COVID-19 Vaccine Infodemiology using Unsupervised Natural Language Processing

COVID-19 pandemic has resulted in excess mortality globally and presented an unprecedented challenge to people's lives. Despite the benefits of getting a COVID-19 vaccine, there have been arguments against the available vaccines and vaccine hesitancy worldwide. In this work, we analyze the information published by the public on Reddit as a digital forum, using unsupervised natural language processing to discover useful insights from the collected data related to COVID-19 vaccines, and validate the results of our study using Google Trends. Our results show that the government's contributions to the vaccination process, vaccine side-effects, and opposition to vaccine mandate and lock-downs are the main concerns shared by the public on digital forums. Moreover, we provide our collected data publicly available for further infodemiology studies by researchers and practitioners. © 2022 IEEE.

The support of healthcare workers suffering from COVID 19

Introduction : The COVID-19 pandemic has focused attention on the challenges and risks faced by frontline healthcare workers (HCW). This study aimed to describe the quality of management of HCW affected by the COVID-19. Methods : This is a cross-sectional study enrolling all HCW of Farhat Hached Academic hospital who had been affected by COVID-19 during the period from september to December 2020. Results : During the study period, 27 HCW were affected with a mean age of 42.3 ±10 years and a ratio-sex of 0.25. The most represented category was nurses (33.3%) followed by technicians (26.1%). Gynecology department had the highest number of affected HCW (14.4%) followed by pediatric department and administration in 7.2% and 5.7% respectively. The mean of seniority was 14.5± 11 years. The majority of participants (97.4%) reported a medical care. Twelve HCW (4.5%) were hospitalized with an average length of hospital stay of 7.55 ± 6.12 days. The average length of sick leave was 18.68 ± 10.99 days. During the lockdown, 38.6% of HCW took care of their children without any external help. All of the HCW were supported by phone calls from colleagues in 88.4% of cases, the hierarchy in 67.4% of cases, occupational medicine in 60.3% of cases. Conclusion : The impact of COVID 19 is greater in HCW than in the general population. The affected staff should have a multidimensional management to avoid post covid sequelae in both physical and mental levels.

Pre-study for facilitating the discovery of microfluidic properties in blood vessels using retinal fundus images

COVID-19 disease may cause alterations of microfluidic properties of blood circulation in the retinal tissue. For the pre-study of microfluidic blood physiology and transport, retinal fundus images are applied for clinical screening of abnormal vessels forms. However, fundus images captured by operators with various levels of experience have diversity in quality. Low-quality fundus images increase uncertainty in clinical observation and lead to the risk of misdiagnosis. Due to the optical beam of fundus image acquisition and vessel structure of the retina, natural image restoration methods cannot be applied straight to address this problem. The semi-automatic blind deblurring is a useful technique to restore the underlying sharp image given some assumed or known information about the cause of degradation. In this work, we propose a new hybrid algorithm for image restoration that does not require a priori knowledge of the noise distribution. The degraded image is first de-convoluted in Fourier space by parametric Wiener filtering;then, it is smoothed by using the anisotropic diffusion. The filtering model was tested on 177 fundus images. Experiment filtering results show the efficiency of our algorithm with a superlative performance (p-value < 0.05) when compared with state of the art methods.

The impact of the covid-19 pandemic on the management of cirrhotic patients

Introduction: The COVID-19 pandemic has disrupted the processes developed to provide quality care to patients with cirrhosis. The care of these patients has certainly been changed by the crisis, but lasting lessons have been learned. Aims & Methods: The aim of this work was to assess the impact of the COVID-19 pandemic (first wave) on the management of cirrhotic patients, and its consequences on the course of the disease. This is a single-center cross-sectional study of all patients followed for cirrhosis who should present for consultation during the three months during the first wave (April-May-June). Data collection was carried out by consulting patient data as well as computerized emergency medical records. For the missing data, they were collected through a telephone conversation. Results: 96 patients were included, with mean age of 60.92 years [49-77 years], and a sex ratio (M / F) of 1.18. The etiology of the cirrhosis was: viral hepatitis B (33.3%), viral hepatitis C (33.3%), chronic alcoholism (4.2%), autoimmune hepatitis ( 4.2%), NASH (12.5%), and of undetermined cause (12.5%). The mean duration of disease progression was 3.87 years [1-14 years]. Only two patients were on long-term immunosuppressive therapy. In 54.2% of cases the cirrhosis was already decompensated before the pandemic. It was classified as Child-Pugh A (58.3%), B (37.5%) and C (4.2%), with a Meld score> 15 in 12.5% of cases. During the first wave of the pandemic, 37.5% of patients stopped their treatments: by unavailability (8.3%), by lack of renewal following the interruption of consultations (25%), for fear of ''increase the risk of infection with COVID-19 (4.2%). The mean duration of treatment discontinuation was 5.67 weeks [2-12 weeks]. At the time of containment, treatment was not always available in 70.8% of cases. 52 patients (54.2%) missed their consultation appointments: because of the interruption of consultations (37.5%), the ban on travel between cities (8.3%), or for fear of infection (8.3%). 58.3% of patients described worsening of their symptoms. 56 patients (58.4%) had consulted the emergency room during confinement, the reason for the consultation was: digestive hemorrhage (57.1%), abdominal pain (28.5%), edema-ascitic syndrome (7 , 1%) and jaundice (7.1%). 54.2% of patients were hospitalized in our department at least once during the pandemic. Hepatocellular carcinoma was diagnosed in 38.5% of patients. The mean length of hospital stay was 5.47 days [3-7]. 36 patients had worsening hepatocellular function (change from stage B to Child-Pugh stage C in 20 patients, and from stage A to stage B in 16 patients). In multivariate analysis, the occurrence of complications was significantly associated with the duration of treatment discontinuation (p = 0.034), and the discontinuation of consultations (p = 0.049). Nasopharyngeal swabs for Covid PCR were performed in 16.7% of patients following the appearance of suggestive signs, all coming back negative. Conclusion: The COVID-19 pandemic had a significant pejorative impact on the management of cirrhotic patients. The management of this serious chronic disease during this period constitutes a real challenge, justifying the development of alternative measures.

Building a smart dynamic kernel with compact support based on deep neural network for efficient X-ray image denoising

Gaussian filtering is a successful computer operation vision to reduce noise and calculate the gradient intensity change of an image. However, it’s well known that in scale space context, the Gaussian kernel has some drawbacks, loss of information caused by the unavoidable Gaussian truncation and the prohibitive processing time due to the mask size. To give a solution to both problems, a new kernel family with compact support and its separable version were presented in the literature. The theoretical study of these kernels shows that the new family kernel is parameterised by a scale parameter and generated in such a way that fine scale structures are successively suppressed when the scale parameter is increased. Moreover, the scale parameter is increased, the image is blurred and details and border are removed. All these disadvantages are related to the static nature of these kernels. In this paper, we propose a smart kernel based on deep neural networks (dnn) to create a dynamic kernel with compact support called DSKCS. The parameter involved in the filtering process is calculated in real time and supervised by deep neural networks. The filter is continuously variable to detect, clean and avoid noisy areas of the image. Extensive experiments show that the proposed kernel can improve the classic kernel and presents a solution for its limitations related to its static nature. Furthermore, different metrics calculated illustrate our approach efficiency. As stated in the filtering performance, which reveals the highest PSNR and NCC with the metrics results (PSNR = 32.18, NCC = 0.95). Also, we recorded more than 0.89 for area under curves of the classification results using DBN-DNN technique.

Deep Neural Network for Covid-19 Pandemic Recognition Using CT Data

Covid-19 pandemic detection is the key to health safety and coronavirus prevention. Due to the complex changes in CT scan treatment, it is difficult to identify the Covid-19 in the lung image. According to the latest clinical research, an automated fast framework is still required to resolve error prone problem from the pandemic assessment and Covid19 patients screening during this critical control period. Computer aided methods can be very useful in this regard. They are suitable to estimate the infected lung boundary based on elliptical Hough transform with reduced time processing. In this paper, we propose to use a computerized approach to show that the deep neural network (DNN) is a distinctive method to classify Covid-19 pandemic. Experimental results on various lung CT scan images of different Covid-19 patients, demonstrate the effectiveness of the proposed methodology when compared to the manual scoring of pathological experts. According to the performance evaluation, we recorded more than 92% for accuracy of infection detected in ROI scoring over the truths provided by experienced radiologists. Comparative automatic studies are performed to demonstrate the suitability of the proposed technique over other advanced techniques from the literature.