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Alzheimer’s disease (AD) is a neurodegenerative pathology, which is characterized by progressive and irreversible cognitive impairment. Most of the neuronal perturbations described in AD can be associated with soluble amyloid– β oligomers (SO-Aβ). There is a large amount of evidence demonstrating the neuroprotective effect of Nicotine neurotransmission in AD, mainly through nicotinic acetylcholine receptor (nAChR) activation and antiapoptotic PI3K/Akt/Bcl–2 pathway signaling. Using HPLC and GC/MS, we isolated and characterized two alkaloids obtained from C. scoparius, Lupanine (Lup), and 17– oxo-sparteine (17– ox), and examined their neuroprotective properties in a cellular model of SO-Aβ toxicity. Our results showed that Lup and 17– ox (both at 0.03μM) prevented SO-Aβ-induced toxicity in PC12 cells (Lup: 64±7%; 17– ox: 57±6%). Similar results were seen in hippocampal neurons where these alkaloids prevented SO-Aβ neurotoxicity (Lup: 57±2%; 17– ox: 52±3%) and increased the frequency of spontaneous calcium transients (Lup: 60±4%; 17– Ox: 40±3%), suggesting an enhancing effect on neural network activity and synaptic activity potentiation. All of the neuroprotective effects elicited by both alkaloids were completely blocked by α-bungarotoxin. Additionally, we observed that the presence of both Lup and 17– ox increased Akt phosphorylation levels (52±4% and 35±7%, respectively) in cells treated with SO-Aβ (3 h). Taken together, our results suggest that the activation of nAChR by Lup and 17– ox induces neuroprotection in different cellular models, and appears to be an interesting target for the development of new pharmacological tools and strategies against AD.

In order to reduce greenhouse gas emissions, countries worldwide are transforming their energy systems with higher shares of renewable energy and smart technologies for demand response. Microgrids play an essential role in the transformation of electric grids to smart grids. However, renewable sources present new challenges, particularly those of high variability, which creates uncertainties in the supply side that can affect the security of electricity access at affordable prices. This paper proposes a novel Stackelberg stochastic model to account for different sources of uncertainty. The Stackelberg model considers microgrids as leaders (upper-level problem) with uncertainty regarding the availability of wind and solar sources and electricity prices. Availability of renewable sources is modeled via chance constraints, which allows assessing the risk of microgrids over-committing supply levels. Uncertainty in electricity prices is modeled via a set of demand scenarios with a given probability distribution. The lower-level problem of the Stackelberg problem considers an electricity dispatch problem for each demand scenario. The proposed model allows measuring the strategic actions of microgrids when facing different types of uncertainties and how the smart grid should adapt to guarantee that demand levels are supplied. The results show the effectiveness of the proposed method. We find that microgrids risk levels above 30% do not correlate with further benefits, such as reduced electricity prices. We also identified that in average, depending on the social cost of carbon and demand level, microgrids can cover their own demand and supply 15% of the electricity demand in the grid.

Evidencia empírica muestra que la renovación urbana en altura además de re-estratificar socioeconómicamente los barrios, establece una oferta de movilidad y configuración territorial que puede ser ventajosa para un determinado sujeto y no para otro en términos de restricciones económicas, culturales e incluso físicas de movilidad y transporte. Estas condiciones son conceptualizadas en el “capital de movilidad”, entendido como la capacidad de los sujetos de ser móviles en el territorio a partir de capacidades de acceso, competencia y apropiación. El objetivo es comprender de qué forma las condiciones del capital de movilidad determinan un aprovechamiento socialmente diferenciado del territorio expresado en la movilidad cotidiana de distintos habitantes del pericentro de Santiago. Se realiza un análisis cuantitativo de la Encuesta Panel de Barrios 2 (2016) proporcionada por el Proyecto FONDECYT Regular 1151287, en el que participan los autores, analizando comparativamente tres unidades de estudio en el pericentro de Santiago de Chile. Los resultados desmitifican algunos de los supuestos de eficiencia asociados a la verticalización en donde las decisiones de movilidad de nuevos residentes de departamentos se moldean en dimensiones de libertad y mayores opciones, no así para antiguos residentes de casa, que presentan restricciones físicas y limitados recursos para ejercer la movilidad; es decir, se encuentran “confinados” espacialmente.

The high sensitivity Compton and Rayleigh X-ray scattering signals can be used to gain valuable information on the chemical composition of various matrices, by exploiting the ratio of those signals as a function of the effective atomic number (Zeff). Neither total reflection X-ray fluorescence (TXRF) nor the effect of the experimental setup, including sample preparation, X-ray excitation source selection, and band deconvolution procedure, has been assessed in this kind of approach. Here, a Compton/Rayleigh ratio and Zeff-based TXRF method was set up and tested as an analytical tool for milk samples differentiation. The method was developed using a 90° scattering angle and assessed using different X-ray excitation sources: a molybdenum tube (Mo Kα 17.5 KeV) and a tungsten tube (W Lα 8.5 KeV and W-Brems 35 KeV). The evaluation of independent Compton and Rayleigh signals was performed by non-Gaussian and Gaussian curve resolution methods, and both height and area-based calculations were evaluated. Different sample preparation conditions were assessed. By using 11 standard materials, a calibration curve for Compton/Rayleigh ratio versus Zeff was established. The method was tested to determine the Zeff of milk samples, which enabled its use as a parameter to differentiate them. Good precisions were obtained with the Mo excitation source and the area-based calculations, which allowed to differentiate undiluted milk samples by species, treatment, and fat content according to their Compton/Rayleigh ratio. This simple and rapid method has the potential to be used for the differentiation of various types of samples, including liquids, solids, and aerosols.

The financial market has been transformed by FinTech platforms, which offer innovative products and services. FinTech platforms use a combination of technologies to digitalize processes and introduce new business models with unique characteristics. The benefits of FinTech initiatives are not limited to boosting the economic development of emerging economies; they also have a transformative impact on society and stakeholders. The transformative impact enables the democratization of digital services, making them accessible to marginalized groups that the traditional financial banking system has excluded. Our study theorizes how FinTech organizational capabilities (digital technologies, digital infrastructures, and talented employees) and FinTech ecosystem conditions (competitors, regulations, digital gender gaps, and institutional gaps) impact inclusion, transparency, and market expansion inspired by transformative entrepreneurship. We tested it through a multilevel analysis using data from 333 worldwide large FinTech platforms combined with secondary data at the country level. Results provide insights into how digital infrastructure, a skilled workforce, and closing the gaps for disadvantaged groups are crucial determinant factors for increasing FinTech impacts in emerging economies. Several implications for entrepreneurs, society, and policymakers emerge as a call for fostering digital societies and FinTech industry in emerging economies.

Structural Health Monitoring (SHM) aims at detecting, localizing, and characterizing damages in civil, mechanical, and aerospatial structures, which are hardly detectable in visual inspections. The collection, analysis, and visualization of data captured by sensors installed on these structures can be strongly supported by modern techniques of Data Science. In particular, the visualization of these data provides valuable help to experts on structural health and decision makers on preventive and corrective maintenance. Unfortunately, existing systems of data visualization still demand those stakeholders for a high level of software programming skills to take full advantage of visual and interactive exploration of data that sensors capture and output. This work introduces a model-driven approach to develop data visualization in the domain of structural health monitoring, in particular, of bridges. This approach is based on the definition of a Domain Specific Language (DSL) that describes the main concepts of an infrastructure of sensors typically used in SHM, along with common graphics and visual alternatives of data visualization. This DSL is instantiated by a modeling language, composed of a metamodel, a visual representation of concepts, and a set of model-to-text transformation rules. In this way, non-programmers can implement their own data visualization from a graphical and intuitive design, by automatically generating the corresponding code. This approach was implemented in a modeling and code-generation tool, called Vis4bridge, whose usability and output were successfully evaluated through the development of tasks and case studies.

Polyphenols are bioactive substances that participate in the prevention of chronic illnesses. High content has been described in Berberis microphylla G. Forst (calafate), a wild berry extensively distributed in Chilean–Argentine Patagonia. We evaluated its beneficial effect through the study of mouse plasma metabolome changes after chronic consumption of this fruit. Characterized calafate extract was administered in water, for four months, to a group of mice fed with a high-fat diet and compared with a control diet. Metabolome changes were studied using UHPLC-DAD-QTOF-based untargeted metabolomics. The study was complemented by the analysis of protein biomarkers determined using Luminex technology, and quantification of OH radicals by electron paramagnetic resonance spectroscopy. Thirteen features were identified with a maximum annotation level-A, revealing an increase in succinic acid, activation of tricarboxylic acid and reduction of carnitine accumulation. Changes in plasma biomarkers were related to inflammation and cardiovascular disease, with changes in thrombomodulin (−24%), adiponectin (+68%), sE-selectin (−34%), sICAM-1 (−24%) and proMMP-9 (−31%) levels. The production of OH radicals in plasma was reduced after calafate intake (−17%), especially for the group fed with a high-fat diet. These changes could be associated with protection against atherosclerosis due to calafate consumption, which is discussed from a holistic and integrative point of view.

Implementing nationally determined contributions is more challenging for developing countries given potential economic consequences. Chile, a developing economy, is committed to reaching carbon neutrality by 2050. To do so, Chile announced multiple mitigation strategies such as phasing out coal by 2040, peaking emissions by 2025, and developing renewable energies. Fortunately, Chile holds a prominent renewable potential, standing out for solar, but it also has a significant challenge decarbonizing an economy that heavily relies on fossil fuels. The contribution of this paper is twofold. First, the first country-level disaggregated version of GCAM Latin America (GCAM-LA) was developed, where all South American countries are modeled as an independent energy-economy region. This model includes Chile as a separated region and incorporates interactions among the energy, water, agriculture and land use, economy, and climate systems. Second, different decarbonization strategies to reach carbon neutrality by 2050 were obtained, considering technology availability and high renewable energy potential. Results indicate that carbon neutrality is feasible when enforcing different combinations of the current Chilean mitigation strategies, even delaying coal phase-out by five years and failure of developing advanced solar technologies. If some policies are not fully implemented, such as delaying coal phase-out by five years or failure of developing advanced solar technologies, carbon neutrality can be achieved by incurring in a higher capital cost in the power sector. Moreover, decarbonization is mainly driven by high electrification levels in the final demand sector, reaching 53.7%–62.9% of the total consumption. However, such levels of electrification are reduced, particularly in the transport sector, when Chile relies on negative emissions from the land use and forestry sector.

Objetivo: En el contexto de una discusión, aún abierta, en torno al concepto de ciudad creativa, el presente artículo contrasta discursivas globales con apreciaciones locales de personas consideradas creativas, identificando características urbanas que atraen y generan arraigo, y otras que producen desafección e incomodidad. Metodología: El caso de estudio fue el Área Metropolitana de Concepción (AMC), Chile, realizando entrevistas a personas consideradas creativas; los entrevistados fueron seleccionados mediante técnica de bola de nieve, y el universo muestral se acotó bajo la lógica de saturación. Finalmente, la información obtenida se sistematizó con el software de Análisis de Contenido, T-Lab.8.1. Conclusiones: Los resultados se contrastaron con un estado del arte y discursivas académicas globales, evidenciando consensos, disensos y jerarquías, develando el necesario y local ajuste de una planificación y diseño urbano, en pos de una política de competitividad urbana basada en la fijación y atracción de talento creativo, máxime, de sistemas urbanos intermedios se trata. Originalidad: Estudiar la capacidad de atracción-retención que ejercen las urbes intermedias para con los grupos creativos, con una óptica centrada en las personas y sus experiencias, es poco habitual, pese a que resulta fundamental en un escenario de competencia global por atraer el talento y capital humano avanzado.

This study explores the emergence of a new entrepreneurship phenomenon (digital social entrepreneurship) as a result of the collaboration among many agents (N-Helix), given the government’s limited capacity to respond to the stakeholders’ needs satisfaction related to an exogenous event (e.g., the COVID-19 pandemic). Our theory development is based on three ongoing academic debates related to (a) the unrepresentativeness of the stakeholder theory in entrepreneurship research; (b) the emergence of digital social entrepreneurship (DSE) as a bridge between stakeholders’ needs, socio-economic actors, and digital-social initiatives; and (c) the role of N-Helix collaborations to facilitate the emergence of global knowledge-intensive initiatives and the rapid adoptions of open innovations. Our results support our assumptions about the positive mediation effect of DSE in the relationship between N-Helix collaborations and stakeholders’ satisfaction. Notably, results show how pandemic has intensified these relationships and how DSE in N-Helix collaborations can generate social impacts globally. Some implications for policy-makers have emerged from our results that should be considered during/post-COVID-19 pandemic.

Purpose Given the importance of growth-oriented entrepreneurship in the context of economic development and the need to understand how rural communities can be developed, the purpose of this research paper is to determine how the drivers of growth expectations differ between urban and rural settings. Design/methodology/approach The methodology is threefold: firstly, a descriptive analysis with non-parametric testing is conducted; then pooled regression model is used to analyse the predictors of growth expectations in both contexts, and finally, coarsened exact matching is used to identify possible self-selection bias. Findings In contrast to mainstream entrepreneurship theory, it is found that entrepreneurs’ intrinsic knowledge, skills and abilities are not significant in the rural-specific model. The only exception is entrepreneurs’ educational level, the importance of which is emphasised as a pivotal factor in increasing high-growth ventures in rural communities. Additionally, when self-selection is eliminated, rurality worsens growth intentions. Practical implications There is evidence that some growth-oriented entrepreneurs self-select into rural communities. Because the high-growth entrepreneurial dynamics in rural areas are unique, public policies should target purpose-driven entrepreneurial education. This includes encouraging “lifestyle entrepreneurship” (e.g. retirees returning to rural areas to become entrepreneurs), preventing entrepreneurial brain drain in rural areas and attracting highly educated urban entrepreneurs to exploit opportunities in rural areas. Originality/value This research attempts to contribute to the ongoing debate regarding the factors that drive high-growth entrepreneurs in rural areas by analysing rural entrepreneurs in the high-growth context of a developing economy. The focus is on Chile – a country that is rarely investigated compared to the USA or Europe – to extend the literature on high-growth ventures and entrepreneurial ecosystems.

Se argumenta que, en barrios periurbanos de producción Estatal, las propiedades espaciales del entorno construido y la forma física de lo edificado, no son suficientes para comprender la complejidad de la concentración de delitos y la formación de barrios seguros. Cuando se trata de barrios de producción estatal localizados en áreas desprovistas de servicios y equipamientos, la escala de análisis del espacio y la formación de centralidad cobra vital importancia. En términos de seguridad, la investigación sobre el trazado de la trama urbana y espacios públicos en áreas residenciales son esporádicos e inconcluyentes (Hillier & Sahbaz, 2008). En este mismo ámbito se ha priorizado la idea de apropiación defensiva del espacio, por sobre la necesidad de construir barrios seguros (Greene y Mora, 2018), dando menor atención al rol del espacio público y privado, en términos de seguridad y ocurrencia de delitos. Ante esta necesidad, esta investigación tiene como objetivo identificar y comprender las propiedades espaciales del entorno construido que intervienen en la formación de barrios seguros, problematizando la ocurrencia de delitos en el espacio público y privado, de esta forma dar luces sobre el potencial del entorno construido para favorecer -o no- la formación de barrios seguros. La investigación aborda dos barrios localizados en el periurbano de la comuna de San Pedro de la Paz, región del Bío-Bío, Chile: Boca Sur Nuevo y San Pedro de la Costa, sectores contiguos pero originados por el Estado en períodos históricos diferentes. El estudio utiliza una metodología cuantitativa, por un lado, para el análisis espacial, se implementa el enfoque de Sintaxis Espacial desarrollado por Hillier y Hanson (1984). La sintaxis espacial se basa en la teoría de grafos de la matemática discreta para el cálculo de las relaciones espaciales configurativas entre las calles de la ciudad (Yamu, 2021). Este enfoque se complementa con la metodología de Solá Morales (1997) a partir de los preceptos de urbanización, parcelación y edificación. Así, el espacio se observa como un aspecto intrínseco de las actividades que realizan las personas, refiriéndose no solo a las cualidades de los espacios individuales, sino también a las interrelaciones entre los espacios que componen la disposición espacial y el modo en que las personas utilizan y se mueven por las ciudades. Por otro lado, se realiza un análisis socioespacial cuantitativo de la concentración de delitos en el espacio, los datos son extraídos del Sistema Estadístico Delictual (SIED) en su versión de datos territoriales para el año 2019. En el análisis se consideraron delitos de mayor connotación social (DMCS), referidos a aquellos “delitos de carácter violento y que afectan la propiedad, la vida y bienes de las personas, generando con ello un impacto público” (AMUCH, 2018). Los datos delictuales fueron complementados con variables espaciales del caso de estudio. Los resultados muestran que el enfoque de Sintaxis Espacial. junto al enfoque morfológico de Solá Morales, permiten identificar las propiedades espaciales que son decisivas a la hora de mejorar entornos urbanos a nivel barrial y a nivel comunal en términos de barrios seguros, profundizando los modos de urbanización de los Estados Neoliberales que influyen en la percepción de la seguridad.

Purpose Innovative initiatives focusing on social and environmental impact often need help to secure traditional financial resources for their launch. Equity crowdfunding platforms (ECF) provide a potential funding source for these initiatives, particularly for technological inventors. This research paper aims to theorize how ECF campaigns attract investors to invest in technological initiatives with social and environmental value proposition impacts. Design/methodology/approach Using an inductive qualitative approach, the authors have gained insights, from 35 sustainable technological projects sponsored by a Chilean equity-crowdfunding platform, regarding the business model's transformation to achieve sustainable social and environmental impacts. Findings Findings show that disruptive technologies and sustainable aims are pivotal factors in successfully attracting investors to support sustainable technological initiatives through ECF platforms or campaigns. These factors led investors to actively engage with these projects and contribute to the value-creation process by transforming business models with social and environmental impacts and utilizing sustainable technology to enhance efficiency and optimize available resources. Research limitations/implications Due to the nature of this research, researchers must test the proposed conceptual framework using longitudinal quantitative data from multiple ECF platforms, technological solutions and investors worldwide in future research to enhance the comprehension of this phenomenon. Practical implications The findings highlight the significant contribution of ECF platforms and technological portfolios toward creating sustainable impacts. It is a good signal for investors interested in investing in technological initiatives and addressing social and environmental challenges. Social implications The contribution of disruptive technological projects from ECF platforms and ECF investors to tackle social and environmental challenges. Originality/value This research theorizes how ECF platforms tackle social challenges by encouraging investors to invest and participate with entrepreneurs in the co-creation process of sustainable technological solutions.

Maternal thyroid alterations have been widely associated with the risk of gestational diabetes mellitus (GDM). This study aims to 1) test the first and the second trimester full maternal thyroid profile on the prediction of GDM, both alone and combined with non-thyroid data; and 2) make that prediction independent of the diagnostic criteria, by evaluating the effectiveness of the different maternal variables on the prediction of oral glucose tolerance test (OGTT) post load glycemia. Pregnant women were recruited in Concepción, Chile. GDM diagnosis was performed at 24–28 weeks of pregnancy by an OGTT (n = 54 for normal glucose tolerance, n = 12 for GDM). 75 maternal thyroid and non-thyroid parameters were recorded in the first and the second trimester of pregnancy. Various combinations of variables were assessed for GDM and post load glycemia prediction through different classification and regression machine learning techniques. The best predictive models were simplified by variable selection. Every model was subjected to leave-one-out cross-validation. Our results indicate that thyroid markers are useful for the prediction of GDM and post load glycemia, especially at the second trimester of pregnancy. Thus, they could be used as an alternative screening tool for GDM, independently of the diagnostic criteria used. The final classification models predict GDM with cross-validation areas under the receiver operating characteristic curve of 0.867 (p<0.001) and 0.920 (p<0.001) in the first and the second trimester of pregnancy, respectively. The final regression models predict post load glycemia with cross-validation Spearman r correlation coefficients of 0.259 (p = 0.036) and 0.457 (p<0.001) in the first and the second trimester of pregnancy, respectively. This investigation constitutes the first attempt to test the performance of the whole maternal thyroid profile on GDM and OGTT post load glycemia prediction. Future external validation studies are needed to confirm these findings in larger cohorts and different populations.

Berberis microphylla G Forst (Calafate) have been used in traditional medicine from prehispanic times in Patagonia. In the last decade the consumption of the fruit has been increased due to their antioxidant capacity, and because several studies demonstrated health benefits associated with the protection against atherosclerosis and other metabolic diseases. Nevertheless, the bioactivity properties of the leaves, a by-product of agronomic management, have been poorly studied. Recently, 108 compounds mainly hydroxycinnamic acids, flavonols, and berberine were identified in a methanolic extract of the leaves, demonstrating great potential for the development of new functional beverages. Based on these, for first time a comprehensive chemical characterization and bioactivity was evaluated for a Calafate leaves infusion prepared in hot water. For this, chemical characterization of the infusion was performed by UHPLC-Q-TOF and TXRF. Bioactivity was assayed by antioxidant capacity, cell cytotoxicity, and cell oxidative stress assays. Inhibition of both Aβ aggregation for Alzheimer's disease and gastrointestinal enzymes for metabolic syndromes were evaluated. The results show that the infusion is rich in hydroxycinnamic acids and other bioactive compounds. The infusion does not contain toxic metals or cytotoxicity activity. The infusion can reduce intracellular reactive oxygen species in HUVEC cells and showed a reduction in the Aβ aggregation being a potential beverage for Alzheimer's prevention. Finally, the infusion had in-vitro hypoglycemic and hypolipidemic effects. These results support the usage of Berberis microphylla G Forst leaves as a new functional beverage.

Gestational Diabetes Mellitus (GDM) is characterized by abnormal maternal D-glucose metabolism and altered insulin signaling. Dysregulation of thyroid hormones (TH) tri-iodethyronine (T3) and L-thyroxine (T4) Hormones had been associated with GDM, but the physiopathological meaning of these alterations is still unclear. Maternal TH cross the placenta through TH Transporters and their Deiodinases metabolize them to regulate fetal TH levels. Currently, the metabolism of TH in placentas with GDM is unknown, and there are no other studies that evaluate the fetal TH from pregnancies with GDM. Therefore, we evaluated the levels of maternal TH during pregnancy, and fetal TH at delivery, and the expression and activity of placental deiodinases from GDM pregnancies. Pregnant women were followed through pregnancy until delivery. We collected blood samples during 10–14, 24–28, and 36–40 weeks of gestation for measure Thyroid-stimulating hormone (TSH), Free T4 (FT4), Total T4 (TT4), and Total T3 (TT3) concentrations from Normal Glucose Tolerance (NGT) and GDM mothers. Moreover, we measure fetal TSH, FT4, TT4, and TT3 in total blood cord at the delivery. Also, we measured the placental expression of Deiodinases by RT-PCR, western-blotting, and immunohistochemistry. The activity of Deiodinases was estimated quantified rT3 and T3 using T4 as a substrate. Mothers with GDM showed higher levels of TT3 during all pregnancy, and an increased in TSH during second and third trimester, while lower concentrations of neonatal TT4, FT4, and TT3; and an increased TSH level in umbilical cord blood from GDM. Placentae from GDM mothers have a higher expression and activity of Deiodinase 3, but lower Deiodinase 2, than NGT mothers. In conclusion, GDM favors high levels of TT3 during all gestation in the mother, low levels in TT4, FT4 and TT3 at the delivery in neonates, and increases deiodinase 3, but reduce deiodinase 2 expression and activity in the placenta.

Amyloid beta peptide (Aβ) is tightly associated with the physiopathology of Alzheimer’s Disease (AD) as one of the most important factors in the evolution of the pathology. In this context, we previously reported that Aβ increases the expression of ionotropic purinergic receptor 2 (P2×2R). However, its role on the cellular and molecular Aβ toxicity is unknown, especially in human brain of AD patients. Using cellular and molecular approaches in hippocampal neurons, PC12 cells, and human brain samples of patients with AD, we evaluated the participation of P2×2R in the physiopathology of AD. Here, we reported that Aβ oligomers (Aβo) increased P2×2 levels in mice hippocampal neurons, and that this receptor increases at late Braak stages of AD patients. Aβo also increases the colocalization of APP with Rab5, an early endosomes marker, and decreased the nuclear/cytoplasmic ratio of Fe65 and PGC-1α immunoreactivity. The overexpression in PC12 cells of P2×2a, but not P2×2b, replicated these changes in Fe65 and PGC-1α; however, both overexpressed isoforms increased levels of Aβ. Taken together, these data suggest that P2×2 is upregulated in AD and it could be a key potentiator of the physiopathology of Aβ. Our results point to a possible participation in a toxic cycle that increases Aβ production, Ca2+ overload, and a decrease of PGC-1α. These novel findings put the P2×2R as a key novel pharmacological target to develop new therapeutic strategies to treat Alzheimer’s Disease.

Introduction: Machine learning (ML) corresponds to a wide variety of methods that use mathematics, statistics and computational science to learn from multiple variables simultaneously. By means of pattern recognition, ML methods are able to find hidden correlations and accomplish accurate predictions regarding different conditions. ML has been successfully used to solve varied problems in different areas of science, such as psychology, economics, biology and chemistry. Therefore, we wondered how far it has penetrated into the field of obstetrics and gynecology. Aim: To describe the state of art regarding the use of ML in the context of pregnancy diseases and complications. Methodology: Publications were searched in PubMed, Web of Science and Google Scholar. Seven subjects of interest were considered: gestational diabetes mellitus, preeclampsia, perinatal death, spontaneous abortion, preterm birth, cesarean section, and fetal malformations. Current state: ML has been widely applied in all the included subjects. Its uses are varied, the most common being the prediction of perinatal disorders. Other ML applications include (but are not restricted to) biomarker discovery, risk estimation, correlation assessment, pharmacological treatment prediction, drug screening. data acquisition and data extraction. Most of the reviewed articles were published in the last five years. The most employed ML methods in the field are non-linear. Except for logistic regression, linear methods are rarely used. Future challenges: To improve data recording, storage and update in medical and research settings from different realities. To develop more accurate and understandable ML models using data from cutting-edge instruments. To carry out validation and impact analysis studies of currently existing high-accuracy ML models. Conclusion: The use of ML in pregnancy diseases and complications is quite recent, and has increased over the last few years. The applications are varied and point not only to the diagnosis, but also to the management, treatment, and pathophysiological understanding of perinatal alterations. Facing the challenges that come with working with different types of data, the handling of increasingly large amounts of information, the development of emerging technologies, and the need of translational studies, it is expected that the use of ML continue growing in the field of obstetrics and gynecology.

Gestational Diabetes Mellitus (GDM) is a hyperglycemia state that impairs maternal and offspring health, short and long-term. It is usually diagnosed at 24–28 weeks of pregnancy (WP), but at that time the fetal phenotype is already altered. Machine learning (ML)-based models have emerged as an auspicious alternative to predict this pathology earlier, however, they must be validated in different populations before their implementation in routine clinical practice. This review aims to give an overview of the ML-based models that have been proposed to predict GDM before 24–28 WP, with special emphasis on their current validation state and predictive performance. Articles were searched in PubMed. Manuscripts written in English and published before January 1, 2022, were considered. 109 original research studies were selected, and categorized according to the type of variables that their models involved: medical, i.e. clinical and/or biochemical parameters; alternative, i.e. metabolites, peptides or proteins, micro-ribonucleic acid molecules, microbiota genera, or other variables that did not fit into the first category; or mixed, i.e. both medical and alternative data. Only 8.3 % of the reviewed models have had validation in independent studies, with low or moderate performance for GDM prediction. In contrast, several models that lack of independent validation have shown a very high predictive power. The evaluation of these promising models in future independent validation studies would allow to assess their performance on different populations, and continue their way towards clinical implementation. Once settled, ML-based models would help to predict GDM earlier, initiate its treatment timely and prevent its negative consequences on maternal and offspring health.

The Latin American region has attracted a great amount of interest among management and organizational scholars in recent years. The distinctive economic, social, and institutional features of the region represent a unique opportunity for theory building and testing in management and business research. This research answers the following overarching question: How the research about organizations in the Latin American context has evolved and how could it move forward? We perform an in-depth analysis consisting of a systematic review and bibliometric techniques (i.e., co-occurrence, co-citation, and co-authorship network analysis) of 1940 peer-reviewed articles published in the field during the 2004–2021 period. We examine the most influential publications, authors, journals, and research organizations. Building on our analysis and results, we describe current research hotspots and suggest avenues for future research. Our results contribute to a broad discussion relative to the relevance of context in the organizational research community, providing the first holistic analysis of it.