Abstract: Quantitative analysis of ventricular cerebrospinal fluid (vCSF) proteins following acute brain injury (ABI) may help identify pathophysiological pathways and potential biomarkers that can predict unfavorable outcome. In this prospective proteomic analysis study, consecutive patients with severe ABI expected to require intraventricular catheterization for intracranial pressure (ICP) monitoring for at least 5 days and patients without ABI admitted for elective clipping of an unruptured cerebral aneurysm were included. vCSF samples were collected within the first 24 h after ABI and ventriculostomy insertion and then every 24 h for 5 days. Data-independent acquisition and sequential window acquisition of all theoretical spectra (SWATH) mass spectrometry were used to compare differences in protein expression in patients with ABI and patients without ABI and in patients with traumatic and nontraumatic ABI. We included 50 patients with ABI (SAH n = 23, TBI n = 15, intracranial hemorrhage n = 6, ischemic stroke n = 3, others n = 3) and 12 patients without ABI. There were significant differences in the expression of 255 proteins between patients with and without ABI (p < 0.01). There were intraday and interday differences in expression of seven proteins related to increased inflammation, apoptosis, oxidative stress, and cellular response to hypoxia and injury. Among these, glial fibrillary acidic protein expression was higher in patients with ABI with severe intracranial hypertension (ICH) (ICP ? 30 mm Hg) or death compared to those without (log 2 fold change: +2.4; p < 0.001), suggesting extensive primary astroglial injury or death. Dysregulated vCSF protein expression after ABI may be associated with an increased risk of severe ICH and death. Resumen: El análisis cuantitativo de las proteínas en el líquido cefalorraquídeo ventricular (vLCR) después de una lesión cerebral aguda (LCA) puede ayudar a identificar vías fisiopatológicas y posibles biomarcadores que pueden predecir un resultado desfavorable. En este estudio prospectivo de análisis proteómico, se incluyeron pacientes consecutivos con LCA grave que se esperaba que necesitaran cateterismo intraventricular cerebral para monitorizar la presión intracraneal (PIC) durante al menos 5 días y pacientes sin LCA ingresados para clipaje electivo de un aneurisma cerebral no roto. Las muestras de vCSF se recolectaron dentro de las primeras 24 h después de la inserción del ITB y la ventriculostomía y luego cada 24 h durante 5 días. Se utilizó la espectrometría de masas de adquisición independiente de datos y adquisición de ventana secuencial de todos los espectros teóricos (SWATH) para comparar las diferencias en la expresión de proteínas en pacientes con LCA y pacientes sin LCA y en pacientes con LCA traumática y no traumática. Se incluyeron 50 pacientes con LCA (HSA n = 23, TCE n = 15, hemorragia intracraneal n = 6, ictus isquémico n = 3, otros n = 3) y 12 pacientes sin LCA. Hubo diferencias significativas en la expresión de 255 proteínas entre pacientes con y sin LCA (p <0,01). Hubo diferencias intradía e interdía en la expresión de siete proteínas relacionadas con el aumento de la inflamación, la apoptosis, el estrés oxidativo y la respuesta celular a la hipoxia y las lesiones. Entre estos, la expresión de la proteína ácida fibrilar glial fue mayor en pacientes con LCA con hipertensión intracraneal (HIC) grave (PIC ? 30 mm Hg) o muerte en comparación con aquellos sin (cambio log 2 veces: + 2,4; p < 0,001), lo que sugiere una extensa Lesión astroglial primaria. La expresión desregulada de las proteínas en el vLCR después de una LCA puede estar asociada con un mayor riesgo de HIC grave y muerte.

Abstract: Acute brain injuries are associated with high mortality rates and poor long-term functional outcomes. Measurement of cerebrospinal fluid (CSF) biomarkers in patients with acute brain injuries may help elucidate some ofthe pathophysiological pathways involved in the prognosis of these patients. We performed a systematic search and descriptive review using the MEDLINE database and the PubMed interface from inception up to June 29, 2021. Of the 39 studies that met our criteria, 30 reported that the biomarker concentration was associated with neurological outcome and 9 reported no association. In traumatic brain injury patients, increased extracellular concentrations of biomarkers related to neuronal cytoskeletal disruption, apoptosis and inflammation were associated with the severity of acute brain injury, early mortality and worse long-term functional outcome. Reduced concentrations of protein biomarkers related to impaired redox function were associated with increased risk of neurological deficit. In non-traumatic acute brain injury, concentrations of CSF protein biomarkers related to dysregulated inflammation and apoptosis were associated with a greater risk of vasospasm and a larger volume of brain ischemia. Resumen: Las lesiones cerebrales agudas se asocian con altas tasas de mortalidad y malos resultados funcionales a largo plazo. La medición de biomarcadores en el líquido cefalorraquídeo (LCR) en pacientes con lesiones cerebrales agudas puede ayudar a dilucidar algunas de las vías fisiopatológicas implicadas en el pronóstico de estos pacientes. Realizamos una búsqueda sistemática y revisión descriptiva utilizando la base de datos MEDLINE y la interfaz PubMed desde su inicio hasta el 29 de junio de 2021.De los 39 estudios que cumplieron con nuestros criterios de inclusión, 30 informaron que la concentración del biomarcador se asoció con el resultado neurológico y 9 no informaron ninguna asociación. En lesiones cerebrales traumáticas, el aumento de las concentraciones extracelulares de biomarcadores relacionados con la alteración del citoesqueleto neuronal, la apoptosis y la inflamación se asociaron con la gravedad de la lesión cerebral aguda, la mortalidad temprana y un peor resultado funcional a largo plazo. Las concentraciones reducidas de biomarcadores proteicos relacionados con la función redox deteriorada se asociaron con un mayor riesgo de déficit neurológico. En la lesión cerebral aguda no traumática, las concentraciones de biomarcadores proteicos del LCR relacionados con la inflamación desregulada y la apoptosis se asociaron con un mayor riesgo de vasoespasmo y un mayor volumen de isquemia cerebral.

Abstract: Reseña literaria de la novela “Lectura Fácil” de la escritora española Cristina Morales, ganadora del premio Herralde de Novela. Estableciendo conexiones con la escritora chilena Diamela Eltit, el cineasta argentino Gaspar Noé y la banda española Mecano, esta reseña indaga en las exploraciones de la autora en cuanto a feminismo, diversidad funcional, identidad, género, sistema económico, entre otros.

Abstract: 3D printing technologies enable medicine customization adapted to patients' needs. There are several 3D printing techniques available, but majority of dosage forms and medical devices are printed using nozzle-based extrusion, laser-writing systems, and powder binder jetting. 3D printing has been demonstrated for a broad range of applications in development and targeting solid, semi-solid and locally applied or implanted medicines. 3D printed solid dosage forms allow the combination of one or more drugs within the same solid dosage form to improve patient compliance, facilitate deglutition, tailor the release profile, or fabricate new medicines for which no dosage form is available. Sustained release 3D-printed implants, stents and medical devices have been used mainly for joint replacement therapies, medical prostheses, and cardiovascular applications. Locally applied medicines such as wound dressing, microneedles, and medicated contact lenses have also been manufactured using 3D printing techniques. The challenge is to select the 3D printing tech-nique most suitable for each application and the type of pharmaceutical ink that should be devel-oped that possesses the required physicochemical and biological performance. The integration of biopharmaceuticals and nanotechnology-based drugs along with 3D printing (""Nanoprinting"") brings printed personalized nanomedicines within the most innovative perspectives for the coming years. Continuous manufacturing through the use of 3D-printed microfluidic chips facilitates their translation into clinical practice.

Abstract: The Métropole de Lyon, involved in a major Permeable City project, was selected as a pilot site for the Life Artisan project. The latter aims to demonstrate and value the potential of Nature-Based Solutions (NBS). As such, the Metropole de Lyon has adapted and set up stormwater trees to manage rainwater in the urban space formerly built. This involves removing the kerb of the tree pit, digging out around the tree pit, and installing an infiltration trench at the street gutter’s level (stormwater path). The space inside the tree pit is planted to promote the creation of simplified ecosystems. To test the concept, 10 stormwater trees have been retrofitted in consultation with citizens. The retrofitted trees and the untouched trees are equipped with sensors to measure their effectiveness as NBS. The first results show that although the trees pits were dimensioned to abate, by infiltration, 15 mm of rain for about 600 m2, a maximum of 39 mm of rain has been abated over the monitored period. The success of this test has motivated the local authority to deploy more stormwater trees and to accelerate the sustainable transition to a permeable and green city. To do so, the transition method from the European program SWITCH (Sustainable Water management Improves Tomorrow’s Cities Health) is used. It led to the writing of a technical guide on stormwater trees to raise awareness on the subject for the metropolitan actors, in the first place. In addition, the supply of rainwater to the trees doubles their growth over a season and increases the evapotranspiration, which could have an impact for the urban cool islands. } Abstract: La Métropole de Lyon, que participa en un importante proyecto de Ciudad Permeable, fue seleccionada como lugar piloto para el proyecto Life Artisan. Este último pretende demostrar y valorar el potencial de las Soluciones Basadas en la Naturaleza (SBN). Para ello, la Metropole de Lyon ha adaptado e instalado árboles de lluvia para gestionar la escorrentia en el espacio urbano anteriormente construido. Se trata de retirar el bordillo del alcorque, excavar alrededor del alcorque e instalar una zanja de infiltración al nivel de la cuneta de la calle (camino de aguas pluviales). El espacio interior del alcorque se planta para favorecer la creación de ecosistemas simplificados. Para probar el concepto, se han retroadaptado 10 árboles de lluvia en consulta con los ciudadanos. Los árboles retroadaptados y los intactos están equipados con sensores para medir su eficacia como NBS. Los primeros resultados muestran que, aunque los alcorques estaban dimensionados para reducir, por infiltración, 15 mm de lluvia en unos 600 m2, se ha reducido un máximo de 39 mm de lluvia durante el periodo supervisado. El éxito de esta prueba ha motivado a la autoridad local a desplegar más árboles pluviales y a acelerar la transición sostenible hacia una ciudad permeable y verde. Para ello se utiliza el método de transición del programa europeo SWITCH (Sustainable Water management Improves Tomorrow's Cities Health). Ha llevado a la redacción de una guía técnica sobre los árboles de lluvia para sensibilizar sobre el tema a los agentes metropolitanos, en primer lugar. Además, el suministro de agua de lluvia a los árboles duplica su crecimiento a lo largo de una temporada y aumenta la evapotranspiración, lo que podría repercutir en la creación de islas urbanas de frescura.

Abstract: The incidence of burn injuries is higher in low-and middle-income countries, and particularly in remote areas where the access to specialized burn assessment, care and recovery is limited. Given the high costs associated with one of the most used techniques to evaluate the severity of a burn, namely laser Doppler imaging (LDI), an alternative approach could be beneficial for remote locations. This study proposes a novel approach to estimate the LDI from digital images of a burn. The approach is a pixelwise regression model based on convolutional neural networks. To minimize the dependency on the conditions in which the images are taken, the effect of two image normalization techniques is also studied. Results indicate that the model performs satisfactorily on average, presenting low mean absolute and squared errors and high structural similarity index. While no significant differences are found when changing the normalization of the images, the performance is affected by their quality. This suggests that changes in the intensity of the images do not alter the relevant information about the wound, whereas changes in brightness, contrast and sharpness do. Resumen: La incidencia de lesiones por quemaduras es mayor en países de ingresos bajos y medianos, especialmente en áreas remotas donde el acceso a evaluación, cuidado y recuperación especializados para quemaduras es limitado. Dados los altos costos asociados con una de las técnicas más utilizadas para evaluar la gravedad de una quemadura, en particular las imágenes de Doppler láser (LDI), un enfoque alternativo podría ser beneficioso para ubicaciones remotas. Este estudio propone un enfoque novedoso para estimar el LDI a partir de imágenes digitales de una quemadura. El enfoque es un modelo de regresión píxel a píxel basado en redes neuronales convolucionales. Para minimizar la dependencia de las condiciones en las que se toman las imágenes, también se estudia el efecto de dos técnicas de normalización de imágenes. Los resultados indican que el modelo tiene un rendimiento satisfactorio en promedio, presentando errores absolutos y cuadrados medios bajos y un índice de similitud estructural alto. Si bien no se encuentran diferencias significativas al cambiar la normalización de las imágenes, el rendimiento se ve afectado por su calidad. Esto sugiere que los cambios en la intensidad de las imágenes no alteran la información relevante sobre la herida, mientras que los cambios en brillo, contraste y nitidez sí lo hacen.

Abstract: Thoracic bio-impedance (BioZ) measurements have been proposed as an alternative for respiratory monitoring. Given the ambulatory nature of this modality, it is more prone to noise sources. In this study, two pre-trained machine learning models were used to classify BioZ signals into clean and noisy classes. The models were trained on data from patients suffering from chronic obstructive pulmonary disease, and their performance was evaluated on data from patients undergoing bariatric surgery. Additionally, transfer learning (TL) was used to optimize the models for the new patient cohort. Lastly, the effect of different breathing patterns on the performance of the machine learning models was studied. Results showed that the models performed accurately when applying them to another patient population and their performance was improved by TL. However, different imposed respiratory frequencies were found to affect the performance of the models. Abstract: Las mediciones de bioimpedancia torácica (BioZ) se han propuesto como una alternativa para el monitoreo respiratorio. Dada la naturaleza ambulatoria de esta modalidad, es más propensa a fuentes de ruido. En este estudio, se utilizaron dos modelos de aprendizaje automático preentrenados para clasificar las señales de BioZ en clases limpias y ruidosas. Los modelos se entrenaron con datos de pacientes que padecen enfermedad pulmonar obstructiva crónica, y su rendimiento se evaluó en datos de pacientes sometidos a cirugía bariátrica. Además, se utilizó la transferencia de aprendizaje (TL) para optimizar los modelos para la nueva cohorte de pacientes. Por último, se estudió el efecto de diferentes patrones de respiración en el rendimiento de los modelos de aprendizaje automático. Los resultados mostraron que los modelos funcionaron con precisión al aplicarlos a otra población de pacientes y su rendimiento mejoró mediante TL. Sin embargo, se encontró que diferentes frecuencias respiratorias impuestas afectaban el rendimiento de los modelos.

Abstract: Antibiotic resistance is driven by selection, but the degree to which a bacterial strain’s evolutionary history shapes the mechanism and strength of resistance remains an open question. Here, we reconstruct the genetic and evolutionary mechanisms of carbapenem resistance in a clinical isolate of Klebsiella quasipneumoniae. A combination of short- and long-read sequencing, machine learning, and genetic and enzymatic analyses established that this carbapenem-resistant strain carries no carbapenemase-encoding genes. Genetic reconstruction of the resistance phenotype confirmed that two distinct genetic loci are necessary in order for the strain to acquire carbapenem resistance. Experimental evolution of the carbapenem-resistant strains in growth conditions without the antibiotic revealed that both loci confer a significant cost and are readily lost by de novo mutations resulting in the rapid evolution of a carbapenem-sensitive phenotype. To explain how carbapenem resistance evolves via multiple, low-fitness single-locus intermediates, we hypothesised that one of these loci had previously conferred adaptation to another antibiotic. Fitness assays in a range of drug concentrations show how selection in the antibiotic ceftazidime can select for one gene (blaDHA-1) potentiating the evolution of carbapenem resistance by a single mutation in a second gene (ompK36). These results show how a patient’s treatment history might shape the evolution of antibiotic resistance and could explain the genetic basis of carbapenem-resistance found in many enteric-pathogens.

Abstract: Abandoned mines are generally associated with the release of heavy metals into the environment. Through mine water, numerous streams and rivers in Scotland are currently severely polluted with potentially toxic metals. To determine the magnitude of the contamination by Pb, Zn, Cu, and Cd in the Glengonnar catchment, along with their spatial variation and potential risk to human health and surface water, sediment and water samples were collected at different locations along a section of the catchment. Heavy metals concentrations were determined by MP – AES and AAS, their spatial variation was observed through SEM – EDS, and the risk assessment was conducted through RISC5 software and Remedial Targets Methodology. Concentrations of metals were higher in sediments (up to 70,668.41 mg/kg Pb, 3,802.48 mg/kg Zn, and 1,078.60 mg/kg Cu) than in water (up to 2,385.16 ?g/L Pb, and 334.52 ?g/L Zn), as a result of the speciation and the adsorption-distribution coefficient of metals, together with the pH of the water. Metals, as free ions and in particulate form, were found to be present at Pb processing and Pb mining areas, respectively. Risk assessment revealed that Pb is the metal of greatest concern and concentrations are prone to harm human health. No detrimental effects to human health were related with concentrations of Zn and Cu in water or sediments. Concentrations of Pb, Zn and Cu in sediments represent a considerable risk to the aquatic environment of the surface water. Consequently, historical legacy of the mining operations at Leadhills means that risk to human health and surface water is present and is likely to continue; reason why adequate mitigation techniques are required in order to decrease pollution and dispersion of heavy metals.

Abstract: Changes in respiratory rate have been found to be one of the early signs of health deterioration in patients. In environments where diagnostic tools and medical attention are scarce, the monitoring of the respiratory signal becomes crucial to timely detect life-threatening conditions. This signal can be measured using wearable technology; however, the use of such technology is often hampered by the low quality of the recordings. Therefore, to apply these data in diagnosis, it is important to determine which parts of the signal are of sufficient quality. This study aims to evaluate the performance of a signal quality assessment framework, where two machine learning algorithms (support vector machine-SVM, and convolutional neural network-CNN) were used. The models were pretrained using data of patients suffering from chronic obstructive pulmonary disease. The generalization capability of the models was evaluated by testing them on data from a different patient population, presenting normal and pathological breathing. The new patients underwent bariatric surgery and performed a controlled breathing protocol, displaying six different breathing patterns. Data augmentation (DA) and transfer learning (TL) were used to increase the size of the training set and to optimize the models for the new dataset. The effect of the different breathing patterns on the performance of the classifiers was also studied. The SVM did not improve when using DA, however, when using TL, the performance improved significantly (p<0.05) compared to DA. The opposite effect was observed for CNN, where the biggest improvement was obtained using DA. The models presented a low performance for shallow, slow and fast breathing patterns. These results suggest that it is possible to classify respiratory signals obtained with wearable technologies using pretrained machine learning models. Resumen: Se ha encontrado que cambios en la frecuencia respiratoria son uno de los primeros signos de deterioro de la salud en pacientes. En entornos donde las herramientas de diagnóstico y la atención médica son escasas, el monitoreo de la señal respiratoria se vuelve crucial para detectar de manera oportuna condiciones que amenazan la vida. Esta señal se puede medir mediante tecnología portátil; sin embargo, el uso de dicha tecnología a menudo se ve obstaculizado por la baja calidad de las señales. Por lo tanto, para aplicar estos datos para diagnóstico, es importante determinar qué partes de la señal tienen una calidad suficientemente buena. Este estudio tiene como objetivo evaluar el rendimiento de un marco de evaluación de calidad de señal, donde se utilizaron dos algoritmos de aprendizaje automático (máquina de soporte vectorial-SVM y red neuronal convolucional-CNN). Los modelos fueron preentrenados utilizando datos de pacientes que padecen enfermedad pulmonar obstructiva crónica. La capacidad de generalización de los modelos se evaluó probándolos en datos de una población de pacientes diferente, que presentaban respiración normal y patológica. Los nuevos pacientes se sometieron a cirugía bariátrica y realizaron un protocolo de respiración controlada, mostrando seis patrones de respiración diferentes. Se utilizó aumento de datos (AD) y transferencia de aprendizaje (TL) para aumentar el tamaño del conjunto de entrenamiento y optimizar los modelos para el nuevo conjunto de datos. Se estudió también el efecto de los diferentes patrones de respiración en el rendimiento de los clasificadores. La SVM no mejoró al usar AD, sin embargo, al usar TL, el rendimiento mejoró significativamente (p<0.05) en comparación con AD. Se observó el efecto contrario en CNN, donde la mayor mejora se obtuvo con AD. Los modelos presentaron un bajo rendimiento para patrones de respiración superficiales, lentos y rápidos. Estos resultados sugieren que es posible clasificar las señales respiratorias obtenidas con tecnologías portátiles mediante modelos preentrenados de aprendizaje automático.