A Robust Automated Framework for Classification of CT Covid-19 Images Using MSI-ResNet

Nowadays, the COVID-19 virus disease is spreading rampantly. There are some testing tools and kits available for diagnosing the virus, but it is in a limited count. To diagnose the presence of disease from radiological images, automated COVID-19 diagnosis techniques are needed. The enhancement of AI (Artificial Intelligence) has been focused in previous research, which uses X-ray images for detecting COVID-19. The most common symptoms of COVID-19 are fever, dry cough and sore throat. These symptoms may lead to an increase in the rigorous type of pneumonia with a severe barrier. Since medical imaging is not suggested recently in Canada for critical COVID-19 diagnosis, computer-aided systems are implemented for the early identification of COVID-19, which aids in noticing the disease progression and thus decreases the death rate. Here, a deep learning-based automated method for the extraction of features and classification is enhanced for the detection of COVID-19 from the images of computer tomography (CT). The suggested method functions on the basis of three main processes: data preprocessing, the extraction of features and classification. This approach integrates the union of deep features with the help of Inception 14 and VGG-16 models. At last, a classifier of Multi-scale Improved ResNet (MSI-ResNet) is developed to detect and classify the CT images into unique labels of class. With the support of available open-source COVID-CT datasets that consists of 760 CT pictures, the investigational validation of the suggested method is estimated. The experimental results reveal that the proposed approach offers greater performance with high specificity, accuracy and sensitivity. © 2023 CRL Publishing. All rights reserved.

Parotid Abscess in A Case of Acute Lymphoblastic Leukemia

Parotid gland enlargement as a presenting manifestation of acute lymphoblastic leukemia (ALL) is very rare, even though it has been reported in acute myeloid leukemia. Here we present a case of parotid abscess in a case of ALL in the presence of Dengue.

The Secondary Economic Sector’s Role in Post-pandemic Recovery: Analysis in the Neoliberal Capitalism Context

In the context of systemic crisis exacerbated by the COVID-19 pandemic, origin of which is implicit in the instrumental rationality of the neoliberal economy, this research proposes a conceptual approach explaining the decisions toward economic recovery adopted by governments. This study analyzes the institutional arrangements that characterize the secondary economic sector aimed at confronting the existing relationship between economic development and economic growth. Therefore, this research is based on the economic actions that characterize current public policies;it argues that government intervention distorts the prices of products and services which are necessary to achieve a balanced economic growth. Such policies include government investment programs that stimulate the economy and invigorate the market through resource mobility as a result of fiscal stimuli or public debt, postulates of which are located in the Keynesian economic model. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Remote assessment of action tremors during COVID-19: A pooled meta-analysis of transcutaneous afferent patterned stimulation in Parkinson's disease and essential tremor

Objective: To compare action tremor assessed in-person, remotely by telemedicine, and remotely using objective motion-sensor data. Background: Access to clinical care has become more difficult for patients living with chronic diseases such as Essential Tremor (ET) and Parkinson's Disease (PD), due to COVID-19. This meta-analysis compared studies using in-person or telemedicine clinical appointments along with motion-sensor measurements to assess the therapeutic benefit of Transcutaneous Afferent Patterned Stimulation (TAPS) [Pahwa R. et al., 2019] for action tremor (i.e., tremor elicited by voluntary muscle contraction, including postural and kinetic tremors) in ET and PD patients. Design/Methods: De-identified tremor motion data were compared across three cohorts receiving TAPS therapy under clinical supervision: (1) ET action tremor assessed in-person and remotely (N=193;3-month motion-sensor remote assessment with 3 in-person visits);(2) PD action tremor assessed in-person (N=11;5 in-person visits), and (3) PD action tremor assessed remotely (telemedicine visits over 1-month;currently enrolling). The pooled analysis compared (i) sensor-measured improvements in tremor severity with TAPS therapy, and (ii) correlation between clinician-rated and sensor-measured assessments of tremor severity. Results: ET patients experienced a median 42% reduction in tremor severity in-clinic and 53% reduction in tremor severity at home (study 1) [Isaacson S. et al., 2020]. PD patients experienced a median 38% reduction in tremor severity (study 2). Sensor-measured reductions in severity were consistent across patient cohorts (p=0.3, PD vs ET in-person), but different across assessment locations (p<0.01, ET in-person vs ET remote). Clinical gold-standard ratings of tremor severity were correlated with simultaneously-measured sensor assessments (r=0.67 (study 1)1;0.89 (study 2)). Conclusions: These findings provide consistent measures of action tremor reduction from TAPS therapy in PD and ET and suggest that occasional, in-person assessments may not always capture daily at-home improvements;and demonstrate how wearable sensors can be incorporated into remote clinical studies via telemedicine for patients with movement disorders.