COVID-19 Mortality Risk Prediction Using X-Ray Images

The pandemic caused by coronavirus COVID-19 has already had a massive impact in our societies in terms of health, economy, and social distress. One of the most common symptoms caused by COVID-19 are lung problems like pneumonia, which can be detected using X-ray images. On the other hand, the popularity of Machine Learning models has grown exponentially in recent years and Deep Learning techniques have become the state-of-the-art for image classification tasks and is widely used in the healthcare sector nowadays as support for clinical decisions. This research aims to build a prediction model based on Machine Learning, including Deep Learning, techniques to predict the mortality risk of a particular patient given an X-ray and some basic demographic data. Keeping this in mind, this paper has three goals. First, we use Deep Learning models to predict the mortality risk of a patient based on this patient X-ray images. For this purpose, we apply Convolutional Neural Networks as well as Transfer Learning techniques to mitigate the effect of the reduced amount of COVID19 data available. Second, we propose to combine the prediction of this Convolutional Neural Network with other patient data, like gender and age, as input features of a final Machine Learning model, that will act as second and final layer. This second model layer will aim to improve the goodness of fit and prediction power of our first layer. Finally, and in accordance with the principle of reproducible research, the data used for the experiments is publicly available and we make the implementations developed easily accessible via public repositories. Experiments over a real dataset of COVID-19 patients yield high AUROC values and show our two-layer framework to obtain better results than a single Convolutional Neural Network (CNN) model, achieving close to perfect classification

EAHP European Statements baseline survey 2015

Objectives The 2015 EAHP European Statements survey was related to sections 2, 5 and 6 of the European Statements of Hospital Pharmacy (Statements). In addition to collection of statistical data about the level of implementation of the Statements, it was also intended to identify important barriers to their implementation. Methods The online questionnaire was sent to all hospital pharmacies in EAHP member countries. Data were analysed by researchers from Keele University School of Pharmacy, UK and the EAHP Survey Group. Results There were a total of 949 responses (response rate 18%). In the first part of the survey, data was collected on hospital pharmacy setting. While almost half of hospital pharmacies served over 500 beds, 80% of hospital pharmacies had 10 or less pharmacists. In section B, we gathered evidence about the degree of implementation of sections 2, 5 and 6 of the Statements and the main barriers to and drivers of implementation. Five questions with the lowest implementation level were then further analysed. Only five countries had 50% or more of hospital pharmacies reporting that the hospital pharmacists routinely publish hospital pharmacy practice research. 67% of participants stated that they had contingency plans for medicines shortages. The majority of countries (n=20) have less than half of respondents using computerised decision support to reduce the risk of medication errors. When asked if an audit had been undertaken in the past 3 years to identify priorities in medicines use processes, the mean percentage of positive responses for a country was 58%. Conclusions EAHP has gained an informative overview of the implementation level as well as the barriers to and drivers of implementation in sections 2, 5 and 6. This is essential to inform the plans for EAHP to best support their implementation

Predicting Puget Sound\u27s organic carbon—and why we need enhanced monitoring

How much has the total organic carbon deposited into the water column and sediments of Puget Sound increased due to human activity? How has that increase impacted sediment flux rates, hypoxia and the carbonate system balance? These are two important questions with answers that are still elusive. To date, both marine and freshwater organic carbon measurements in Puget Sound are relatively sparse. In the long-term, inadequate temporal and spatial organic carbon data may lead to an incomplete and incoherent understanding of carbon cycling in the Puget Sound. The Salish Sea Model, developed by PNNL in collaboration with Department of Ecology, provides insights into the extent of organic carbon loading and concentrations in the Puget Sound. Model scenario runs indicate that autochtonous organic detritus derived from increased productivity related to human nitrogen loading, combined with allochthonous carbon from direct loading due to human activity, has resulted in an increased loading of non-algal organic carbon ranging from 20 and 25% in a significant portion of the Puget Sound’s main basin, as well as in multiple inlets. This increase in organic carbon is expected to have an impact in heterotrophic respiration rates and eutrophication. This presentation will focus on loading rates and predicted organic carbon concentrations throughout the Puget Sound using the Salish Sea Model. It will point to the need for enhanced dissolved and particulate organic carbon measurements in our region, as well as basin-scale measurements of respiration rates, to optimize the alignment of on-going, long term monitoring and modeling efforts

Emotional and mental nuances and technological approaches: Optimising Fact-Check dissemination through cognitive reinforcement technique †

The issue of the dissemination of fake news has been widely addressed in the literature, but the issue of the dissemination of fact checks to debunk fake news has not received sufficient attention. Fake news is tailored to reach a wide audience, a concern that, as this paper shows, does not seem to be present in fact checking. As a result, fact checking, no matter how good it is, fails in its goal of debunking fake news for the general public. This paper addresses this problem with the aim of increasing the effectiveness of the fact checking of online social media posts through the use of cognitive tools, yet grounded in ethical principles. The paper consists of a profile of the prevalence of fact checking in online social media (both from the literature and from field data) and an assessment of the extent to which engagement can be increased by using simple cognitive enhancements in the text of the post. The focus is on Snopes and (Formula presented.) (formerly Twitter).FCT -Fundação para a Ciência e a Tecnologia(2022.06822

Combined flow-focus and self-assembly routes for the formation of lipid stabilized oil-shelled microbubbles

Lipid and polymer stabilized microbubbles are used in medicine as contrast agents for ultrasound imaging and are being developed for the delivery of water soluble drugs to diseased areas of the body. However, many new therapeutics exhibit poor water solubility or stability, which has led to the requirement for the development of effective hydrophobic drug delivery systems. This study presents a new method to produce microbubbles coated with an oil layer capable of encapsulating hydrophobic drugs and suitable for targeted, triggered drug release. This new method utilizes highly controllable flow-focusing microfluidics with lipid oil nanodroplets self-assembling and spreading at gas–aqueous interfaces. Oil layer inside microbubbles were produced with diameters of 2.4±0.3 μm (s.d., 1.6 μm) and at concentrations up to 106 bubbles per milliliter. The mechanism of oil layer inside microbubble assembly and stability were characterized using methods including contact angle measurements, quartz crystal microbalance with dissipation monitoring and fluorescence resonance energy transfer imaging

Physical biomarkers of disease progression:on-chip monitoring of changes in mechanobiology of colorectal cancer cells

Disease can induce changes to subcellular components, altering cell phenotype and leading to measurable bulk-material mechanical properties. The mechanical phenotyping of single cells therefore offers many potential diagnostic applications. Cells are viscoelastic and their response to an applied stress is highly dependent on the magnitude and timescale of the actuation. Microfluidics can be used to measure cell deformability over a wide range of flow conditions, operating two distinct flow regimes (shear and inertial) which can expose subtle mechanical properties arising from subcellular components. Here, we investigate the deformability of three colorectal cancer (CRC) cell lines using a range of flow conditions. These cell lines offer a model for CRC metastatic progression; SW480 derived from primary adenocarcinoma, HT29 from a more advanced primary tumor and SW620 from lymph-node metastasis. HL60 (leukemia cells) were also studied as a model circulatory cell, offering a non-epithelial comparison. We demonstrate that microfluidic induced flow deformation can be used to robustly detect mechanical changes associated with CRC progression. We also show that single-cell multivariate analysis, utilising deformation and relaxation dynamics, offers potential to distinguish these different cell types. These results point to the benefit of multiparameter determination for improving detection and accuracy of disease stage diagnosis