Multi-scale alignment and Spatial ROI Module for COVID-19 Diagnosis

Coronavirus Disease 2019 (COVID-19) has spread globally and become a health crisis faced by humanity since first reported. Radiology imaging technologies such as computer tomography (CT) and chest X-ray imaging (CXR) are effective tools for diagnosing COVID-19. However, in CT and CXR images, the infected area occupies only a small part of the image. Some common deep learning methods that integrate large-scale receptive fields may cause the loss of image detail, resulting in the omission of the region of interest (ROI) in COVID-19 images and are therefore not suitable for further processing. To this end, we propose a deep spatial pyramid pooling (D-SPP) module to integrate contextual information over different resolutions, aiming to extract information under different scales of COVID-19 images effectively. Besides, we propose a COVID-19 infection detection (CID) module to draw attention to the lesion area and remove interference from irrelevant information. Extensive experiments on four CT and CXR datasets have shown that our method produces higher accuracy of detecting COVID-19 lesions in CT and CXR images. It can be used as a computer-aided diagnosis tool to help doctors effectively diagnose and screen for COVID-19. © 2022 IEEE.

Image data augmentation for improving performance of deep learning-based model in pathological lung segmentation

Accurate segmentation of lung fields from chest Xray (CXR) images is very important for subsequent analysis of many pulmonary diseases. Deep Neural Networks (DNN)-based methods have achieved remarkable progress in many image related tasks. However, their performance depends highly on the distribution of training and test samples, and they perform well if both training and test samples are from the same distribution. For example, DNN-based lung segmentation methods perform well on segmentation of healthy lung or lung with mild disease, however their performance is poor on lungs with severe abnormalities. Pulmonary opacification, which blurs the lung boundary, is one of the main reasons. A solution to this problem is data augmentation to increase the pool of training images, however despite the great success of traditional data augmentation techniques for natural images, they are not very effective for medical images. To simulate CXR images with opacification and low contrast, we present a novel image data augmentation technique in this study. To generate an augmented image, we first generate a random area inside the lung and then blur the area with a gaussian filter. Then, low contrast is simulated by adjusting the contrast and brightness. To evaluate the utility of the proposed augmentation technique, we applied it to images with different pulmonary diseases such as tuberculosis, pneumoconiosis and covid-19 from three public datasets as well as a private dataset and compared its effect on segmentation performance with traditional data augmentation techniques. Results suggest that the proposed technique outperforms traditional data augmentation techniques for all datasets on lung segmentation, in terms of Dice Coefficient (DC) and Jaccard Index (JI). Extensive experiments on multiple datasets validate the effectiveness of the proposed data augmentation technique.

Bidirectional Convolutional-LSTM based Network for lung segmentation of chest X-ray images

Deep Neural Networks (DNN)-based methods, particularly UNet, are considered as state-of-the-art for many medical imaging tasks. However, despite remarkable progress on segmenting the normal lung, performance of the UNet is unsatisfactory on challenging chest X-ray (CXR) images. This could be due to mainly two limiting factors: (1) skip connections that merge feature maps of similar size from encoding and decoding paths, and (2) loss of spatial information due to repetitive down-sampling operations. To overcome these problems, in this study, we propose a DNN-based new architecture that replaces the skip connections with a bidirectional convolutional-LSTM (BC-LSTM) module that allows exchange of more information between encoder and decoder paths and also capture spatiotemporal information. For further improvement, we add a multiple kernel pooling (MKP) block at the lowest level of UNet to encode more spatial information by different sized pooling operations. To evaluate the performance of our method, we use CXR images with different pulmonary diseases such as tuberculosis, pneumoconiosis, and Covid-19 from four public datasets as well as a private dataset and compare its performance with a standard UNet model. Results suggest that the proposed framework outperforms the UNet for all five datasets on lung segmentation, in terms of two evaluation metrics, namely Dice Coefficient (DC) and Jaccard Index (JI). © 2021 IEEE.

Dangers of mRNA vaccines.

"Necessity is the mother of invention:" An adage was brought to life with the emergence of the mRNA vaccine against the backdrop of the foreboding and mercurial COVID-19 pandemic. Considering a negligible adverse-effect profile and a break-neck manufacturing speed, it shone bright as the ideal vaccine candidate. However, "all that glitters is not gold," as was evidenced by the significant reactogenicity, a host of multi-systemic side-effects, that are being reported by the vaccine recipients; which is palpably resulting in a shift of emotions for the vaccine, accounting for vaccine hesitancy. Anaphylaxis, antibody-dependent enhancements, and deaths, comprise the most serious side-effects, albeit occurring in sparing numbers. Storage and transportation require fastidious temperatures, rendering it substantially inaccessible to a country like India. The biggest jolt, however, was the unfolding of the biases in reporting vaccine efficacy, as only the attractively high numbers of the relatively equivocal relative risk reduction were reported while keeping at bay the meager numbers of the more forthright absolute risk reduction. Notwithstanding the fallacies, the mRNA vaccine still promises hope; and with the right precautions and finesse, can be potentiated, as "a watched pot never boils."

Impact of COVID-19 pandemic on persons with psychiatric disorders.

From the beginning of COVID-19 pandemic, concerns have been raised about its effect on mental health and on patients with psychiatric illnesses. A few reports suggest that patients with COVID-19 have increased symptoms of anxiety disorders, post-traumatic stress disorder, depression, increased substance use, and insomnia. There is an increased trend seen in flare-up of psychotic symptoms and new emergence of psychotic symptoms in previously healthy adults. There is extensive research available on the impact of COVID-19 on physical health, but there is a paucity of studies on the effects of COVID-19 on psychiatric illness.

Impact of COVID-19 on obsessive-compulsive disorder: A case series.

A public health emergency of international concern, novel coronavirus disease (COVID-19), began in Wuhan, China, in December 2019. Since then, it has been caused a pandemic along with serious mental health problems. No other disorder is more vulnerable to the current situation than obsessive-compulsive disorder (OCD). Our case series focuses on the effects of COVID-19 on OCD along with its various manifestations. Most of our patients had exacerbated symptoms during the current situation, but there were also new onset OCD cases triggered by a variety of stressors.

Exploring the therapeutic role of nanomedicine in COVID-19 disease

COVID-19 or the Coronavirus disease 2019, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a great pandemic. At the time of writing this (29th March 2021), more than 127 M people had affected and 2.78 M had died across the world. Due to the lack of specific treatment against COVID-19, antiviral agents and effective medicines are critically needed to prevent the COVID-19 pandemic. Revised drugs such as remdesivir have revealed a favourable clinical efficacy against COVID-19. This review provides an overview of the origin of coronavirus, the role of nanomedicine in coronavirus, nanomedicine vaccines, diagnosis, and therapy against coronavirus. This information may cause any effect in the disease outbreak. Taking all this under consideration, an effort has been made to teach readers the easiest method of the role of nanomedicine, which may play a pivotal role in the management of diseases.