A Deep Neural Network for Retinal Fundus Disease Classification (DNNRFDC) model was developed, evaluated based on various performance metrics, and compared with four established pre-trained models (EfficientNetB7, EfficientNetB0, UNet, and ResNet152) utilizing transfer learning techniques. The results showed that the proposed DNNRFDC model outperformed these pre-trained models in terms of overall accuracy across all three datasets, achieving an impressive accuracy of 94.10%. Furthermore, the DNNRFDC model has fewer parameters and lower computational requirements, making it more efficient for real-time applications. This innovative model represents a promising avenue for further advancements in the field of ophthalmological diagnosis and care. Despite these promising results, it is essential to acknowledge the limitations of this study, namely the evaluation conducted by using publicly available datasets that may not fully represent the diversity and complexity of real-world clinical scenarios. Future research could incorporate more diverse datasets and explore the integration of additional diagnostic modalities to further enhance the model's robustness and clinical applicability.

Thermal to visual cross-domain face recognition is the process of recognising faces captured in the thermal domain and matching them with the visual domain images. Its significance lies in enabling accurate face recognition across different domains, allowing for enhanced security and surveillance capabilities in various environments, particularly during night-time or adverse weather conditions where thermal imaging excels. By bridging the gap between thermal and visual domains, this cross-domain face recognition improves the overall performance and applicability of face recognition systems in real-world scenarios. This survey explores the existing literature on thermal-to-visual cross-domain face recognition, categorising it into two distinct approaches: the first classification is based on the learning model, and the other is on generalisation approaches. This survey aims to provide a nuanced understanding of the current landscape, methodologies, and challenges in this evolving field by systematically reviewing and categorising the extensive body of research in thermal-to-visual cross-domain face recognition.

Showing empathy to others in pain helps forge stronger social connections. Pain could be expressed through various ways, which may activate distinctive empathic responses. However, few studies have investigated the influence of pain representations on neural responses of pain. This study examined this question by recording EEG signals of 28 neurotypical adults during a pain empathy task. Sixty-two video clips depicting the natural unfolding pain process including three phases (i.e., antecedent cause phase, pain event phase, and affective consequence phase) were used as stimuli. During the antecedent cause phase, the visual perception-related ERPs, such as N100 and P100, were elicited with no significant condition differences (pain vs. no-pain). Later, the pain event phase activated a frontal P300 without condition differences and another posterior-occipital N200 with less negativity during the pain event condition compared to the no-pain condition. Finally, the affective consequence phase, which depicted either pained or neutral faces of the main character, activated a more positive frontal-central-posterior P200 and a more negative posterior-occipital N170 during the pained face condition. In conclusion, our study has identified both cognitive and affective components of empathy which are modulated by different pain representations. Furthermore, the facial coding-related ERP, such as the N170, was specifically elicited by showing facial expressions of pain. In conclusion, empathy for others’ pain involves multiple subcomponents and is influenced by pain representations.