Use of Virtual Reality and Videogames in the Physiotherapy Treatment of Stroke Patients: A Pilot Randomized Controlled Trial

© 2023 by the authors. This document is made available under the CC-BY 4.0 license http://creativecommons.org/licenses/by /4.0/ This document is the Accepted version of a Published Work that appeared in final form in International Journal of Environmental Research and Public Health. To access the final edited and published work see https:// doi.org/10.3390/ijerph20064747 AA stroke is a neurological condition with a high impact in terms of physical disability in the adult population, requiring specific and effective rehabilitative approaches. Virtual reality (VR), a technological approach in constant evolution, has great applicability in many fields of rehabilitation, including strokes. The aim of this study was to analyze the effects of a traditional neurological physiotherapy-based approach combined with the implementation of a specific VR-based program in the treatment of patients following rehabilitation after a stroke. Participants (n = 24) diagnosed with a stroke in the last six months were randomly allocated into a control group (n = 12) and an experimental group (n = 12). Both groups received one-hour sessions of neurological physiotherapy over 6 weeks, whilst the experimental group was, in addition, supplemented with VR. Patients were assessed through the Daniels and Worthingham Scale, Modified Ashworth Scale, Motor Index, Trunk Control Test, Tinetti Balance Scale, Berg Balance Scale and the Functional Ambulation Classification of the Hospital of Sagunto. Statistically significant improvements were obtained in the experimental group with respect to the control group on the Motricity Index (p = 0.005), Trunk Control Test (p = 0.008), Tinetti Balance Scale (p = 0.004), Berg Balance Scale (p = 0.007) and the Functional Ambulation Classification of the Hospital of Sagunto (p = 0.038). The use of VR in addition to the traditional physiotherapy approach is a useful strategy in the treatment of strokes

MEGARA focal plane subsystems

MEGARA (Multi-Espectrografo en GTC de Alta Resolucion para Astronomia) is the future optical Integral-Field Unit (IFU) and Multi-Object Spectrograph (MOS) for GTC. The Fiber Units are placed at one Folded Cassegrain focus and feed the spectrograph located on a Nasmyth-type platform. This paper summarizes the status of the design of the MEGARA Folded Cassegrain Subsystems after the PDR (held on March 2012), as well as the prototyping that has been carried out during this phase. The MEGARA Fiber Unit has two IFUs: a Large Compact Bundle covering 12.5 arcsec x 11.3 arcsec on sky (100 microns fiber-core), and a Small Compact Bundle, of 8.5 arcsec x 6.7 arcsec (70 microns fiber-core), plus a Fiber MOS positioner, able to place up to 100 mini-bundles 7 fibers each (100 microns fiber-core) in MOS configuration within a 3.5arcmin x 3.5arcmin FOV. A field lens provides a telecentric focal plane where the fibers are located. Microlens arrays couple the telescope beam to the collimator focal ratio at the entrance of the fibers (providing the f/17 to f/3 focal ratio reduction to enter into the fibers). Finally, the fibers, organized in bundles, end in the pseudo-slit plate, which will be placed at the entrance focal plane of the MEGARA spectrographs

Activation of T-bet, FOXP3, and EOMES in Target Organs From Piglets Infected With the Virulent PRRSV-1 Lena Strain

Transcription factors (TFs) modulate genes involved in cell-type-specific proliferative and migratory properties, metabolic features, and effector functions. Porcine reproductive and respiratory syndrome virus (PRRSV) is one of the most important pathogen agents in the porcine industry; however, TFs have been poorly studied during the course of this disease. Therefore, we aimed to evaluate the expressions of the TFs T-bet, GATA3, FOXP3, and Eomesodermin (EOMES) in target organs (the lung, tracheobronchial lymph node, and thymus) and those of different effector cytokines (IFNG, TNFA, and IL10) and the Fas ligand (FASL) during the early phase of infection with PRRSV-1 strains of different virulence. Target organs from mock-, virulent Lena-, and low virulent 3249-infected animals humanely euthanized at 1, 3, 6, 8, and 13 days post-infection (dpi) were collected to analyze the PRRSV viral load, histopathological lesions, and relative quantification through reverse transcription quantitative PCR (RT-qPCR) of the TFs and cytokines. Animals belonging to both infected groups, but mainly those infected with the virulent Lena strain, showed upregulation of the TFs T-bet, EOMES, and FOXP3, together with an increase of the cytokine IFN-g in target organs at the end of the study (approximately 2 weeks post-infection). These results are suggestive of a stronger polarization to Th1 cells and regulatory T cells (Tregs), but also CD4+ cytotoxic T lymphocytes (CTLs), effector CD8+ T cells, and gdT cells in virulent PRRSV-1-infected animals; however, their biological functionality should be the object of further studies

Activation of pro- and anti-inflammatory responses in lung tissue injury during the acute phase of PRRSV-1 infection with the virulent strain Lena

Porcine reproductive and respiratory syndrome virus (PRRSV) plays a key role in porcine respiratory disease complex modulating the host immune response and favouring secondary bacterial infections. Pulmonary alveolar macrophages (PAMs) are the main cells supporting PRRSV replication, with CD163 as the essential receptor for viral infection. Although interstitial pneumonia is by far the representative lung lesion, suppurative bronchopneumonia is described for PRRSV virulent strains. This research explores the role of several immune markers potentially involved in the regulation of the inflammatory response and sensitisation of lung to secondary bacterial infections by PRRSV-1 strains of different virulence. Conventional pigs were intranasally inoculated with the virulent subtype 3 Lena strain or the low virulent subtype 1 3249 strain and euthanised at 1, 3, 6 and 8 dpi. Lena-infected pigs exhibited more severe clinical signs, macroscopic lung score and viraemia associated with an increase of IL-6 and IFN-γ in sera compared to 3249-infected pigs. Extensive areas of lung consolidation corresponding with suppurative bronchopneumonia were observed in Lena-infected pigs. Lung viral load and PRRSV-N-protein+ cells were always higher in Lena-infected animals. PRRSV-N-protein+ cells were linked to a marked drop of CD163+ macrophages. The number of CD14+ and iNOS+ cells gradually increased along PRRSV-1 infection, being more evident in Lena-infected pigs. The frequency of CD200R1+ and FoxP3+ cells peaked late in both PRRSV-1 strains, with a strong correlation between CD200R1+ cells and lung injury in Lena-infected pigs. These results highlight the role of molecules involved in the earlier and higher extent of lung lesions in piglets infected with the virulent Lena strain, pointing out the activation of routes potentially involved in the restraint of the local inflammatory response.info:eu-repo/semantics/acceptedVersio

Oncogenic Role of Secreted Engrailed Homeobox 2 (EN2) in Prostate Cancer

Engrailed variant-2 (EN2) has been suggested as a potential diagnostic biomarker; however, its presence and functional role in prostate cancer (PCa) cells is still controversial or unknown. Here, we analyzed 1) the expression/secretion profile of EN2 in five independent samples cohorts from PCa patients and controls (prostate tissues and/or urine) to determine its utility as a PCa biomarker; and 2) the functional role of EN2 in normal (RWPE1) and tumor (LNCaP/22Rv1/PC3) prostate cells to explore its potential value as therapeutic target. EN2 was overexpressed in our two cohorts of PCa tissues compared to control and in tumor cell lines compared with normal-like prostate cells. This profile was corroborated in silico in three independent data sets [The Cancer Genome Atlas(TCGA)/Memorial Sloan Kettering Cancer Center (MSKCC)/Grasso]. Consistently, urine EN2 levels were elevated and enabled discrimination between PCa and control patients. EN2 treatment increased cell proliferation in LNCaP/22Rv1/PC3 cells, migration in RWPE1/PC3 cells, and PSA secretion in LNCaP cells. These effects were associated, at least in the androgen-sensitive LNCaP cells, with increased AKT and androgen-receptor phosphorylation levels and with modulation of key cancer-associated genes. Consistently, EN2 treatment also regulated androgen-receptor activity (full-length and splicing variants) in androgen-sensitive 22Rv1 cells. Altogether, this study demonstrates the potential utility of EN2 as a non-invasive diagnostic biomarker for PCa and provides novel and valuable information to further investigate its putative utility to develop new therapeutic tools in PCa

Transfer learning for galaxy morphology from one survey to another

© 2018 The Author(s). Published by Oxford University Press on behalf of the Royal Astronomical Society.Deep Learning (DL) algorithms for morphological classification of galaxies have proven very successful, mimicking (or even improving) visual classifications. However, these algorithms rely on large training samples of labelled galaxies (typically thousands of them). A key question for using DL classifications in future Big Data surveys is how much of the knowledge acquired from an existing survey can be exported to a new dataset, i.e. if the features learned by the machines are meaningful for different data. We test the performance of DL models, trained with Sloan Digital Sky Survey (SDSS) data, on Dark Energy survey (DES) using images for a sample of $\sim$5000 galaxies with a similar redshift distribution to SDSS. Applying the models directly to DES data provides a reasonable global accuracy ($\sim$ 90%), but small completeness and purity values. A fast domain adaptation step, consisting in a further training with a small DES sample of galaxies ($\sim$500-300), is enough for obtaining an accuracy > 95% and a significant improvement in the completeness and purity values. This demonstrates that, once trained with a particular dataset, machines can quickly adapt to new instrument characteristics (e.g., PSF, seeing, depth), reducing by almost one order of magnitude the necessary training sample for morphological classification. Redshift evolution effects or significant depth differences are not taken into account in this study.Peer reviewedFinal Accepted Versio

She\u27s So Bubbly

We introduce the Automatic Learning for the Rapid Classification of Events (ALeRCE) broker, an astronomical alert broker designed to provide a rapid and self-consistent classification of large etendue telescope alert streams, such as that provided by the Zwicky Transient Facility (ZTF) and, in the future, the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). ALeRCE is a Chilean-led broker run by an interdisciplinary team of astronomers and engineers working to become intermediaries between survey and follow-up facilities. ALeRCE uses a pipeline that includes the real-time ingestion, aggregation, cross-matching, machine-learning (ML) classification, and visualization of the ZTF alert stream. We use two classifiers: a stamp-based classifier, designed for rapid classification, and a light curve–based classifier, which uses the multiband flux evolution to achieve a more refined classification. We describe in detail our pipeline, data products, tools, and services, which are made public for the community (see https://alerce.science). Since we began operating our real-time ML classification of the ZTF alert stream in early 2019, we have grown a large community of active users around the globe. We describe our results to date, including the real-time processing of 1.5 × 10⁸ alerts, the stamp classification of 3.4 × 10⁷ objects, the light-curve classification of 1.1 × 10⁶ objects, the report of 6162 supernova candidates, and different experiments using LSST-like alert streams. Finally, we discuss the challenges ahead in going from a single stream of alerts such as ZTF to a multistream ecosystem dominated by LSST