Aberrant Brain Entropy in Posttraumatic Stress disorder Comorbid with Major Depressive Disorder during the Coronavirus Disease 2019 Pandemic (preprint)

Aim Previously, neuroimaging studies on comorbid Posttraumatic-Major depression disorder (PTSD-MDD) comorbidity found abnormalities in multiple brain regions among patients. Recent neuroimaging studies have revealed dynamic nature on human brain activity during resting state, and entropy as an indicator of dynamic regularity may provide a new perspective for studying abnormalities of brain function among PTSD-MDD patients. During the COVID-19 pandemic, there has been a significant increase in the number of patients with PTSD-MDD. We have decided to conduct research on resting-state brain functional activity of patients who developed PTSD-MDD during this period using entropy.Methods 33 patients with PTSD-MDD and 36 matched TCs were recruited. PTSD and depression symptoms were assessed using multiple clinical scales. All subjects underwent functional magnetic resonance imaging (fMRI) scans. And the brain entropy (BEN) maps were calculated using the BEN mapping toolbox. A two-sample t-test was used to compare the differences in the brain entropy between the PTSD-MDD comorbidity group and TC group. Furthermore, correlation analysis was conducted between the BEN changes in patients with PTSD-MDD and clinical scales.Results Compared to the TCs, PTSD-MDD patients had a reduced BEN in the right middle frontal orbital gyrus (R_MFOG), left putamen, and right inferior frontal gyrus, opercular part (R_IFOG). Furthermore, a higher BEN in the R_MFOG was related to higher CAPS and HAMD-24 scores in the patients with PTSD-MDD.Conclusion The results showed that the R_MFOG is a potential marker for showing the symptom severity of PTSD-MDD comorbidity. Consequently, PTSD-MDD may have reduced BEN in frontal and basal ganglia regions which are related to emotional dysregulation and cognitive deficits.

CT-Based Quantitative Assessment of Coronavirus Disease 2019 Using a Deep Learning-Based Segmentation System: A Longitudinal Study (preprint)

BackgroundCoronavirus disease 2019 (COVID-19) is a global catastrophic disease that has severely affected more than 185 countries. The key steps in fighting against COVID-19 involve early detection and tracking of the treatment effects. A large number of studies highlighted computed tomography (CT) as a reliable method for early diagnosis and follow-up monitoring of the disease. However, there are limited data on quantitative analysis of the follow-up images. In this study, we used a deep learning model using a neural network with high accuracy in automatic segmentation and quantification to analyze the infected lesions on chest CT images.MethodsWe used a deep learning model using a neural network with high accuracy in automatic segmentation and quantification to analyze the infected lesions on chest CT images. A total of 14 patients (mean age, 53±14 years; age range, 23–74 years; 42.9% men and 57.1% women) with confirmed mild-type COVID-19 from January 1 to May 7, 2020, were retrospectively reviewed. Initial and follow-up original CT images were collected, and CT quantitative parameters, including percentage of infection (POI) and density variation of pneumonia, were determined.ResultsThe median initial POI was 3.4% (interquartile range, IQR 0.5%–8.4%) for the whole lung, 0.8% (IQR 0.2%–6.7%) for the left lung, and 5.8% (IQR 0.5%–9.7%) for the right lung. The infection was more serious in the right than in the left lung. The infected region mainly involved bilateral lower lobes, more pronounced on the right side. Quantitative CT showed that POI significantly decreased throughout the follow-up period in all 14 patients (p < 0.001). Among them, 50% of the patients had a more significant decrease in POI (51.3%) after a negative nucleic acid test. Moreover, there was a significant decrease in the CT number range of ground-glass opacities (GGO) and consolidation (p < 0.001).ConclusionsThis study demonstrated the quantitative analysis of follow-up CT scans plays an important role in the monitoring of COVID-19 treatment, which could help in treatment planning and standardizing the assessment for discharge.

Correlation between CT findings and outcomes in 46 Patients with Coronavirus Disease-19 (preprint)

Purpose: The aim of this study was to retrospectively analyze chest Computed Tomography (CT) findings in COVID-19 pneumonia and identify features associated with poor prognosis. Methods: This retrospective review included 46 patients with RT-PCR confirmed COVID-19 infection. Basic clinical characteristics and detailed CT features were evaluated and compared between patients who recovered (n = 40) from coronavirus and those who expired (n = 6). Chest CT examinations for ground-glass opacity, crazy-paving pattern, consolidation, and fibrosis were scored by two reviewers. The total CT score comprised the sum of lung involvement (5 lobes, scores 1-5 for each lobe, range; 0, none; 25, maximum) was determined. Results: We analyzed clinical data from 46 patients (26 males and 20 females; age 9-82 years) with confirmed COVID-19 pneumonia were evaluated. The chest CTs showed 27 (58.7%) patients had ground-glass opacity, 19 (41.3%) had ground glass and consolidation, and 35 (76.1%) patients had crazy-paving pattern. None of the patients who expired had fibrosis, in contrast to six (15%) patients who recovered from coronavirus. Most patients had subpleural lesions (89.0%), bilateral (87.0%) and lower (93.0%) lung lobe involvement. Diffuse lesions were present in four (67%) patients who succumbed to coronavirus, but only one (2.5%) patient who recovered (p = 0.000). CT identified a greater area of lung lobe involvement in patients who died (p = 0.000). In the group of patients who expired, the total CT score was higher than that of the recovery group (17.2 ± 7.8 vs. 7.1 ± 4.3, p = 0.005). Patients in the death group had lower lymphocyte count and higher C-reactive protein than those in the recovery group (p = 0.011 and p = 0.041, respectively). Conclusion: The CT of patients with COVID-19 mainly showed ground-glass opacity and ground-glass opacity plus consolidation, with a peripheral lower lobe preference. Early fibrosis may correlate with well prognosis. Lymphopenia, elevated C-reactive protein, and high CT score in conjunction with diffuse distribution of lung lesions are indicative of disease severity and short- term mortality.