Network Meta-analysis on the Changes of Amyloid Precursor Protein Expression Following SARS-CoV-2 Infection.

SARS-CoV-2 infection begins with the attachment of its spike (S) protein to angiotensin-converting enzyme-2 (ACE2) followed by complex host immune responses with cardiovascular and neurological implications. Our meta-analyses used QIAGEN Ingenuity Pathway Analysis (IPA) and Knowledge Base (QKB) to investigate how the expression of amyloid precursor protein (APP) was modulated by attachment of SARS-CoV-2 S protein in the brain microvascular endothelial cells (BMVECs) and during COVID-19 in progress. Published 80 host response genes reported to be modulated in BMVECs following SARS-CoV-2 S protein binding were used to identify key canonical pathways and intermediate molecules mediating the regulation of APP production following the attachment of S protein to endothelial cells. This revealed that the attachment of SARS-CoV-2 S protein may inhibit APP expression in the BMVECs. Our results shed light on the molecular mechanisms by which SARS-CoV-2 infection may potentiate the incidence of stroke by inhibiting the production of APP in the BMVECs. We also analyzed molecules associated with COVID-19, which revealed six upstream regulators, TNF, IFNG, STAT1, IL1ß, IL6, and STAT3. The upstream regulators mediate the increased production of APP via intermediators, with eleven regulated by all six upstream regulators. These COVID-19 upstream regulators increased APP expression with a statistically significant Z-score of 3.705 (p value = 0.000211). These findings have revealed molecular mechanisms by which COVID-19 disease may lead to long-term neurological manifestations resulting from the elevated APP expression in line with immune response in the host. Altogether, our study revealed two distinct scenarios which may have differential impact on APP expression.

Molecular Mechanisms of Palmitic Acid Augmentation in COVID-19 Pathologies.

The coronavirus disease 2019 (COVID-19) pandemic has claimed over 2.7 million lives globally. Obesity has been associated with increased severity and mortality of COVID-19. However, the molecular mechanisms by which obesity exacerbates COVID-19 pathologies are not well-defined. The levels of free fatty acids (FFAs) are elevated in obese subjects. This study was therefore designed to examine how excess levels of different FFAs may affect the progression of COVID-19. Biological molecules associated with palmitic acid (PA) and COVID-19 were retrieved from QIAGEN Knowledge Base, and Ingenuity Pathway Analysis tools were used to analyze these datasets and explore the potential pathways affected by different FFAs. Our study found that one of the top 10 canonical pathways affected by PA was the coronavirus pathogenesis pathway, mediated by key inflammatory mediators, including PTGS2; cytokines, including IL1ß and IL6; chemokines, including CCL2 and CCL5; transcription factors, including NFκB; translation regulators, including EEF1A1; and apoptotic mediators, including BAX. In contrast, n-3 fatty acids may attenuate PA's activation of the coronavirus pathogenesis pathway by inhibiting the activity of such mediators as IL1ß, CCL2, PTGS2, and BAX. Furthermore, PA may modulate the expression of ACE2, the main cell surface receptor for the SARS-CoV-2 spike protein.

Network Meta-Analysis on the Mechanisms Underlying Alcohol Augmentation of COVID-19 Pathologies.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic is a worldwide crisis caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Many COVID-19 patients present with fever in the early phase, with some progressing to a hyperinflammatory phase. Ethanol (EtOH) exposure may lead to systemic inflammation. Network meta-analysis was conducted to examine possible relationships between EtOH consumption and COVID-19 pathologies. METHODS: Molecules affected by EtOH exposure were identified by analysis with QIAGEN Knowledge Base. Molecules affected by COVID-19 were identified from studies in MEDLINE, bioRxiv, and medRxiv reporting gene expression profiles in COVID-19 patients, QIAGEN Coronavirus Network Explorer, and analysis of the RNA-sequencing data of autopsied lungs of COVID-19 patients retrieved from the GEO database. Network meta-analysis was then conducted on these molecules using QIAGEN Ingenuity Pathway Analysis (IPA). RESULTS: Twenty-eight studies reporting significant gene expression changes in COVID-19 patients were identified. One RNA-sequencing dataset on autopsied lungs of COVID-19 patients was retrieved from GEO. Our network meta-analysis suggests that EtOH exposure may augment the effects of SARS-CoV-2 infection on hepatic fibrosis signaling pathway, cellular metabolism and homeostasis, inflammation, and neuroinflammation. EtOH may also enhance the activity of key mediators including cytokines, such as IL-1ß, IL-6, and TNF, and transcription factors, such as JUN and STAT, while inhibiting the activity of anti-inflammatory mediators including glucocorticoid receptor. Furthermore, IL-1ß, IL-6, TNF, JUN, and STAT were mapped to 10 pathways predicted to associate with SARS-CoV-2 proteins, including HMGB1, IL-1, and IL-6 signaling pathways. CONCLUSIONS: Our meta-analyses demonstrate that EtOH exposure may augment SARS-CoV-2-induced inflammation by altering the activity of key inflammatory mediators. Our findings suggest that it is important for clinicians to caution patients about the risk of alcohol consumption, which has increased during the COVID-19 pandemic. The findings also call for further investigation into how alcohol exposure affects viral infections.

A Pattern Categorization of CT Findings to Predict Outcome of COVID-19 Pneumonia.

Background: As global healthcare system is overwhelmed by novel coronavirus disease (COVID-19), early identification of risks of adverse outcomes becomes the key to optimize management and improve survival. This study aimed to provide a CT-based pattern categorization to predict outcome of COVID-19 pneumonia. Methods: One hundred and sixty-five patients with COVID-19 (91 men, 4-89 years) underwent chest CT were retrospectively enrolled. CT findings were categorized as Pattern 0 (negative), Pattern 1 (bronchopneumonia pattern), Pattern 2 (organizing pneumonia pattern), Pattern 3 (progressive organizing pneumonia pattern), and Pattern 4 (diffuse alveolar damage pattern). Clinical findings were compared across different categories. Time-dependent progression of CT patterns and correlations with clinical outcomes, i.e." discharge or adverse outcome (admission to ICU, requiring mechanical ventilation, or death), with pulmonary sequelae (complete absorption or residuals) on CT after discharge were analyzed. Results: Of 94 patients with outcome, 81 (86.2%) were discharged, 3 (3.2%) were admitted to ICU, 4 (4.3%) required mechanical ventilation, 6 (6.4%) died. 31 (38.3%) had complete absorption at median day 37 after symptom onset. Significant differences between pattern-categories were found in age, disease severity, comorbidity and laboratory results (all P < 0.05). Remarkable evolution was observed in Pattern 0-2 and Pattern 3-4 within 3 and 2 weeks after symptom-onset, respectively; most of patterns remained thereafter. After controlling for age, CT pattern significantly correlated with adverse outcomes [Pattern 4 vs. Pattern 0-3 [reference]; hazard-ratio [95% CI], 18.90 [1.91-186.60], P = 0.012]. CT pattern [Pattern 3-4 vs. Pattern 0-2 [reference]; 0.26 [0.08-0.88], P = 0.030] and C-reactive protein [>10 vs. ≤ 10 mg/L [reference]; 0.31 [0.13-0.72], P = 0.006] were risk factors associated with pulmonary residuals. Conclusion: CT pattern categorization allied with clinical characteristics within 2 weeks after symptom onset would facilitate early prognostic stratification in COVID-19 pneumonia.

Evaluation of dynamic lung changes during coronavirus disease 2019 (COVID-19) by quantitative computed tomography.

OBJECTIVES: This study aims to trace the dynamic lung changes of coronavirus disease 2019 (COVID-19) using computed tomography (CT) images by a quantitative method. METHODS: In this retrospective study, 28 confirmed COVID-19 cases with 145 CT scans are collected. The lesions are detected automatically and the parameters including lesion volume (LeV/mL), lesion percentage to lung volume (LeV%), mean lesion density (MLeD/HU), low attenuation area lower than - 400HU (LAA-400%), and lesion weight (LM/mL*HU) are computed for quantification. The dynamic changes of lungs are traced from the day of initial symptoms to the day of discharge. The lesion distribution among the five lobes and the dynamic changes in each lobe are also analyzed. RESULTS: LeV%, MLeD, and LM reach peaks on days 9, 6 and 8, followed by a decrease trend in the next two weeks. LAA-400% (mostly the ground glass opacity) declines to the lowest on days 4-5, and then increases. The lesion is mostly seen in the bilateral lower lobes, followed by the left upper lobe, right upper lobe and right middle lobe (p < 0.05). The right middle lobe is the earliest one (on days 6-7), while the right lower lobe is the latest one (on days 9-10) that reaches to peak among the five lobes. CONCLUSIONS: Severity of COVID-19 increases from the day of initial symptoms, reaches to the peak around on day 8, and then decreases. Lesion is more commonly seen in the bilateral lower lobes.

Evolution of CT findings in patients with mild COVID-19 pneumonia.

OBJECTIVES: To delineate the evolution of CT findings in patients with mild COVID-19 pneumonia. METHODS: CT images and medical records of 88 patients with confirmed mild COVID-19 pneumonia, a baseline CT, and at least one follow-up CT were retrospectively reviewed. CT features including lobar distribution and presence of ground glass opacities (GGO), consolidation, and linear opacities were analyzed on per-patient basis during each of five time intervals spanning the 3 weeks after disease onset. Total severity scores were calculated. RESULTS: Of patients, 85.2% had travel history to Wuhan or known contact with infected individuals. The most common symptoms were fever (84.1%) and cough (56.8%). The baseline CT was obtained on average 5 days from symptom onset. Four patients (4.5%) had negative initial CT. Significant differences were found among the time intervals in the proportion of pulmonary lesions that are (1) pure GGO, (2) mixed attenuation, (3) mixed attenuation with linear opacities, (4) consolidation with linear opacities, and (5) pure consolidation. The majority of patients had involvement of ≥ 3 lobes. Bilateral involvement was more prevalent than unilateral involvement. The proportions of patients observed to have pure GGO or GGO and consolidation decreased over time while the proportion of patients with GGO and linear opacities increased. Total severity score showed an increasing trend in the first 2 weeks. CONCLUSIONS: While bilateral GGO are predominant features, CT findings changed during different time intervals in the 3 weeks after symptom onset in patients with COVID-19. KEY POINTS: • Four of 88 (4.5%) patients with COVID-19 had negative initial CT. • Majority of COVID-19 patients had abnormal CT findings in ≥ 3 lobes. • A proportion of patients with pure ground glass opacities decreased over the 3 weeks after symptom onset.

Quantitative computed tomography analysis for stratifying the severity of Coronavirus Disease 2019.

To examine the feasibility of using a computer tool for stratifying the severity of Coronavirus Disease 2019 (COVID-19) based on computed tomography (CT) images. We retrospectively examined 44 confirmed COVID-19 cases. All cases were evaluated separately by radiologists (visually) and through an in-house computer software. The degree of lesions was visually scored by the radiologist, as follows, for each of the 5 lung lobes: 0, no lesion present; 1, <1/3 involvement; 2, >1/3 and < 2/3 involvement; and 3, >2/3 involvement. Lesion density was assessed based on the proportion of ground-glass opacity (GGO), consolidation and fibrosis of the lesions. The parameters obtained using the computer tool included lung volume (mL), lesion volume (mL), lesion percentage (%), and mean lesion density (HU) of the whole lung, right lung, left lung, and each lobe. The scores obtained by the radiologists and quantitative results generated by the computer software were tested for correlation. A Chi-square test was used to test the consistency of radiologist- and computer-derived lesion percentage in the right/left lung, upper/lower lobe, and each of the 5 lobes. The results showed a strong to moderate correlation between lesion percentage scores obtained by radiologists and the computer software (r ranged from 0.7679 to 0.8373, P < 0.05), and a moderate correlation between the proportion of GGO and mean lesion density (r = -0.5894, P < 0.05), and proportion of consolidation and mean lesion density (r = 0.6282, P < 0.05). Computer-aided quantification showed a statistical significant higher lesion percentage for lower lobes than that assessed by the radiologists (χ2 = 8.160, P = 0.004). Our experiments demonstrated that the computer tool could reliably and accurately assess the severity and distribution of pneumonia on CT scans.