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

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

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

Developing a Raman spectroscopy-based tool to stratify patient response to pre-operative radiotherapy in rectal cancer

Rectal cancer patients frequently receive pre-operative radiotherapy (RT), prior to surgical resection. However, colorectal cancer is heterogeneous and the degree of tumour response to pre-operative RT is highly variable. There are currently no clinically approved methods of predicting response to RT, and a significant proportion of patients will show no clinical benefit, despite enduring the side-effects. We evaluated the use of Raman spectroscopy (RS), a non-destructive technique able to provide the unique chemical fingerprint of tissues, as a potential tool to stratify patient response to pre-operative RT. Raman measurements were obtained from the formalin-fixed, paraffin-embedded (FFPE) pre-treatment biopsy specimens of 20 rectal cancer patients who received pre-operative RT. A principal component analysis and linear discriminant analysis algorithm was able to classify patient response to pre-operative RT as good or poor, with an accuracy of 86.04 ± 0.14% (standard error). Patients with a good response to RT showed greater contributions from protein-associated peaks, whereas patients who responded poorly showed greater lipid contributions. These results demonstrate that RS is able to reliably classify tumour response to pre-operative RT from FFPE biopsies and highlights its potential to guide personalised cancer patient treatment