The Differential Organization of F-Actin Alters the Distribution of Organelles in Cultured When Compared to Native Chromaffin Cells

Cultured bovine chromaffin cells have been used extensively as a neuroendocrine model to study regulated secretion. In order to extend such experimental findings to the physiological situation, it is necessary to study mayor cellular structures affecting secretion in cultured cells with their counterparts present in the adrenomedullary tissue. F-actin concentrates in a peripheral ring in cultured cells, as witnessed by phalloidin?rodhamine labeling, while extends throughout the cytoplasm in native cells. This result is also confirmed when studying the localization of ?-fodrin, a F-actin-associated protein. Furthermore, as a consequence of this redistribution of F-actin, we observed that chromaffin granules and mitochondria located into two different cortical and internal populations in cultured cells, whereas they are homogeneously distributed throughout the cytoplasm in the adrenomedullary tissue. Nevertheless, secretion from isolated cells and adrenal gland pieces is remarkably similar when measured by amperometry. Finally, we generate mathematical models to consider how the distribution of organelles affects the secretory kinetics of intact and cultured cells. Our results imply that we have to consider F-actin structural changes to interpret functional data obtained in cultured neuroendocrine cells.This study was supported by grants from the Spanish Ministerio de Economía y Competitividad (BFU2011-25095 and BFU2015- 63684-P, MINECO, FEDER, UE) to LMG

Fetal Brain Tissue Annotation and Segmentation Challenge Results

In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context. However, manual segmentation of cerebral structures is time-consuming and prone to error and inter-observer variability. Therefore, we organized the Fetal Tissue Annotation (FeTA) Challenge in 2021 in order to encourage the development of automatic segmentation algorithms on an international level. The challenge utilized FeTA Dataset, an open dataset of fetal brain MRI reconstructions segmented into seven different tissues (external cerebrospinal fluid, grey matter, white matter, ventricles, cerebellum, brainstem, deep grey matter). 20 international teams participated in this challenge, submitting a total of 21 algorithms for evaluation. In this paper, we provide a detailed analysis of the results from both a technical and clinical perspective. All participants relied on deep learning methods, mainly U-Nets, with some variability present in the network architecture, optimization, and image pre- and post-processing. The majority of teams used existing medical imaging deep learning frameworks. The main differences between the submissions were the fine tuning done during training, and the specific pre- and post-processing steps performed. The challenge results showed that almost all submissions performed similarly. Four of the top five teams used ensemble learning methods. However, one team's algorithm performed significantly superior to the other submissions, and consisted of an asymmetrical U-Net network architecture. This paper provides a first of its kind benchmark for future automatic multi-tissue segmentation algorithms for the developing human brain in utero.Comment: Results from FeTA Challenge 2021, held at MICCAI; Manuscript submitte

Discourse Analysis and Terminology in Languages for Specific Purposes

Aquest importantíssim recull conté estudis i reflexions sobre temes rellevants en la recerca sobre LSP: anglès mèdic, el llenguatge de la publicitat i periodístic, telecomunicacions i terminologia informàtica, llenguatge comercial i jurídic... Malgrat que gran part dels treballs aplegats es refereixen a l'anglès, també hi ha que tracten l'alemany, francès i altres llengües. Conté textos en anglès, francés, portuguès i castellà

A Large-Scale Genetic Analysis Reveals a Strong Contribution of the HLA Class II Region to Giant Cell Arteritis Susceptibility

We conducted a large-scale genetic analysis on giant cell arteritis (GCA), a polygenic immune-mediated vasculitis. A case-control cohort, comprising 1,651 case subjects with GCA and 15,306 unrelated control subjects from six different countries of European ancestry, was genotyped by the Immunochip array. We also imputed HLA data with a previously validated imputation method to perform a more comprehensive analysis of this genomic region. The strongest association signals were observed in the HLA region, with rs477515 representing the highest peak (p = 4.05 × 10−40, OR = 1.73). A multivariate model including class II amino acids of HLA-DRβ1 and HLA-DQα1 and one class I amino acid of HLA-B explained most of the HLA association with GCA, consistent with previously reported associations of classical HLA alleles like HLA-DRB1∗04. An omnibus test on polymorphic amino acid positions highlighted DRβ1 13 (p = 4.08 × 10−43) and HLA-DQα1 47 (p = 4.02 × 10−46), 56, and 76 (both p = 1.84 × 10−45) as relevant positions for disease susceptibility. Outside the HLA region, the most significant loci included PTPN22 (rs2476601, p = 1.73 × 10−6, OR = 1.38), LRRC32 (rs10160518, p = 4.39 × 10−6, OR = 1.20), and REL (rs115674477, p = 1.10 × 10−5, OR = 1.63). Our study provides evidence of a strong contribution of HLA class I and II molecules to susceptibility to GCA. In the non-HLA region, we confirmed a key role for the functional PTPN22 rs2476601 variant and proposed other putative risk loci for GCA involved in Th1, Th17, and Treg cell function

Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

Funder: European Society of Intensive Care Medicine; doi: http://dx.doi.org/10.13039/501100013347Funder: Flemish Society for Critical Care NursesAbstract: Purpose: Intensive care unit (ICU) patients are particularly susceptible to developing pressure injuries. Epidemiologic data is however unavailable. We aimed to provide an international picture of the extent of pressure injuries and factors associated with ICU-acquired pressure injuries in adult ICU patients. Methods: International 1-day point-prevalence study; follow-up for outcome assessment until hospital discharge (maximum 12 weeks). Factors associated with ICU-acquired pressure injury and hospital mortality were assessed by generalised linear mixed-effects regression analysis. Results: Data from 13,254 patients in 1117 ICUs (90 countries) revealed 6747 pressure injuries; 3997 (59.2%) were ICU-acquired. Overall prevalence was 26.6% (95% confidence interval [CI] 25.9–27.3). ICU-acquired prevalence was 16.2% (95% CI 15.6–16.8). Sacrum (37%) and heels (19.5%) were most affected. Factors independently associated with ICU-acquired pressure injuries were older age, male sex, being underweight, emergency surgery, higher Simplified Acute Physiology Score II, Braden score 3 days, comorbidities (chronic obstructive pulmonary disease, immunodeficiency), organ support (renal replacement, mechanical ventilation on ICU admission), and being in a low or lower-middle income-economy. Gradually increasing associations with mortality were identified for increasing severity of pressure injury: stage I (odds ratio [OR] 1.5; 95% CI 1.2–1.8), stage II (OR 1.6; 95% CI 1.4–1.9), and stage III or worse (OR 2.8; 95% CI 2.3–3.3). Conclusion: Pressure injuries are common in adult ICU patients. ICU-acquired pressure injuries are associated with mainly intrinsic factors and mortality. Optimal care standards, increased awareness, appropriate resource allocation, and further research into optimal prevention are pivotal to tackle this important patient safety threat

Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

Funder: European Society of Intensive Care Medicine; doi: http://dx.doi.org/10.13039/501100013347Funder: Flemish Society for Critical Care NursesAbstract: Purpose: Intensive care unit (ICU) patients are particularly susceptible to developing pressure injuries. Epidemiologic data is however unavailable. We aimed to provide an international picture of the extent of pressure injuries and factors associated with ICU-acquired pressure injuries in adult ICU patients. Methods: International 1-day point-prevalence study; follow-up for outcome assessment until hospital discharge (maximum 12 weeks). Factors associated with ICU-acquired pressure injury and hospital mortality were assessed by generalised linear mixed-effects regression analysis. Results: Data from 13,254 patients in 1117 ICUs (90 countries) revealed 6747 pressure injuries; 3997 (59.2%) were ICU-acquired. Overall prevalence was 26.6% (95% confidence interval [CI] 25.9–27.3). ICU-acquired prevalence was 16.2% (95% CI 15.6–16.8). Sacrum (37%) and heels (19.5%) were most affected. Factors independently associated with ICU-acquired pressure injuries were older age, male sex, being underweight, emergency surgery, higher Simplified Acute Physiology Score II, Braden score 3 days, comorbidities (chronic obstructive pulmonary disease, immunodeficiency), organ support (renal replacement, mechanical ventilation on ICU admission), and being in a low or lower-middle income-economy. Gradually increasing associations with mortality were identified for increasing severity of pressure injury: stage I (odds ratio [OR] 1.5; 95% CI 1.2–1.8), stage II (OR 1.6; 95% CI 1.4–1.9), and stage III or worse (OR 2.8; 95% CI 2.3–3.3). Conclusion: Pressure injuries are common in adult ICU patients. ICU-acquired pressure injuries are associated with mainly intrinsic factors and mortality. Optimal care standards, increased awareness, appropriate resource allocation, and further research into optimal prevention are pivotal to tackle this important patient safety threat

Correction to: Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

The original version of this article unfortunately contained a mistake

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Identificación y caracterización automatizada de lesiones de tuberculosis en imágenes de tomografía axial computarizada

Tuberculosis is an infectious airborne disease which 10.0 million people developed and 1.5 million died of in 2018. The organ most vulnerable to the disease is the lung and the lesions that occur determine if it is in a latent or active state. The purpose of this project is to develop a Python model that detects latent tuberculosis lesions in computed tomography (CT) images. An easily accessible database has been created with 24 TACs of minipigs? lungs infected with Mycobacterium tuberculosis and euthanized 12 weeks after the initial infection. To analyze them, different pre-processing, segmentation and post-processing techniques have been used to change their representation and facilitate the identification of the different structures they present and the measurement of their characteristics. To locate the carina, a model has been implemented that applies a global threshold, a mask inversion and a filtering of regions by area and eccentricity. Lung volume has been found by creating a mask and applying an Otsu threshold. Lesions have been detected by determining the local maxima present in the lungs volume and by applying a region growing algorithm. Attributes of all identified structures have been extracted, which have subsequently been used to train a decision tree, a Machine Learning algorithm capable of classifying them into lesions or healthy areas. With the developed program, it has been possible to characterize the majority of tuberculosis lesions present in the computed tomography images. Furthermore, the results obtained show that the attributes that best discriminate lesions from healthy regions are the mean diameter, the intensity in the center and the distance in the vertical direction to the carina.La tuberculosis es una enfermedad infecciosa transmitida por el aire que en el año 2018 desarrollaron 10.0 millones de personas y por causa de la cual murieron 1.5 millones. El órgano más vulnerable a la enfermedad es el pulmón y las lesiones que se producen determinan si ésta está en un estado latente o en un estado activo. El propósito de este proyecto es desarrollar un modelo en Python que permita detectar lesiones de tuberculosis en estado latente en imágenes de tomografía axial computarizada (TAC). Se ha creado una base de datos fácilmente accesible con 24 TACs de pulmones de minipigs infectados con Mycobacterium tuberculosis y eutanasiados 12 semanas después de la infección inicial. Para analizarlos se han utilizado diferentes técnicas de pre-procesamiento, segmentación y post-procesamiento de imágenes para cambiar su representación y facilitar la identificación de las diferentes estructuras que presentan y la medida de sus características. Para localizar la carina, se ha implementado un modelo que aplica un umbral global, una inversión de la máscara y un filtrado de regiones por área y excentricidad. El volumen pulmonar se ha encontrado creando una máscara y aplicando un umbral Otsu. Las lesiones se han detectado determinando los máximos locales presentes en los pulmones y mediante un crecimiento de regiones. De todas las estructuras identificadas se han extraído sus atributos, que posteriormente se han utilizado para entrenar un árbol de decisión, algoritmo de Machine Learning capaz de clasificarlas en lesiones o zonas sanas. Con el programa desarrollado, se ha conseguido caracterizar la mayoría de lesiones de tuberculosis presentes en las imágenes de tomografía axial computarizada. Además, los resultados obtenidos muestran que los atributos que mejor discriminan las lesiones de regiones sanas son el diámetro medio, la intensidad en el centro y la distancia en dirección vertical a la carina

Luces y sombras en la modelización de la COVID-19

Objectius de Desenvolupament Sostenible::3 - Salut i Benestar::3.3 - Per a 2030, posar fi a les epidèmies de la sida, tuberculosi, malària i les malalties tropicals desateses, i com­batre l’hepatitis, les malalties transmeses per l’aigua i altres malalties transmissiblesObjectius de Desenvolupament Sostenible::3 - Salut i BenestarPostprint (published version