University of Cuenca

Abstract Understanding the weather and climate of Mesoamerica and the Caribbean remains challenging due to complex hydroclimate interactions, limited observations, and poor representation of regional processes in global models. We introduce the Mesoamerica Affinity Group (MAAG), a National Science Foundation (NSF) National Center for Atmospheric Research (NCAR) and community initiative that fosters research collaboration to advance weather and climate science, develop convection-permitting datasets, and promote knowledge exchange. MAAG’s first major contribution is a 2-week convection-permitting simulation of Hurricane Maria (2017) using Model for Prediction Across Scales–Atmosphere (MPAS-A), featuring a novel regional 15- to 3-km variable-resolution mesh over the region. Initial evaluation shows that MPAS-A captures key features like precipitation patterns, the intertropical convergence zone, and low-level jets. Some biases remain, particularly in enhanced land convection and slight deviations in Maria’s track. This novel dataset, now publicly available through NCAR’s Data Archive, supports studies of other extreme events and mesoscale convective systems active during the same period. It offers a valuable resource for the research community. MAAG is a new but rapidly growing initiative achieving notable milestones in a short time. It serves as a collaborative platform for codesigning high-resolution modeling experiments aimed at producing actionable weather and climate information. We invite the community to join MAAG, explore this initial dataset, and advance regional weather and climate research. Significance Statement The Mesoamerica Affinity Group (MAAG), a National Science Foundation (NSF) National Center for Atmospheric Research (NCAR) and community initiative, aims at addressing the complex challenges of understanding weather and climate in Mesoamerica and the Caribbean. MAAG’s first major achievement is a 2-week convection-permitting simulation of Hurricane Maria (2017) using a novel 15- to 3-km variable-resolution mesh. This dataset accurately captures key regional features and is publicly available through NCAR’s Data Archive. By fostering collaboration through data production and sharing, monthly group meetings that serve as a platform for networking and knowledge exchange, and the development of advanced high-resolution datasets, MAAG provides a vital resource for advancing regional weather and climate science. The initiative is rapidly growing, serving as a platform for codesigned modeling experiments aimed at producing actionable climate information for academia and different sectors. We invite the scientific community to join MAAG and advance research in this critical region.

Hearing loss is among the most common chronic diseases worldwide, affecting approximately 466 million people. Increasing evidence suggests that several cardiovascular risk factors traditionally associated with cardiovascular disease may be associated with hearing loss; therefore, we conducted an umbrella review to synthesize existing systematic reviews and meta-analyses evaluating this association. Systematic reviews and meta-analyses were searched in PubMed, Scopus, Web of Science, and the Cochrane Library from inception to April 2025. Methodological quality was assessed using the AMSTAR-2, and the certainty of evidence was evaluated using the GRADE criteria. Ten systematic reviews were included. Associations were observed between hearing loss and multiple cardiovascular risk factors and comorbidities, with the most consistent evidence for cardiovascular disease, metabolic syndrome, hypertension, diabetes, obesity, smoking, and alcohol consumption; findings for total cholesterol and low-density lipoprotein cholesterol were inconsistent or not statistically significant. The reported odds ratios ranged from 0.78 to 4.22. These associations may be related to mechanisms such as microvascular damage and inflammation; however, the certainty of the evidence was generally low. Overall, the findings suggest a possible association between cardiovascular risk factors and hearing loss, although they highlight the need for further longitudinal studies with standardized hearing loss definitions and improved control of confounding factors.

Introduction: Sensory processing is fundamental to the neurophysiological development of individuals with cerebral palsy. Variations in sensory thresholds can influence orofacial motor patterns, impacting feeding and nutritional status. Objective: To characterize the sensory profile, orofacial function, and nutritional status of children and adolescents with cerebral palsy at the Cerebral Palsy Institute of Azuay. Methodology: A cross-sectional descriptive study was conducted. Forty-three participants were evaluated using the Sensory Profile 2. Orofacial function (chewing and swallowing) and nutritional status were also assessed using anthropometry in subsamples corresponding to each area. Results: Low sensory registration predominated (60.5%), reflecting a diminished response to environmental stimuli. In orofacial function, alterations in chewing (85.3%) and dysphagia (73.5%) were noted. Regarding nutritional status, 63% presented with normal weight; However, 18.5% showed evidence of malnutrition due to deficiency, indicating a group of clinical vulnerability. Conclusions: The coexistence of low-schedule profiles with orofacial motor difficulties underscores the importance of integrating sensory assessment into the clinical approach to cerebral palsy. This allows for a deeper understanding of the factors related to feeding and nutritional status in this population.

Acute pulmonary embolism represents a clinical entity associated with high morbidity and mortality, in which early risk stratification is essential to guide therapeutic management. The aim of this study was to evaluate recent scientific evidence on the prognostic value of computed tomography in patients with acute pulmonary embolism. A systematic review of the literature was conducted in accordance with the PRISMA 2020 recommendations, using a structured search of international electronic databases and including observational studies published between 2021 and 2025. Studies assessing computed tomography–derived parameters from CT pulmonary angiography were analyzed, with particular emphasis on markers related to right ventricular overload and their association with early clinical outcomes, such as 30-day mortality and hemodynamic deterioration. The methodological quality of the included studies was assessed using validated tools for prognostic factor research, and results were synthesized qualitatively due to methodological heterogeneity. The analyzed evidence demonstrated that computed tomography provides relevant prognostic information in acute pulmonary embolism. Signs of right ventricular dysfunction and overload, especially the right-to-left ventricular diameter ratio, were consistently associated with early adverse clinical outcomes. In conclusion, the systematic integration of these tomographic findings improves risk stratification in acute pulmonary embolism, although prospective, multicenter studies with standardized criteria are still required to consolidate their clinical application.

Total thyroidectomy is the established surgical standard for hereditary and sporadic medullary thyroid cancer (sMTC). However, for unilateral sporadic tumors, its benefit over lobectomy remains uncertain. To compare oncologic outcomes between total thyroidectomy and lobectomy in patients with sMTC. MEDLINE, Embase, Scopus, and Cochrane Central Register of Controlled Trials were searched from inception to December 2025 to identify comparative studies on patients with sMTC who underwent total thyroidectomy or lobectomy. Reviewers working independently and in duplicate screened titles, abstracts, and full-text articles for eligibility using standardized instructions. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and Meta-Analysis of Observational Studies in Epidemiology (MOOSE) guidelines were used for reporting. Primary outcomes were mortality, overall survival, structural recurrence, biochemical cure, and distant metastasis development. The secondary outcome was postoperative complications. Effect measures were calculated as odds ratios (ORs) or relative risks (RRs) with 95% CIs using a random-effects model. The risk of bias was assessed using the Newcastle-Ottawa Scale. Nine retrospective studies met inclusion criteria and comprised 1371 patients (295 of 397 patients [74.3%] with documented sex were female, and median age ranged from 45.0 to 58.2 years). A total of 531 patients (38.7%) underwent lobectomy and 840 (61.3%) underwent total thyroidectomy. Of 341 tumors, 280 (82.1%) lacked extrathyroidal extension, 284 of 401 tumors (70.8%) measured smaller than 2 cm (70.8%), and 197 of 343 tumors (57.4%) were node negative; central neck dissection was performed in 445 of 492 patients (90.4%). Multifocal disease was uncommon and reported in 30 of 330 patients (9.1%). Mortality did not differ at 5 years (RR, 0.30; 95% CI, 0.07-1.35) or beyond (RR, 1.00; 95% CI, 0.40-2.47). Overall survival at 5 years was similar (RR, 1.02; 95% CI, 0.94-1.11). Total thyroidectomy was not associated with lower structural recurrence rates at 5 years (OR, 0.45; 95% CI, 0.14-1.49), but it was associated beyond 5 years (OR, 7.26; 95% CI, 1.07-49.21). No differences were observed for biochemical cure at 5 years (OR, 0.86; 95% CI, 0.47-1.56) or beyond 5 years (OR, 0.87; 95% CI, 0.26-2.89). Distant metastasis development did not differ at 5 years (OR, 1.64; 95% CI, 0.09-31.52). Sensitivity analyses revealed no differences across any outcomes. Postoperative complications were more common in total thyroidectomy. Risk of bias was high in 4 studies, moderate in 4, and low in 1. Findings in this systematic review and meta-analysis, based on data from retrospective studies, suggest that thyroid lobectomy may be associated with oncologic outcomes comparable to total thyroidectomy in selected patients with sMTC.

Entre la gran cantidad de organismos que alberga el suelo, se dan relaciones de diferentes tipos; las relaciones simbióticas, muy conocidas entre microorganismos y plantas han sido reportadas para diferentes especies, sin embargo, poco se conoce sobre las interacciones entre Macleania rupestris y los microorganismos asociados a ella. M. rupestris, conocida localmente como «joyapa» es una especie nativa de los Andes perteneciente a la familia Ericaceae, su fruto es una baya comestible consumida por comunidades locales, aves (incluyendo algunas en peligro de extinción) y mamíferos como el oso andino, siendo una especie de gran importancia ecológica. Con el fin de identificar microorganismos cultivables promotores del crecimiento vegetal, se aislaron bacterias a partir de raíces de joyapa y se evaluó en condiciones in vitro, la capacidad de los aislados de solubilizar fosfatos y producir Ácido Indol Acético [AIA]. Doce de las 20 cepas bacterianas aisladas presentaron halos de solubilización de fosfato cuando se sembraron en medio NBRIP y ninguna evidenció producción de AIA al evaluarlas mediante tinción con el reactivo de Kovacs luego de su cultivo por 24 horas en medio enriquecido con triptófano 1%. Las cepas que presentaron resultados positivos para la prueba de solubilización de fosfato fueron inoculadas en plántulas de joyapa para evaluar su efecto en el desarrollo. Luego de 12 semanas se registró el crecimiento de las plantas (tamaño final menos tamaño inicial), número de hojas, número y longitud de raíces y biomasa. Los resultados mostraron diferencias estadísticamente significativas entre el tratamiento control (no inoculado) y la cepa 12 para la variable crecimiento y entre el tratamiento control y las cepas 2, 6, 11 y 12 para la variable biomasa, superando al testigo en ambos casos. Los resultados evidencian la capacidad de algunos aislados bacterianos asociados a M. rupestris de solubilizar fosfatos en condiciones in vitro y muestran los efectos positivos que algunos de ellos tienen en el desarrollo vegetal de esta especie. El aislamiento y caracterización de cepas bacterianas que promueven el desarrollo vegetal podría llevar al desarrollo de biofertilizantes que faciliten el cultivo de esta especie andina de importancia ecológica y potencien su uso en sistemas de producción a fin de generar alternativas para la conservación y uso sustentable de ésta y otras bayas andinas y la fauna asociada a ellas. Finalmente, es importante recalcar que el suelo es una fuente de valiosos recursos que necesitan ser conservados y explorados para su aprovechamiento sustentable.

Introduction: Medical imaging is essential for detecting pathologies, but it depends on the radiologist's experience. Additionally, artificial intelligence has emerged as a promising tool to improve accuracy and efficiency, although it is still debated whether it can replace the radiologist or should be a complement. Objective: To evaluate the role of artificial intelligence in medical imaging, analyzing its ability to complement or replace professionals in interpreting medical studies. Also, to identify the advantages, limitations, and impact of AI in modern radiology. Method: A systematic literature review was conducted on the use of artificial intelligence in medical imaging, focusing on studies from the last 5 years. The search included articles from high-impact scientific journals. Results: Artificial intelligence has demonstrated performance similar to that of radiologists in interpreting images of common pathologies. However, its clinical implementation faces several challenges, such as the need for large volumes of high-quality data and the difficulty of addressing complex clinical situations. Additionally, the importance of human supervision in decision-making and contextual image interpretation is emphasized, as AI cannot fully replicate this. Conclusions: AI should not be considered a replacement for the radiologist but rather a complement that helps reduce errors, optimize time, and enable more accurate medical care. The successful integration of AI in radiology depends on continuous collaboration between technology and healthcare professionals, as well as proper training and regulation.

Abstract. Tropical dry forests play an important role in the global carbon cycle, but their responses to climate variability are still not well understood. Using a three-year period (April 2022 to March 2025) of eddy covariance measurements, we studied seasonal and annual controls on carbon balances in a Tumbesian dry forest in Southern Ecuador. During the study period, the forest functioned as a net carbon sink, with a net ecosystem exchange (NEE) of -285 gCm−2 year−1. The strongest carbon uptake occurred during the wet period (Feb–May) with 173.86 ± 66 gCm−2 month−1, while it was reduced to 39.80 ± 8.12 gCm−2 month−1 in the dry season (August–November). Light use efficiency (LUE) and water use efficiency (WUE) were used to characterize the functional controls on carbon fluxes at both seasonal and annual scales. WUE showed relatively stable water–carbon exchange, whereas LUE displayed clear seasonal variation, reflecting the strong influence of seasonal vegetation growth and greenness. Principle component analysis (PCA) was conducted to further analyze controlling mechanisms in carbon fluxes. Seasonal results showed that gross primary productivity (GPP) was mainly controlled by energy-related factors, while ecosystem respiration (Reco) was primarily driven by a moisture–temperature gradient. Annually, GPP was predominantly influenced by variations in vapor pressure deficit (VPD), soil temperature (Ts), and incoming radiation (Rg), reflecting a strong coupling between surface energy balance and atmospheric moisture demand. These drivers were further modulated by ENSO related climate variability, as reflected by shifts in their PCA loadings across years. Overall, the results reveal a decoupling between photosynthesis and respiration and show that tropical dry forests are highly vulnerable to increasing climate extremes, highlighting the need for improved representation of these processes in Earth system models.

Intelligent Transportation Systems (ITS) have progressively incorporated machine learning to optimize traffic efficiency, enhance safety, and improve real-time decision-making. However, the traditional centralized machine learning (ML) paradigm faces critical limitations regarding data privacy, scalability, and single-point vulnerabilities. This study explores FL as a decentralized alternative that preserves privacy by training local models without transferring raw data. Based on a systematic literature review encompassing 39 ITS-related studies, this work classifies applications according to their architectural detail—distinguishing systems from models—and identifies three families of federated learning (FL) frameworks: privacy-focused, integrable, and advanced infrastructure. Three representative frameworks—Federated Learning-based Gated Recurrent Unit (FedGRU), Digital Twin + Hierarchical Federated Learning (DT + HFL), and Transfer Learning with Convolutional Neural Networks (TFL-CNN)—were comparatively analyzed against a client–server baseline to assess their suitability for ITS adaptation. Our qualitative, architecture-level comparison suggests that DT + HFL and TFL-CNN, characterized by hierarchical aggregation and edge-level coordination, are conceptually better aligned with scalability and stability requirements in vehicular and traffic deployments than pure client–server baselines. FedGRU, while conceptually relevant as a meta-framework for coordinating multiple organizational models, is primarily intended as a complementary reference rather than as a standalone architecture for large-scale ITS deployment. Through application-level evaluations—including traffic prediction, accident detection, transport-mode identification, and driver profiling—this study demonstrates that FL can be effectively integrated into ITS with moderate architectural adjustments. This work does not introduce new experimental results; instead, it provides a qualitative, architecture-level comparison and adaptation guideline to support the migration of ITS applications toward federated learning. Overall, the results establish a solid methodological foundation for migrating centralized ITS architectures toward federated, privacy-preserving intelligence, in alignment with the evolution of edge and 6G infrastructures.