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The rising incidence of noncommunicable diseases, combined with the costs of mitigating climate change, sovereign debt and regional conflicts, is undermining global health security and threatening progress towards achieving the sustainable development goals of the United Nations. The negative impact of these polycrises is disproportionately borne by low- and middle-income countries, which have the highest disease burden and lowest health-care spending. Health digitalization is emerging as a promising countermeasure, accelerated by artificial intelligence (AI) software and quantum computing hardware. We provide a multisector critical analysis of the three key enablers - governance, infrastructure and security--of the responsible AI-enabled digitalization for safe, affordable, equitable and sustainable healthcare systems in low- and middle-income countries. We consider leading use cases in public-private partnerships, democratized sovereign AI and embedded human security. Our analysis proposes that these use cases demonstrate how digital AI-accelerated global health may be advanced as human-centred managed strategic competition. We conducted our analysis through an inclusive range of theoretical perspectives and practical experience spanning academia, industry and practice across the world. We provide recommendations for the responsible management of the key enablers to accelerate global health for all. We anticipate that this paper will be useful for public health decision-makers, both in low- and middle-income countries leading local health digitalization, and in high-income countries supporting this transaction through their technologies, funding and knowledge exchange. [phrase omitted] L'incidence croissante des maladies non transmissibles, associee aux couts de l'attenuation du changement climatique, a la dette souveraine et aux conflits regionaux, compromet la securite sanitaire mondiale et menace l'avancement de la realisation des objectifs de developpement durable des Nations unies. L'impact negatifde ces crises multiples touche de maniere disproportionnee les pays a revenu faible et intermediaire, qui presentent la charge de morbidite la plus elevee et les depenses de sante les plus faibles. La numerisation de la sante apparait comme une contre-mesure prometteuse, acceleree par les logiciels d'intelligence artificielle (IA) et le materiel d'informatique quantique. Nous fournissons une analyse critique multisectorielle des trois principaux catalyseurs (gouvernance, infrastructure et securite) de la numerisation responsable basee sur l'IA pour des systemes de soins de sante surs, abordables, equitables et durables dans les pays a revenu faible et intermediaire. Nous examinons les principaux cas d'utilisation dans les partenariats public-prive, l'IA souveraine democratisee et la securite humaine integree. Notre analyse suggere que ces cas d'utilisation demontrent la capacite des services numeriques mondiaux de sante acceleres par l'IA a progresser en tant que concurrence strategique geree centree sur l'humain. Nous avons mene notre analyse a partir d'un large eventail de perspectives theoriques et d'experiences pratiques couvrant le monde universitaire, le secteur medical et les cabinets medicaux a travers le monde. Nous formulons des recommandations pour une gestion responsable des facteurs cles permettant d'ameliorer la sante mondiale pour tous. Nous sommes d'avis que ce document sera utile aux decideurs en matiere de sante publique, a la fois dans les pays a revenu faible et intermediaire qui promeuvent la numerisation de la sante a l'echelle locale, ainsi que dans les pays a revenu eleve qui soutiennent cette transaction par leurs technologies, un financement et l'echange de connaissances. La creciente incidencia de las enfermedades no transmisibles, junto con los costes de la mitigacion del cambio climatico, la deuda soberana y los conflictos regionales, amenaza la seguridad sanitaria mundial y los avances hacia la consecucion de los Objetivos de Desarrollo Sostenible de las Naciones Unidas. Los paises de ingresos bajos y medios, que tienen la mayor carga de morbilidad y el menor gasto sanitario, asumen de forma desproporcionada el impacto negativo de estas policrisis. La digitalizacion de la sanidad se perfila como una prometedora contramedida, acelerada por el software de inteligencia artificial (IA) y el hardware de computacion cuantica. Se ofrece un analisis critico multisectorial de los tres factores clave: gobernanza, infraestructura y seguridad, de la digitalizacion responsable impulsada por la IA para lograr sistemas de atencion sanitaria seguros, asequibles, equitativos y sostenibles en los paises de ingresos bajos y medios. Se estudian los principales casos de uso en las asociaciones publico-privadas, la IA soberana democratizada y la seguridad humana integrada. El analisis propone que estos casos de uso demuestren como la salud mundial acelerada por la IA digital puede avanzar como competencia estrategica, gestionada y centrada en el ser humano. El analisis se realizo desde varias perspectivas teoricas y experiencias practicas que abarcan el mundo academico, la industria y la practica en todo el mundo. Se presentan recomendaciones para la gestion responsable de los principales factores que aceleran la salud mundial para todos. Se preve que este documento sea de utilidad para los responsables de tomar decisiones en materia de salud publica, tanto en los paises de ingresos bajos y medios que lideran la digitalizacion sanitaria local, como en los paises de ingresos altos que apoyan este proceso a traves de sus tecnologias, financiacion e intercambio de conocimientos., Introduction The rising incidence of noncommunicable diseases, combined with the costs of mitigating the effects of climate change, sovereign debt, human capital migration and regional conflicts, is undermining global health [...]

The fundamental trade-off between current and future reproduction has long been considered to result in a tendency for species that can grow large to begin reproduction at a larger size. Due to the prolonged time required to reach maturity, estimates of tree maturation size remain very rare and we lack a global view on the generality and the shape of this trade-off. Using seed production from five continents, we estimate tree maturation sizes for 486 tree species spanning tropical to boreal climates. Results show that a species' maturation size increases with maximum size, but in a non-proportional way: the largest species begin reproduction at smaller sizes than would be expected if maturation were simply proportional to maximum size. Furthermore, the decrease in relative maturation size is steepest in cold climates. These findings on maturation size drivers are key to accurately represent forests' responses to disturbance and climate change.

Abstract Background Hepatocellular carcinoma (HCC) genetic/transcriptomic signatures have been widely described. However, its proteomic characterization is incomplete. We performed non-targeted quantitative proteomics of HCC samples and explored its clinical, functional, and molecular consequences. Methods Non-targeted quantitative proteomics were performed on cytosolic and nuclear fractions of liver samples [HCC vs. non-tumour adjacent tissue (NTAT), n = 42 patients]. Changes were confirmed in 7 in silico HCC cohorts. Functional and molecular implications were evaluated on HCC-derived cell lines after silencing/overexpressing VARS1 and/or MAGI1. VARS1-overexpressing Hep3B cells were used for in vivo studies [Extreme Limiting Dilution Assay (ELDA) and orthotopic tumour formation]. Quantitative proteomics were performed on VARS1-overexpressing HCC cell lines. Results Quantitative proteomics revealed the dysregulation of the cytosolic and nuclear proteomes in HCC, and defined two proteomic HCC subgroups, the most aggressive associated to the dysregulation of the aminoacyl-tRNA synthetases (ARSs). ARSs dysregulation was corroborated in in silico HCC cohorts and associated to poor prognosis. Patients with ARSs upregulation had genomic/transcriptomic characteristics of the proliferative HCC. Valine tRNA-aminoacyl synthetase (VARS1) was the ARSs most consistently overexpressed and associated to aggressiveness. VARS1 modulation (silencing/overexpression) altered tumour establishment-associated parameters in vitro and/or in vivo. Quantitative proteomics on cells overexpressing VARS1 and rescue experiments identified the downregulation of MAGI1, a tumour suppressor in HCC, as a mediator of VARS1 function. Conclusions Quantitative proteomics defines two prognosis-related proteomic HCC subgroups. ARSs machinery is dysregulated in the aggressive subgroup, bearing potential as prognostic biomarkers. VARS1 promotes aggressiveness through the modulation of MAGI1, representing a novel targetable vulnerability in HCC.

Abstract The increasing aging population and aging-associated diseases have become a global issue for decades. People over 65 show an increased prevalence and greater severity of periodontitis, which poses threats to overall health. Studies have demonstrated a significant association between aging and the dysfunction of neutrophils, critical cells in the early stages of periodontitis, and their crosstalk with macrophages and T and B lymphocytes to establish the periodontal lesion. Neutrophils differentiate and mature in the bone marrow before entering the circulation; during an infection, they are recruited to infected tissues guided by the signal from chemokines and cytokines to eliminate invading pathogens. Neutrophils are crucial in maintaining a balanced response between host and microbes to prevent periodontal diseases in periodontal tissues. The impacts of aging on neutrophils’ chemotaxis, anti-microbial function, cell activation, and lifespan result in impaired neutrophil functions and excessive neutrophil activation, which could influence periodontitis course. We summarize the roles of neutrophils in periodontal diseases and the aging-related impacts on neutrophil functional responses. We also explore the underlying mechanisms that can contribute to periodontitis manifestation in aging. This review could help us better understand the pathogenesis of periodontitis, which could offer novel therapeutic targets for periodontitis.

Abstract Background Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer with limited treatment options and a poor prognosis. The critical role of epigenetic alterations such as changes in DNA methylation, histones modifications, and chromatin remodeling, in pancreatic tumors progression is becoming increasingly recognized. Moreover, in PDAC these aberrant epigenetic mechanisms can also limit therapy efficacy. This study aimed to investigate the expression and prognostic significance of a key epigenetic complex encompassing DNA methyltransferase-1 (DNMT1), the histone methyltransferase G9a, and the scaffold protein UHRF1 in PDAC. We also evaluated the therapeutic potential of an innovative inhibitor targeting these epigenetic effectors. Methods Immunohistochemical analysis of DNMT1, G9a, and UHRF1 expression was conducted in human PDAC tissue samples. Staining was semi-quantitatively scored, and overexpression was defined as moderate to strong positivity. The prognostic impact was assessed by correlating protein expression with patient survival. The antitumoral effects of the dual DNMT1-G9a inhibitor CM272 were tested in PDAC cell lines, followed by transcriptomic analyses to identify underlying mechanisms. The in vivo antitumoral efficacy of CM272 was evaluated in PDAC xenograft and syngeneic mouse models, both alone and in combination with anti-PD1 immunotherapy. Results DNMT1, G9a, and UHRF1 were significantly overexpressed in PDAC cells and stroma compared to normal pancreatic tissues. Simultaneous overexpression of the three proteins was associated with significantly reduced survival in resected PDAC patients. CM272 exhibited potent antiproliferative activity in PDAC cell lines, inducing apoptosis and altering key metabolic and cell cycle-related genes. CM272 also enhanced chemotherapy sensitivity and significantly inhibited tumor growth in vivo without detectable toxicity. Combination of CM272 with anti-PD1 therapy further improved antitumor responses and immune cell infiltration, particularly CD4 + and CD8 + T cells. Conclusions The combined overexpression of DNMT1, G9a, and UHRF1 in PDAC is a strong predictor of poor prognosis. CM272, by targeting this epigenetic complex, shows promising therapeutic potential by inducing apoptosis, reprogramming metabolic pathways, and enhancing immune responses. The combination of CM272 with immunotherapy offers a novel, effective treatment strategy for PDAC.

Resumen: Objetivo: Desarrollar un modelo que prediga con la mayor exactitud posible el estado vital a los 6 meses tras fractura de cadera, utilizando para ello cinco fuentes de datos obtenidas a través del Registro Nacional de Fracturas de Cadera, la Unidad de Gestión Sanitaria y la Dirección Económica. Material y metodología: La población de estudio es una cohorte de pacientes que sufrieron fractura de cadera entre mayo de 2020 y diciembre de 2022. A partir de cinco fuentes diferentes de datos se crea un almacén con las variables necesarias. Se realiza un análisis de valores perdidos y atípicos, así como de desbalanceo de las clases de la variable objetivo («estado vital»). Se entrenan 14 diferentes modelos algorítmicos con los datos de entrenamiento. Se selecciona el modelo que mejor rendimiento obtenga y se realiza una puesta a punto fina. Finalmente se analiza el rendimiento del modelo con datos de test. Resultados: Se crea un almacén de datos con 502 pacientes y 144 variables. El modelo con mejor rendimiento es la regresión lineal. Dieciséis de los 24 casos de pacientes fallecidos son clasificados como vivos, y 14 pacientes vivos son clasificados como fallecidos. Se consigue una sensibilidad del 31%, una precisión del 34% y un área bajo la curva de 0,65. Conclusiones: No se ha conseguido generar un modelo de predicción de éxitus a los 6 meses con nuestra cohorte. Sin embargo, creemos que el método utilizado para generar algoritmos basados en aprendizaje automático puede servir de referencia para futuros trabajos. Abstract: Background and objective: The objective is to develop a model that predicts vital status six months after fracture as accurately as possible. For this purpose we will use five different data sources obtained through the National Hip Fracture Registry, the Health Management Unit and the Economic Management Department. Material and methods: The study population is a cohort of patients over 74 years of age who suffered a hip fracture between May 2020 and December 2022. A warehouse is created from five different data sources with the necessary variables. An analysis of missing values and outliers as well as unbalanced classes of the target variable («vital status») is performed. Fourteen different algorithmic models are trained with the training. The model with the best performance is selected and a fine tuning is performed. Finally, the performance of the selected model is analyzed with test data. Results: A data warehouse is created with 502 patients and 144 variables. The best performing model is Linear Regression. Sixteen of the 24 cases of deceased patients are classified as live, and 14 live patients are classified as deceased. A sensitivity of 31%, an accuracy of 34% and an area under the curve of 0.65 is achieved. Conclusions: We have not been able to generate a model for the prediction of six-month survival in the current cohort. However, we believe that the method used for the generation of algorithms based on machine learning can serve as a reference for future works.

Numerous studies have shown reduced performance in plants that are surrounded by neighbours of the same species1,2, a phenomenon known as conspecific negative density dependence (CNDD)3. A long-held ecological hypothesis posits that CNDD is more pronounced in tropical than in temperate forests4,5, which increases community stabilization, species coexistence and the diversity of local tree species6,7. Previous analyses supporting such a latitudinal gradient in CNDD8,9 have suffered from methodological limitations related to the use of static data10-12. Here we present a comprehensive assessment of latitudinal CNDD patterns using dynamic mortality data to estimate species-site-specific CNDD across 23 sites. Averaged across species, we found that stabilizing CNDD was present at all except one site, but that average stabilizing CNDD was not stronger toward the tropics. However, in tropical tree communities, rare and intermediate abundant species experienced stronger stabilizing CNDD than did common species. This pattern was absent in temperate forests, which suggests that CNDD influences species abundances more strongly in tropical forests than it does in temperate ones13. We also found that interspecific variation in CNDD, which might attenuate its stabilizing effect on species diversity14,15, was high but not significantly different across latitudes. Although the consequences of these patterns for latitudinal diversity gradients are difficult to evaluate, we speculate that a more effective regulation of population abundances could translate into greater stabilization of tropical tree communities and thus contribute to the high local diversity of tropical forests.

One mechanism proposed to explain high species diversity in tropical systems is strong negative conspecific density dependence (CDD), which reduces recruitment of juveniles in proximity to conspecific adult plants. Although evidence shows that plant-specific soil pathogens can drive negative CDD, trees also form key mutualisms with mycorrhizal fungi, which may counteract these effects. Across 43 large-scale forest plots worldwide, we tested whether ectomycorrhizal tree species exhibit weaker negative CDD than arbuscular mycorrhizal tree species. We further tested for conmycorrhizal density dependence (CMDD) to test for benefit from shared mutualists. We found that the strength of CDD varies systematically with mycorrhizal type, with ectomycorrhizal tree species exhibiting higher sapling densities with increasing adult densities than arbuscular mycorrhizal tree species. Moreover, we found evidence of positive CMDD for tree species of both mycorrhizal types. Collectively, these findings indicate that mycorrhizal interactions likely play a foundational role in global forest diversity patterns and structure.

In recent years, plant-based diets have gained popularity due to their health benefits, contributions to animal welfare, and positive impact on environmental sustainability. The objective of this study is to evaluate the sensory acceptability and iron content of vegan cheeses produced with legume protein isolates (LPI), including lupin (Lupinus mutabilis Sweet), black beans (Phaseolus vulgaris L.), and chickpea (Cicer arietinum), as well as cushuro algae (CA). A quantitative experimental study was conducted to evaluate the acceptability and iron content of vegan cheeses. The formulations included LPI and CA. The sample consisted of 127 students selected by probability sampling. Just-about-Right (JAR) method was used to evaluate flavor, odor, texture, and color on a scale of 1 to 9. The Kruskal-Wallis test was applied and the data were analyzed using SPSS v. 26. The nutritional composition showed significant differences (p < 0.001), with lupin cheese (LC) highlighting its high protein and iron content. Sensory evaluation revealed that LC received the highest scores, followed by chickpea cheese (CC). The inclusion of LPI and CA in vegan cheeses represents a significant improvement in their nutritional value and acceptability, offering a promising strategy to combat malnutrition and anemia in vulnerable populations.

ABSTRACT Legionella species evade degradation and proliferate within alveolar macrophages as an essential step for the manifestation of disease. However, most intracellular bacterial pathogens are restricted in neutrophils, which are the first line of innate immune defense against invading pathogens. Bacterial degradation within neutrophils is mediated by the fusion of microbicidal granules to pathogen-containing phagosomes and the generation of reactive oxygen species (ROS) by the phagocyte NADPH oxidase complex. Here, we show that human neutrophils fail to trigger microbicidal processes and, consequently, fail to restrict L. longbeachae. In addition, neutrophils infected with L. longbeachae fail to undergo a robust pro-inflammatory response, such as degranulation and IL-8 production. Here, we identify three strategies employed by L. longbeachae for evading restriction by neutrophils and inhibiting the neutrophil microbicidal response to other bacteria co-inhabiting in the same cell. First, L. longbeachae excludes the cytosolic and membrane-bound subunits of the phagocyte NADPH oxidase complex from its phagosomal membrane independent of the type 4 secretion system (T4SS). Consequently, infected neutrophils fail to generate robust ROS in response to L. longbeachae. Second, L. longbeachae impedes the fusion of azurophilic granules to its phagosome and the phagosomes of bacteria co-inhabiting the same cell through T4SS-independent mechanisms. Third, L. longbeachae protects phagosomes of co-inhabiting bacteria from degradation by ROS through a trans-acting T4SS-dependent mechanism. Collectively, we conclude that L. longbeachae evades restriction by human neutrophils via T4SS-independent mechanisms and utilizes trans-acting T4SS-dependent mechanisms for inhibition of neutrophil ROS generation throughout the cell cytosol.IMPORTANCELegionella longbeachae is commonly found in soil environments where it interacts with a wide variety of protist hosts and microbial competitors. Upon transmission to humans, L. longbeachae invades and replicates within alveolar macrophages, leading to the manifestation of pneumonia. In addition to alveolar macrophages, neutrophils are abundant immune cells acting as the first line of defense against invading pathogens. While most intracellular bacterial species are killed and degraded by neutrophils, we show that L. longbeachae evades degradation. The pathogen impairs the major neutrophils’ microbicidal processes, including the fusion of microbicidal granules to the pathogen-containing vacuole. By inhibiting of assembly of the phagocyte NADPH oxidase complex, the pathogen blocks neutrophils from generating microbicide reactive oxygen species. Overall, L. longbeachae employs unique virulence strategies to evade the major microbicidal processes of neutrophils.

Quienes ejercen en la Farmacia Comunitaria (FC), están acusando un escaso reconocimiento en su labor como profesionales de la salud, así como una escasez de expectativas de futuro y una ausencia de objetivos profesionales definidos, que afecta especialmente a quienes no ejercen como titulares, sino como trabajadores por cuenta ajena. Todo esto está provocando una desmotivación y un desapego hacia el trabajo en la FC, que afecta a quienes hoy trabajan en ese ámbito, pero también es percibido por las personas que aún no han iniciado su trayectoria profesional. Para mantener un buen nivel en la atención farmacéutica y responder a las necesidades de la sociedad es imprescindible contar con profesionales cada día con mayor cualificación, pero es también esencial el reconocimiento y sobre todo la motivación con su trabajo en la FC. El desarrollo profesional a través de la implementación de una carrera profesional ya ha sido contemplado por organismos internacionales como la Federación Farmacéutica Internacional y la Organización Mundial de la Salud que propugnan la creación y el apoyo a los grupos de profesionales competentes, apostando por la fuerza del trabajo en equipo para la consecución de los objetivos. La Agrupación Farmacéutica de la Unión Europea, en el documento elaborado sobre el futuro de la farmacia en un plan a 2030, manifiesta la necesidad de garantizar que la remuneración de los farmacéuticos/as comunitarios/as refleje correctamente su contribución a la mejora de la atención farmacéutica, reduciendo la carga sobre otros servicios de salud y garantizando la sostenibilidad de los sistemas sanitarios.

Abrimos este nuevo año con nuevos bríos y renovado entusiasmo por seguir impulsando la investigación científica en bioética a través de nuestra revista. A partir de este año intentaremos elevar el número de artículos publicados por año en el ánimo de continuar y asegurar nuestras indizaciones actuales y las que están por venir en este 2025. Es por lo que, en este número hemos publicado siete artículos y una reseña y tenemos temas variados que van desde reflexiones rigurosas que retoman aspectos éticos y bioéticos de la pandemia por COVID-19 que, a cuatro años de distancia, se han analizado de forma más objetiva, hasta propuestas novedosas en temas actuales de investigación y trasplante de órganos.

Background and objective: The objective is to develop a model that predicts vital status six months after fracture as accurately as possible. For this purpose we will use five different data sources obtained through the National Hip Fracture Registry, the Health Management Unit and the Economic Management Department. Material and methods: The study population is a cohort of patients over 74 years of age who suffered a hip fracture between May 2020 and December 2022. A warehouse is created from five different data sources with the necessary variables. An analysis of missing values and outliers as well as unbalanced classes of the target variable (“vital status”) is performed. Fourteen different algorithmic models are trained with the training. The model with the best performance is selected and a fine tuning is performed. Finally, the performance of the selected model is analysed with test data. Results: A data warehouse is created with 502 patients and 144 variables. The best performing model is Linear Regression. Sixteen of the 24 cases of deceased patients are classified as live, and 14 live patients are classified as deceased. A sensitivity of 31%, an accuracy of 34% and an area under the curve of 0.65 is achieved. Conclusions: We have not been able to generate a model for the prediction of six-month survival in the current cohort. However, we believe that the method used for the generation of algorithms based on machine learning can serve as a reference for future works. Resumen: Objetivo: Desarrollar un modelo que prediga con la mayor exactitud posible el estado vital a los 6 meses tras fractura de cadera, utilizando para ello cinco fuentes de datos obtenidas a través del Registro Nacional de Fracturas de Cadera, la Unidad de Gestión Sanitaria y la Dirección Económica. Material y metodología: La población de estudio es una cohorte de pacientes que sufrieron fractura de cadera entre mayo de 2020 y diciembre de 2022. A partir de cinco fuentes diferentes de datos se crea un almacén con las variables necesarias. Se realiza un análisis de valores perdidos y atípicos, así como de desbalanceo de las clases de la variable objetivo («estado vital»). Se entrenan 14 diferentes modelos algorítmicos con los datos de entrenamiento. Se selecciona el modelo que mejor rendimiento obtenga y se realiza una puesta a punto fina. Finalmente se analiza el rendimiento del modelo con datos de test. Resultados: Se crea un almacén de datos con 502 pacientes y 144 variables. El modelo con mejor rendimiento es la regresión lineal. Dieciséis de los 24 casos de pacientes fallecidos son clasificados como vivos, y 14 pacientes vivos son clasificados como fallecidos. Se consigue una sensibilidad del 31%, una precisión del 34% y un área bajo la curva de 0,65. Conclusiones: No se ha conseguido generar un modelo de predicción de muerte a los 6 meses con nuestra cohorte. Sin embargo, creemos que el método utilizado para generar algoritmos basados en aprendizaje automático puede servir de referencia para futuros trabajos.