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1.
ACS Biomater Sci Eng ; 8(7): 3087-3106, 2022 07 11.
Article in English | MEDLINE | ID: covidwho-1900423

ABSTRACT

The devastating effects of the coronavirus disease 2019 (COVID-19) pandemic have made clear a global necessity for antiviral strategies. Most fatalities associated with infection from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) result at least partially from uncontrolled host immune response. Here, we use an antisense compound targeting a previously identified microRNA (miRNA) linked to severe cases of COVID-19. The compound binds specifically to the miRNA in question, miR-2392, which is produced by human cells in several disease states. The safety and biodistribution of this compound were tested in a mouse model via intranasal, intraperitoneal, and intravenous administration. The compound did not cause any toxic responses in mice based on measured parameters, including body weight, serum biomarkers for inflammation, and organ histopathology. No immunogenicity from the compound was observed with any administration route. Intranasal administration resulted in excellent and rapid biodistribution to the lungs, the main site of infection for SARS-CoV-2. Pharmacokinetic and biodistribution studies reveal delivery to different organs, including lungs, liver, kidneys, and spleen. The compound was largely cleared through the kidneys and excreted via the urine, with no accumulation observed in first-pass organs. The compound is concluded to be a safe potential antiviral treatment for COVID-19.


Subject(s)
COVID-19 , MicroRNAs , Animals , Antiviral Agents/pharmacology , Antiviral Agents/therapeutic use , COVID-19/drug therapy , Humans , Mice , MicroRNAs/genetics , SARS-CoV-2 , Tissue Distribution
2.
Radiology ; 304(1): 185-192, 2022 07.
Article in English | MEDLINE | ID: covidwho-1741709

ABSTRACT

Background The long-term effects of SARS-CoV-2 infection on pulmonary structure and function remain incompletely characterized. Purpose To test whether SARS-CoV-2 infection leads to small airways disease in patients with persistent symptoms. Materials and Methods In this single-center study at a university teaching hospital, adults with confirmed COVID-19 who remained symptomatic more than 30 days following diagnosis were prospectively enrolled from June to December 2020 and compared with healthy participants (controls) prospectively enrolled from March to August 2018. Participants with post-acute sequelae of COVID-19 (PASC) were classified as ambulatory, hospitalized, or having required the intensive care unit (ICU) based on the highest level of care received during acute infection. Symptoms, pulmonary function tests, and chest CT images were collected. Quantitative CT analysis was performed using supervised machine learning to measure regional ground-glass opacity (GGO) and using inspiratory and expiratory image-matching to measure regional air trapping. Univariable analyses and multivariable linear regression were used to compare groups. Results Overall, 100 participants with PASC (median age, 48 years; 66 women) were evaluated and compared with 106 matched healthy controls; 67% (67 of 100) of the participants with PASC were classified as ambulatory, 17% (17 of 100) were hospitalized, and 16% (16 of 100) required the ICU. In the hospitalized and ICU groups, the mean percentage of total lung classified as GGO was 13.2% and 28.7%, respectively, and was higher than that in the ambulatory group (3.7%, P < .001 for both comparisons). The mean percentage of total lung affected by air trapping was 25.4%, 34.6%, and 27.3% in the ambulatory, hospitalized, and ICU groups, respectively, and 7.2% in healthy controls (P < .001). Air trapping correlated with the residual volume-to-total lung capacity ratio (ρ = 0.6, P < .001). Conclusion In survivors of COVID-19, small airways disease occurred independently of initial infection severity. The long-term consequences are unknown. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Elicker in this issue.


Subject(s)
COVID-19/complications , Lung Diseases , COVID-19/diagnostic imaging , Female , Humans , Lung Diseases/diagnostic imaging , Lung Diseases/virology , Male , Middle Aged , Tomography, X-Ray Computed/methods
3.
Cell Rep ; 37(3): 109839, 2021 10 19.
Article in English | MEDLINE | ID: covidwho-1439921

ABSTRACT

MicroRNAs (miRNAs) are small non-coding RNAs involved in post-transcriptional gene regulation that have a major impact on many diseases and provide an exciting avenue toward antiviral therapeutics. From patient transcriptomic data, we determined that a circulating miRNA, miR-2392, is directly involved with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) machinery during host infection. Specifically, we show that miR-2392 is key in driving downstream suppression of mitochondrial gene expression, increasing inflammation, glycolysis, and hypoxia, as well as promoting many symptoms associated with coronavirus disease 2019 (COVID-19) infection. We demonstrate that miR-2392 is present in the blood and urine of patients positive for COVID-19 but is not present in patients negative for COVID-19. These findings indicate the potential for developing a minimally invasive COVID-19 detection method. Lastly, using in vitro human and in vivo hamster models, we design a miRNA-based antiviral therapeutic that targets miR-2392, significantly reduces SARS-CoV-2 viability in hamsters, and may potentially inhibit a COVID-19 disease state in humans.


Subject(s)
COVID-19/genetics , COVID-19/immunology , MicroRNAs/genetics , SARS-CoV-2/genetics , Adult , Aged , Aged, 80 and over , Animals , Antiviral Agents/pharmacology , Biomarkers/metabolism , COVID-19/drug therapy , Cricetinae , Female , Ferrets , Gene Expression Regulation , Glycolysis , Healthy Volunteers , Humans , Hypoxia , Inflammation , Male , Mice , Middle Aged , Proteomics/methods , ROC Curve , Rats
4.
J Appl Physiol (1985) ; 131(2): 454-463, 2021 08 01.
Article in English | MEDLINE | ID: covidwho-1346099

ABSTRACT

This study reports systematic longitudinal pathophysiology of lung parenchymal and vascular effects of asymptomatic COVID-19 pneumonia in a young, healthy never-smoking male. Inspiratory and expiratory noncontrast along with contrast dual-energy computed tomography (DECT) scans of the chest were performed at baseline on the day of acute COVID-19 diagnosis (day 0), and across a 90-day period. Despite normal vital signs and pulmonary function tests on the day of diagnosis, the CT scans and corresponding quantification metrics detected abnormalities in parenchymal expansion based on image registration, ground-glass (GGO) texture (inflammation) as well as DECT-derived pulmonary blood volume (PBV). Follow-up scans on day 30 showed improvement in the lung parenchymal mechanics as well as reduced GGO and improved PBV distribution. Improvements in lung PBV continued until day 90. However, the heterogeneity of parenchymal mechanics and texture-derived GGO increased on days 60 and 90. We highlight that even asymptomatic COVID-19 infection with unremarkable vital signs and pulmonary function tests can have measurable effects on lung parenchymal mechanics and vascular pathophysiology, which may follow apparently different clinical courses. For this asymptomatic subject, post COVID-19 regional mechanics demonstrated persistent increased heterogeneity concomitant with return of elevated GGOs, despite early improvements in vascular derangement.NEW & NOTEWORTHY We characterized the temporal changes of lung parenchyma and microvascular pathophysiology from COVID-19 infection in an asymptomatic young, healthy nonsmoking male using dual-energy CT. Lung parenchymal mechanics and microvascular disease followed different clinical courses. Heterogeneous perfused blood volume became more uniform on follow-up visits up to 90 days. However, post COVID-19 mechanical heterogeneity of the lung parenchyma increased after apparent improvements in vascular abnormalities, even with normal spirometric indices.


Subject(s)
COVID-19 , Pneumonia , COVID-19 Testing , Humans , Lung/diagnostic imaging , Male , Retrospective Studies , SARS-CoV-2 , Tomography, X-Ray Computed
5.
BJR Open ; 3(1): 20200043, 2021.
Article in English | MEDLINE | ID: covidwho-1133651

ABSTRACT

Increasingly, quantitative lung computed tomography (qCT)-derived metrics are providing novel insights into chronic inflammatory lung diseases, including chronic obstructive pulmonary disease, asthma, interstitial lung disease, and more. Metrics related to parenchymal, airway, and vascular anatomy together with various measures associated with lung function including regional parenchymal mechanics, air trapping associated with functional small airways disease, and dual-energy derived measures of perfused blood volume are offering the ability to characterize disease phenotypes associated with the chronic inflammatory pulmonary diseases. With the emergence of COVID-19, together with its widely varying degrees of severity, its rapid progression in some cases, and the potential for lengthy post-COVID-19 morbidity, there is a new role in applying well-established qCT-based metrics. Based on the utility of qCT tools in other lung diseases, previously validated supervised classical machine learning methods, and emerging unsupervised machine learning and deep-learning approaches, we are now able to provide desperately needed insight into the acute and the chronic phases of this inflammatory lung disease. The potential areas in which qCT imaging can be beneficial include improved accuracy of diagnosis, identification of clinically distinct phenotypes, improvement of disease prognosis, stratification of care, and early objective evaluation of intervention response. There is also a potential role for qCT in evaluating an increasing population of post-COVID-19 lung parenchymal changes such as fibrosis. In this work, we discuss the basis of various lung qCT methods, using case-examples to highlight their potential application as a tool for the exploration and characterization of COVID-19, and offer scanning protocols to serve as templates for imaging the lung such that these established qCT analyses have the best chance at yielding the much needed new insights.

6.
Br J Radiol ; 93(1113): 20200538, 2020 Sep 01.
Article in English | MEDLINE | ID: covidwho-696338

ABSTRACT

COVID-19 pneumonia is a newly recognized lung infection. Initially, CT imaging was demonstrated to be one of the most sensitive tests for the detection of infection. Currently, with broader availability of polymerase chain reaction for disease diagnosis, CT is mainly used for the identification of complications and other defined clinical indications in hospitalized patients. Nonetheless, radiologists are interpreting lung imaging in unsuspected patients as well as in suspected patients with imaging obtained to rule out other relevant clinical indications. The knowledge of pathological findings is also crucial for imagers to better interpret various imaging findings. Identification of the imaging findings that are commonly seen with the disease is important to diagnose and suggest confirmatory testing in unsuspected cases. Proper precautionary measures will be important in such unsuspected patients to prevent further spread. In addition to understanding the imaging findings for the diagnosis of the disease, it is important to understand the growing set of tools provided by artificial intelligence. The goal of this review is to highlight common imaging findings using illustrative examples, describe the evolution of disease over time, discuss differences in imaging appearance of adult and pediatric patients and review the available literature on quantitative CT for COVID-19. We briefly address the known pathological findings of the COVID-19 lung disease that may help better understand the imaging appearance, and we provide a demonstration of novel display methodologies and artificial intelligence applications serving to support clinical observations.


Subject(s)
Betacoronavirus , Coronavirus Infections/diagnostic imaging , Coronavirus Infections/pathology , Pneumonia, Viral/diagnostic imaging , Pneumonia, Viral/pathology , Polymerase Chain Reaction/methods , Tomography, X-Ray Computed/methods , COVID-19 , Humans , Lung/diagnostic imaging , Lung/pathology , Pandemics , SARS-CoV-2
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