Susceptibility-Weighted Magnetic Resonance Imaging Highlights Brain Alterations in COVID Recovered Patients. (preprint)

The increasing number of reports of mild to severe psychological, behavioral, and cognitive sequelae in COVID-19 survivors motivates a need for a thorough assessment of the neurological effects of the disease. In this regard, we have conducted a neuroimaging study to understand the neurotropic behavior of the coronavirus. We hypothesize that the COVID-recovered subjects have developed alterations in the brain which can be measured through susceptibility differences in various regions of the brain when compared to healthy controls (HCs). Hence, we performed our investigations on susceptibility-weighted imaging (SWI) volumes. Fatigue, being of the most common symptoms of Long COVID, has also been studied in this work. SWI volumes of 46 COVID and 30 HCs were included in this study. The COVID patients were imaged within six months of their recovery. We performed an unpaired two-sample t-test over the pre-processed SWI volumes of both groups and multiple linear regression was performed to observe group differences and correlation of fatigue with SWI values. The group analysis showed that COVID recovered subjects had significantly higher susceptibility imaging values in regions of the frontal lobe and the brain stem. The clusters obtained in the frontal lobe primarily show differences in the white matter regions. The COVID group also demonstrated significantly higher fatigue levels than the HC group. The regression analysis on the COVID group yielded clusters in the anterior cingulate gyrus and midbrain, which exhibited negative correlations with fatigue scores. This study suggests an association of Long COVID with prolonged effects on the brain and also indicates the viability of the SWI modality for analysis of post-COVID symptoms.

Assessing functional connectivity differences and work-related fatigue in surviving COVID-negative patients. (preprint)

The recent Coronavirus Disease 2019 (COVID-19) has affected all aspects of life around the world. Neuroimaging evidence suggests the novel coronavirus can attack the central nervous system (CNS), causing cerebro-vascular abnormalities in the brain. This can lead to focal changes in cerebral blood flow and metabolic oxygen consumption rate in the brain. However, the extent and spatial locations of brain alterations in COVID-19 survivors are largely unknown. In this study, we have assessed brain functional connectivity (FC) using resting-state functional MRI (RS-fMRI) in 38 (25 males) COVID patients two weeks after hospital discharge, when PCR negative and 31 (24 males) healthy subjects. FC was estimated using independent component analysis (ICA) and dual regression. The COVID group demonstrated significantly enhanced FC in regions from the Occipital and Parietal Lobes, comparing to the HC group. On the other hand, the COVID group exhibited significantly reduced FC in several vermal layers of the cerebellum. More importantly, we noticed negative correlation of FC with self-reported fatigue within regions from the Parietal lobe, which are known to be associated with fatigue. Keywords: COVID, Functional Connectivity, ICA, Fatigue, RS-fMRI

Higher Limbic and Basal Ganglia volumes in surviving COVID-negative patients and the relations to fatigue. (preprint)

BackgroundAmong systemic abnormalities caused by the novel coronavirus, little is known about the critical attack on the central nervous system (CNS). Few studies have shown cerebrovascular pathologies that indicate CNS involvement in acute patients. However, replication studies are necessary to verify if these effects persist in COVID-19 survivors more conclusively. Furthermore, recent studies indicate fatigue is highly prevalent among long-COVID patients. How morphometry in each group relate to work-related fatigue need to be investigated. MethodCOVID survivors were MRI scanned two weeks after hospital discharge. We hypothesized, these survivors will demonstrate altered gray matter volume (GMV) and experience higher fatigue levels when compared to healthy controls, leading to stronger correlation of GMV with fatigue. Voxel-based morphometry was performed on T1-weighted MRI images between 46 survivors and 30 controls. Unpaired two-sample t-test and multiple linear regression were performed to observe group differences and correlation of fatigue with GMV. ResultsThe COVID group experienced significantly higher fatigue levels and GMV of this group was significantly higher within the Limbic System and Basal Ganglia when compared to healthy controls. Moreover, while a significant positive correlation was observed across the whole group between GMV and self-reported fatigue, COVID subjects showed stronger effects within the Posterior Cingulate, Precuneus and Superior Parietal Lobule. ConclusionBrain regions with GMV alterations in our analysis align with both single case acute patient reports and current group level neuroimaging findings. We also newly report a stronger positive correlation of GMV with fatigue among COVID survivors within brain regions associated with fatigue, indicating a link between structural abnormality and brain function in this cohort.

High throughput detection and genetic epidemiology of SARS-CoV-2 using COVIDSeq next-generation sequencing

The rapid emergence of coronavirus disease 2019 (COVID-19) as a global pandemic affecting millions of individuals globally has necessitated sensitive and high-throughput approaches for the diagnosis, surveillance, and determining the genetic epidemiology of SARS-CoV-2. In the present study, we used the COVIDSeq protocol, which involves multiplex-PCR, barcoding, and sequencing of samples for high-throughput detection and deciphering the genetic epidemiology of SARS-CoV-2. We used the approach on 752 clinical samples in duplicates, amounting to a total of 1536 samples which could be sequenced on a single S4 sequencing flow cell on NovaSeq 6000. Our analysis suggests a high concordance between technical duplicates and a high concordance of detection of SARS-CoV-2 between the COVIDSeq as well as RT-PCR approaches. An in-depth analysis revealed a total of six samples in which COVIDSeq detected SARS-CoV-2 in high confidence which were negative in RT-PCR. Additionally, the assay could detect SARS-CoV-2 in 21 samples and 16 samples which were classified inconclusive and pan-sarbeco positive respectively suggesting that COVIDSeq could be used as a confirmatory test. The sequencing approach also enabled insights into the evolution and genetic epidemiology of the SARS-CoV-2 samples. The samples were classified into a total of 3 clades. This study reports two lineages B.1.112 and B.1.99 for the first time in India. This study also revealed 1,143 unique single nucleotide variants and added a total of 73 novel variants identified for the first time. To the best of our knowledge, this is the first report of the COVIDSeq approach for detection and genetic epidemiology of SARS-CoV-2. Our analysis suggests that COVIDSeq could be a potential high sensitivity assay for the detection of SARS-CoV-2, with an additional advantage of enabling the genetic epidemiology of SARS-CoV-2.

Validation of expert system enhanced deep learning algorithm for automated screening for COVID-Pneumonia on chest X-rays (preprint)

The coronavirus disease of 2019 (COVID-19) pandemic exposed a limitation of artificial intelligence (AI) based medical image interpretation systems. Early in the pandemic, when need was greatest, the absence of sufficient training data prevented effective deep learning (DL) solutions. Even now, there is a need for Chest-X-ray (CxR) screening tools in low and middle income countries (LMIC), when RT-PCR is delayed, to exclude COVID-19 pneumonia (Cov-Pneum) requiring transfer to higher care. In absence of local LMIC data and poor portability of CxR DL algorithms, a new approach is needed. Axiomatically, it is faster to repurpose existing data than to generate new datasets. Here, we describe CovBaseAI, an explainable tool which uses an ensemble of three DL models and an expert decision system (EDS) for Cov-Pneum diagnosis, trained entirely on datasets from the pre-COVID-19 period. Portability, performance, and explainability of CovBaseAI was primarily validated on two independent datasets. First, 1401 randomly selected CxR from an Indian quarantine-center to assess effectiveness in excluding radiologic Cov-Pneum that may require higher care. Second, a curated dataset with 434 RT-PCR positive cases of varying levels of severity and 471 historical scans containing normal studies and non-COVID pathologies, to assess performance in advanced medical settings. CovBaseAI had accuracy of 87% with negative predictive value of 98% in the quarantine-center data for Cov-Pneum. However, sensitivity varied from 0.66 to 0.90 depending on whether RT-PCR or radiologist opinion was set as ground truth. This tool with explainability feature has better performance than publicly available algorithms trained on COVID-19 data but needs further improvement.

High throughput detection and genetic epidemiology of SARS-CoV-2 using COVIDSeq next generation sequencing (preprint)

The rapid emergence of coronavirus disease 2019 (COVID-19) as a global pandemic affecting millions of individuals globally has necessitated sensitive and high-throughput approaches for the diagnosis, surveillance and for determining the genetic epidemiology of SARS-CoV-2. In the present study, we used the COVIDSeq protocol, which involves multiplex-PCR, barcoding and sequencing of samples for high-throughput detection and deciphering the genetic epidemiology of SARS-CoV-2. We used the approach on 752 clinical samples in duplicates, amounting to a total of 1536 samples which could be sequenced on a single S4 sequencing flow cell on NovaSeq 6000. Our analysis suggests a high concordance between technical duplicates and a high concordance of detection of SARS-CoV-2 between the COVIDSeq as well as RT-PCR approaches. An in-depth analysis revealed a total of six samples in which COVIDSeq detected SARS-CoV-2 in high confidence which were negative in RT-PCR. Additionally, the assay could detect SARS-CoV-2 in 21 samples and 16 samples which were classified inconclusive and pan-sarbeco positive respectively suggesting that COVIDSeq could be used as a confirmatory test. The sequencing approach also enabled insights into the evolution and genetic epidemiology of the SARS-CoV-2 samples. The samples were classified into a total of 3 clades. This study reports two lineages B.1.112 and B.1.99 for the first time in India. This study also revealed 1,143 unique single nucleotide variants and added a total of 73 novel variants identified for the first time. To the best of our knowledge, this is the first report of the COVIDSeq approach for detection and genetic epidemiology of SARS-CoV-2. Our analysis suggests that COVIDSeq could be a potential high sensitivity assay for detection of SARS-CoV-2, with an additional advantage of enabling genetic epidemiology of SARS-CoV-2.

A Systematic Meta-Analysis of CT Features of COVID-19: Lessons from Radiology (preprint)

Several studies have been published in the past few months describing the CT features of Coronavirus Disease 2019 (COVID-19). There is a great degree of heterogeneity in the study designs, lesion descriptors used and conclusions derived. In our systematic analysis and meta-review, we have attempted to homogenize the reported features and provide a comprehensive view of the disease pattern and progression in different clinical stages. After an extensive literature search, we short-listed and reviewed 49 studies including over 4145 patients with 3615 RT-PCR positive cases of COVID-19 disease. We have found that there is a good agreement among these studies that diffuse bilateral ground-glass opacities (GGOs) is the most common finding at all stages of the disease followed by consolidations and mixed density lesions. 78% of patients with RT-PCR confirmed COVID-19 infections had either ground-glass opacities, consolidation or both. Inter-lobular septal thickening was also found to be a common feature in many patients in advanced stages. The progression of these initial patchy ground-glass opacities and consolidations to diffuse lesions with septal thickening, air bronchograms in the advanced stages, to either diffuse white-out lungs needing ICU admissions or finally resolving completely without or with residual fibrotic strips was also found to be congruent among multiple studies. Prominent juxta-lesional pulmonary vessels, pleural effusion and lymphadenopathy in RT-PCR proven cases were found to have poor clinical prognosis. Additionally, we noted wide variation in terminology used to describe lesions across studies and suggest the use of standardized lexicons to describe findings related to diseases of vital importance.