Regulation of Cortico-Thalamic JNK1/2 and ERK1/2 MAPKs and Apoptosis-Related Signaling Pathways in PDYN Gene-Deficient Mice Following Acute and Chronic Mild Stress

The crosstalk between the opioidergic system and mitogen-activated protein kinases (MAPKs) has a critical role in mediating stress-induced behaviors related to the pathophysiology of anxiety. The present study evaluated the basal status and stress-induced alterations of cortico-thalamic MAPKs and other cell fate-related signaling pathways potentially underlying the anxiogenic endophenotype of PDYN gene-deficient mice. Compared to littermates, PDYN knockout (KO) mice had lower cortical and or thalamic amounts of the phospho-activated MAPKs c-Jun N-terminal kinase (JNK1/2) and extracellular signal-regulated kinase (ERK1/2). Similarly, PDYN-KO animals displayed reduced cortico-thalamic densities of total and phosphorylated (at Ser191) species of the cell fate regulator Fas-associated protein with death domain (FADD) without alterations in the Fas receptor. Exposure to acute restraint and chronic mild stress stimuli induced the robust stimulation of JNK1/2 and ERK1/2 MAPKs, FADD, and Akt-mTOR pathways, without apparent increases in apoptotic rates. Interestingly, PDYN deficiency prevented stress-induced JNK1/2 and FADD but not ERK1/2 or Akt-mTOR hyperactivations. These findings suggest that cortico-thalamic MAPK- and FADD-dependent neuroplasticity might be altered in PDYN-KO mice. In addition, the results also indicate that the PDYN gene (and hence dynorphin release) may be required to stimulate JNK1/2 and FADD (but not ERK1/2 or Akt/mTOR) pathways under environmental stress conditions.This joint research was funded by Red Temática de Investigación Cooperativa en Salud–Red de Trastornos Adictivos (RETICS–RTA, Instituto de Salud Carlos III [ISCIII], MCIU/AEI/FEDER), Grupos RD06/0001/0004 (J.M.) and RD06/0001/0003 (J.A.G.-S.). J.M. also received financial support from Proyectos de Investigación en Salud—ISCIII (grant RD. PI18/00576), Red de Investigación en Atención Primaria de Adicciones (grant RD21/0009/0008), and Delegación del Gobierno para el Plan Nacional Sobre Drogas (PNSD, grant 2019I012) from the Spanish Ministry of Health (MSC). This study was also supported by MCIU/AEI/FEDER (grants RTI2018-094414-A-I00 to A.R.-M., PID2019-109323RA-I00 to T.F., and SAF2008-01311 to J.A.G.-S.), and MSC/FEDER (FIS 05/0429 to J.M). A.R.-M. (grant RYC-2016-19282) and T.F. (grant RYC-2017-22666) are ‘Ramón y Cajal’ Researchers

Sensor Sumergible Multivariable para la Supervisión y Optimización del Proceso de Flotación

Este artículo describe el desarrollo y validación de un sensor sumergible que proporciona en línea y en tiempo real mediciones asociadas a las propiedades de la dispersión de aire y suspensión de partículas en la zona de colección de máquinas de flotación industriales. La tecnología proporciona en forma simultánea y en tiempo real mediciones de la concentración de aire (gas holdup), la densidad, viscosidad y temperatura de la pulpa mineral suspendida bajo la espuma en una máquina de flotación. El sensor comprende una celda de exclusión de gas, un flujometro másico Coriolis y una unidad de procesamiento de datos. La celda de exclusión de gas es un tubo con ambos extremos abiertos y que tiene una contracción gradual en su área de sección transversal que se asemeja a un cono invertido truncado. El flujometro másico Coriolis consiste de un tubo único recto de titanio de una pulgada que está conectado mediante flanges a la celda de exclusión de gas. Cuando el dispositivo sensor se sumerge verticalmente en la pulpa mineral aireada, se induce un flujo descendente continuo de pulpa sin burbujas a través del dispositivo; en donde la magnitud del flujo inducido es proporcional a la concentración de aire en la región en donde el dispositivo sensor está sumergido. En este artículo se presenta su validación en distintas máquinas de flotación industriales. La tecnología fue desarrollada en el Laboratorio de Flotación de la Universidad de Santiago, protegida por la USACH mediante múltiples patentes nacionales e internacionales y licenciada a la Empresa de base Científico Tecnológica U-Sensing Spa para su transferencia tecnológica. Palabras Clave: Sensor; Multivariable; Optimización; Burbujas; Flotación

Gestión para la Realización del Proyecto Productivo de Fabricación y Comercialización de Bolsas Ecológicas Elaboradas por Madres Cabeza de Familia en edades de 18 a 28 años de Ciudadela Sucre en el municipio de Soacha.

El presente proyecto tiene como objetivo plantear la viabilidad de crear un proyecto productivo de bolsas ecologías en el barrio Ciudadela Sucre del municipio de Soacha; beneficiando a madres cabezas de hogar entre 18 a 28 años

Artificial intelligence for the artificial kidney: Pointers to the future of a personalized hemodialysis therapy

Current dialysis devices are not able to react when unexpected changes occur during dialysis treatment, or to learn about experience for therapy personalization. Furthermore, great efforts are dedicated to develop miniaturized artificial kidneys to achieve a continuous and personalized dialysis therapy, in order to improve patient’s quality of life. These innovative dialysis devices will require a real-time monitoring of equipment alarms, dialysis parameters and patient-related data to ensure patient safety and to allow instantaneous changes of the dialysis prescription for assessment of their adequacy. The analysis and evaluation of the resulting large-scale data sets enters the realm of Big Data and will require real-time predictive models. These may come from the fields of Machine Learning and Computational Intelligence, both included in Artificial Intelligence, a branch of engineering involved with the creation of devices that simulate intelligent behavior. The incorporation of Artificial Intelligence should provide a fully new approach to data analysis, enabling future advances in personalized dialysis therapies. With the purpose to learn about the present and potential future impact on medicine from experts in Artificial Intelligence and Machine Learning, a scientific meeting was organized in the Hospital of Bellvitge (Barcelona, Spain). As an outcome of that meeting, the aim of this review is to investigate Artificial Intelligence experiences on dialysis, with a focus on potential barriers, challenges and prospects for future applications of these technologies.Postprint (author's final draft

Artificial intelligence for the artificial kidney: pointers to the future of a personalized hemodialysis therapy

Background: Current dialysis devices are not able to react when unexpected changes occur during dialysis treatment or to learn about experience for therapy personalization. Furthermore, great efforts are dedicated to develop miniaturized artificial kidneys to achieve a continuous and personalized dialysis therapy, in order to improve the patient's quality of life. These innovative dialysis devices will require a real-time monitoring of equipment alarms, dialysis parameters, and patient-related data to ensure patient safety and to allow instantaneous changes of the dialysis prescription for the assessment of their adequacy. The analysis and evaluation of the resulting large-scale data sets enters the realm of "big data" and will require real-time predictive models. These may come from the fields of machine learning and computational intelligence, both included in artificial intelligence, a branch of engineering involved with the creation of devices that simulate intelligent behavior. The incorporation of artificial intelligence should provide a fully new approach to data analysis, enabling future advances in personalized dialysis therapies. With the purpose to learn about the present and potential future impact on medicine from experts in artificial intelligence and machine learning, a scientific meeting was organized in the Hospital Universitari Bellvitge (L'Hospitalet, Barcelona). As an outcome of that meeting, the aim of this review is to investigate artificial intel ligence experiences on dialysis, with a focus on potential barriers, challenges, and prospects for future applications of these technologies. Summary and Key Messages: Artificial intelligence research on dialysis is still in an early stage, and the main challenge relies on interpretability and/or comprehensibility of data models when applied to decision making. Artificial neural networks and medical decision support systems have been used to make predictions about anemia, total body water, or intradialysis hypotension and are promising approaches for the prescription and monitoring of hemodialysis therapy. Current dialysis machines are continuously improving due to innovative technological developments, but patient safety is still a key challenge. Real-time monitoring systems, coupled with automatic instantaneous biofeedback, will allow changing dialysis prescriptions continuously. The integration of vital sign monitoring with dialysis parameters will produce large data sets that will require the use of data analysis techniques, possibly from the area of machine learning, in order to make better decisions and increase the safety of patients

MarinEye - A tool for marine monitoring

This work presents an autonomous system for marine integrated physical-chemical and biological monitoring – the MarinEye system. It comprises a set of sensors providing diverse and relevant information for oceanic environment characterization and marine biology studies. It is constituted by a physicalchemical water properties sensor suite, a water filtration and sampling system for DNA collection, a plankton imaging system and biomass assessment acoustic system. The MarinEye system has onboard computational and logging capabilities allowing it either for autonomous operation or for integration in other marine observing systems (such as Observatories or robotic vehicles. It was designed in order to collect integrated multi-trophic monitoring data. The validation in operational environment on 3 marine observatories: RAIA, BerlengasWatch and Cascais on the coast of Portugal is also discussed.info:eu-repo/semantics/publishedVersio

Relationship between adherence to Dietary Approaches to Stop Hypertension (DASH) diet indices and incidence of depression during up to 8 years of follow-up

Objective Our aim was to evaluate the relationship between adherence to different Dietary Approaches to Stop Hypertension (DASH)-diet indices and the risk of depression. Design This is a prospective study. We assessed 14,051 participants of a dynamic (permanently on-going recruitment) prospective cohort [the Seguimiento Universidad de Navarra (SUN) Project], initially free of depression. At baseline, a validated food–frequency questionnaire was used to assess the adherence to four previously proposed DASH indices (Dixon, Mellen, Fung, and Günther). To define the outcome we applied two definitions of depression: a less conservative definition including only self-reported physician-diagnosed depression (410 incident cases), and a more conservative definition that required both clinical diagnosis of depression and use of antidepressants (113 incident cases). Cox regression and restricted cubic splines analyses were performed. Results After a median follow-up period of 8 years, the multiple-adjusted model showed an inverse association with the Fung DASH score (Hazard Ratio (HR):0·76; 95% Confidence Interval (CI):0·61-0·94) when we used the less conservative definition of depression, and also under the more conservative definition (HR:0·63; 95% CI:0·41-0·95). We observed a weak inverse association with the Mellen DASH score, but no statistically significant association was found for the other definitions. The restricted cubic splines analyses suggested that these associations were non-linear (U-shaped). Conclusions After a median follow-up period of 8 years, the multiple-adjusted model showed an inverse association with the Fung DASH score (Hazard Ratio (HR):0·76; 95% Confidence Interval (CI):0·61-0·94) when we used the less conservative definition of depression, and also under the more conservative definition (HR:0·63; 95% CI:0·41-0·95). We observed a weak inverse association with the Mellen DASH score, but no statistically significant association was found for the other definitions. The restricted cubic splines analyses suggested that these associations were non-linear (U-shaped).</p