Universidad Nacional de San Martín

IntroductionThe use of artificial intelligence (AI) in cervical cytology has increased substantially due to the need for automated tools that support the early detection of precancerous lesions.MethodsThis systematic review examined deep learning models applied to cervical cytology images, focusing on the architectures used, the datasets employed, and the performance metrics reported. Articles published between 2022 and 2025 were retrieved from Scopus using PRISMA methodology. After applying inclusion criteria and full-text screening, 77 studies were included for RQ1 (models), 75 for RQ2 (datasets), and 71 for RQ3 (metrics).ResultsHybrid models were the most prevalent (56%), followed by convolutional neural networks (CNNs) and a growing number of Vision Transformer (ViT)-based approaches. SIPaKMeD and Herlev were the most frequently used datasets, although the use of private datasets is increasing. Accuracy was the most commonly reported metric (mean 87.76%), followed by precision, recall, and F1-score. Several hybrid and ViT-based models exceeded 92% accuracy. Identified limitations included limited cross-validation, reduced clinical representativeness of datasets, and inconsistent diagnostic criteria.DiscussionThis review synthesizes current trends in AI-based cervical cytology, highlights common methodological limitations, and proposes directions for future research to enhance clinical applicability and standardization.

Trypanosoma cruzi, the causative agent of Chagas disease, presents a major public health challenge in Central and South America, affecting approximately 8 million people and placing millions more at risk. The T. cruzi life cycle includes transitions between epimastigote, metacyclic trypomastigote, amastigote, and blood trypomastigote stages, each marked by distinct morphological and molecular adaptations to different hosts and environments. Unlike other trypanosomatids such as Trypanosoma brucei, T. cruzi does not employ a monoallelic model of antigenic variation; instead, it relies on a diverse repertoire of cell-surface associated proteins encoded by large multigene families, which are essential for infectivity and immune evasion. This study analyzes cell-specific transcriptomes using single-cell RNA sequencing of amastigote and trypomastigote cells to characterize stage-specific surface protein expression during mammalian infection. Through clustering and identification of cell-specific markers, we assigned cells to distinct parasite developmental forms. Analysis of individual cells revealed that surface protein-coding genes, especially members of the trans-sialidase-like superfamily (TcS), are expressed with greater heterogeneity than single-copy genes. Moreover, no recurrent combinations of TcS genes were observed between individual cells in the population. Remarkably, a small subset of TcS mRNAs, encoded by genes preferentially located in the core genomic compartment, are frequently detected across the cell population, whereas the vast majority of TcS mRNAs show low detection frequencies and are mainly encoded in the disruptive compartment. Our findings thus reveal transcriptomic heterogeneity within trypomastigote populations where each cell displays unique TcS expression profiles. Focusing on the diversity of surface protein expression, this research aims to deepen our understanding of T. cruzi cellular biology and infection strategies.

Mapping wetlands is essential for conservation, but their dynamic nature makes them difficult to detect, especially drier-end types. These challenges are amplified in urban and peri-urban areas, where wetlands are small, fragmented, and frequently under-represented in inventories. Furthermore, many mapping efforts lack the methodological transparency required for replicability and adaptation. This study addresses these gaps by presenting a novel, replicable, and cost-effective methodological framework for mapping complex wetlands. The approach is structured as a hierarchical workflow that leverages open data and computational resources. It integrates multi-temporal optical, SAR, and LiDAR-derived features within Google Earth Engine to generate a time-series of annual Land Use/Land Cover (LULC) classifications. This is followed by a post-processing workflow that produces a final filtered Wetlands map, a LULC map (mode), and a Frequency map, allowing for a nuanced understanding of wetland dynamics. We developed and evaluated this framework in the Metropolitan area of Buenos Aires, Argentina, a landscape where such wetlands are often overlooked. The framework successfully delineated multiple wetland classes (Ponds, Wet meadows, Artificial wetlands and Rivers) from non-wetland classes. The final Wetlands map was evaluated using 372 independent points with an overall accuracy of 0.91 (95% CI: [0.87, 0.94]), estimated from the population error matrix to provide unbiased accuracy and area estimates. The primary contribution is the transparent methodological framework itself. All scripts are openly shared to facilitate full replication and adaptation, providing environmental managers a robust tool for inventorying and monitoring challenging wetland systems.

The transition toward a circular plastics economy requires materials that reduce fossil dependency while ensuring realistic end-of-life solutions. Agro-industrial byproducts such as wheat gluten (WG) represent promising feedstocks, although their poor processability and high moisture sensitivity limit practical use. In this work, wheat gluten was plasticized with glycerol and urea to obtain plasticized wheat gluten (fWG), which was then blended with 30 wt % poly(butylene adipate-co-terephthalate) (PBAT), and reinforced with 5 wt % montmorillonite (MMT) clays-either unmodified (CNa), organo-modified (C30B), or a 1:1 mixture-to assess the role of clay chemistry in structural organization and functional performance. PBAT incorporation generated interfacial discontinuities and increased water vapor permeability, whereas clay addition improved phase compatibility and reduced PBAT-dispersed domain size, particularly for C30B. Spectroscopic, diffraction, and scattering analyses revealed that clay incorporation further modified the structural organization of the blends, introducing subtle nanostructural rearrangements without altering the crystalline fraction of the polymers. At the macroscopic level, all clay-containing composites exhibited markedly reduced water vapor permeability, achieving values comparable to neat PBAT despite the lower polyester content, and showed modest improvements in mechanical response. In contrast, solubility was governed primarily by the protein fraction and was not further affected by clays. Under home-composting conditions, PBAT exhibited only minor surface deterioration and remained largely intact, yet the fWG-PBAT blend disintegrated at a rate comparable to the protein matrix alone. Clay-containing composites fragmented more gradually, but all WG-based formulations achieved extensive disintegration within 4 weeks. Altogether, these findings indicate that WG-PBAT-clay composites emerge as a sustainable alternative for the development of films formulated with high agro-industrial byproduct content and realistic end-of-life performance.

Con base en la revisión de algunos balances recientes sobre la situación de los estudios de las ideas en América Latina, el ensayo se propone delinear una perspectiva para una zona de tales estudios articulada desde el enfoque civilizacional, es decir, desde la tematización crítica de la visualización de América Latina como una de las civilizaciones del mundo. A tal fin, se presentan una discusión conceptual, una revisión histórica y un corolario, el cual busca ligar dicha tematización crítica con la posibilidad de densificar nuestra teoría social y de robustecer nuestro ideario integracionista. La perspectiva propuesta permite igualmente retomar las promesas asociadas a la propuesta del diálogo inter-civilizacional, en especial, al diálogo entre las civilizaciones que integran el llamado sur global.