Background: Computed tomography (CT) is the preferred imaging method for diagnosing 2019 novel coronavirus (COVID19) pneumonia. Our research aimed to construct a system based on deep learning for detecting COVID-19 pneumonia on high resolution CT, relieve working pressure of radiologists and contribute to the control of the epidemic. Methods: For model development and validation, 46,096 anonymous images from 106 admitted patients, including 51 patients of laboratory confirmed COVID-19 pneumonia and 55 control patients of other diseases in Renmin Hospital of Wuhan University (Wuhan, Hubei province, China) were retrospectively collected and processed. Twenty-seven consecutive patients undergoing CT scans in Feb, 5, 2020 in Renmin Hospital of Wuhan University were prospectively collected to evaluate and compare the efficiency of radiologists against 2019-CoV pneumonia with that of the model. Findings: The model achieved a per-patient sensitivity of 100%, specificity of 93.55%, accuracy of 95.24%, PPV of 84.62%, and NPV of 100%; a per-image sensitivity of 94.34%, specificity of 99.16%, accuracy of 98.85%, PPV of 88.37%, and NPV of 99.61% in retrospective dataset. For 27 prospective patients, the model achieved a comparable performance to that of expert radiologist. With the assistance of the model, the reading time of radiologists was greatly decreased by 65%. Conclusion: The deep learning model showed a comparable performance with expert radiologist, and greatly improve the efficiency of radiologists in clinical practice. It holds great potential to relieve the pressure of frontline radiologists, improve early diagnosis, isolation and treatment, and thus contribute to the control of the epidemic.

Background: Recent studies have reported altered efficiency in selective brain regions and functional networks in patients with alcohol use disorder (AUD). Inefficient processing can reflect or arise from the disorganization of information being conveyed from place to place. However, it remains unknown whether the efficiency and functional connectivity are altered in large-scale topological organization of patients with AUD. Methods: Resting-state functional magnetic resonance imaging (rsfMRI) data were experimentally collected from 21 right-handed males with AUD and 21 right-handed, age-, gender- and education-matched healthy controls (HCs). Graph theory was used to investigate inter-group differences in the topological parameters (global and nodal) of networks and inter-regional functional connectivity. Correlations between group differences in network properties and clinical variables were also investigated in the AUD group. Results: The brain networks of the AUD group showed decreased global efficiency when compared with the HC group. Besides, increased nodal efficiency was found in the left orbitofrontal cortex (OFC), while reduced nodal efficiency was observed in the right OFC, right fusiform gyrus (FFG), right superior temporal gyrus, right inferior occipital gyrus (IOG), and left insula. Moreover, hypo-connectivity was detected between the right dorsolateral prefrontal cortex (DLPFC) and right superior occipital gyrus (SOG) in the AUD group when compared with the HC group. The nodal efficiency of the left OFC was associated with cognitive performance in the AUD group. Conclusions: AUD patients exhibited alterations in brain network efficiency and functional connectivity, particularly in regions linked to multi-sensory modalities. These disrupted topological properties may help to obtain a more comprehensive understanding of large-scale brain network activity. Furthermore, these data provide a potential neural mechanism of impaired cognition in individuals with AUD.

Background: Alcohol use disorder (AUD) has a negative impact on one’s health and wastes a lot of societal resources since it damages one’s brain tissue. Yet the knowledge of the neural mechanisms underlying alcohol addiction still remains limited. This study aims to investigate the neural mechanisms underlying alcohol addiction by using voxel-wise binarized degree centrality (DC), weighted DC and functional connectivity (FC) methods to analyze brain network activity in individuals with AUD. Methods: Thirty-three AUD patients and 29 healthy controls (HC) participated in this study. Binarized and weighted DC approach coupled with a second seed-based FC algorithm was used to assess the abnormal intrinsic hub features in AUD. We also examined the correlation between changes in functional network nodes and the severity of alcohol dependence. Results: Thirty AUD patients and 26 HC were retained after head motion correction. The spatial distribution maps of the binarized DC and weighted DC for the AUD and HC groups were roughly similar. In comparison to HC, the AUD group had decreased binarized DC and decreased weighted DC in the left precentral gyrus (PreCG) and the left inferior parietal lobule (IPL). Significantly different brain regions in the DC analysis were defined as seed points in the FC analysis. Compared with HC, changes in FC within the right inferior temporal gyrus (ITG), right middle temporal gyrus (MTG), left dorsolateral superior frontal gyrus (SFGdor), bilateral IPL, left precuneus (PCUN), left lingual gyrus (LING), right cerebellum_crus1/ITG/inferior occipital gyrus (IOG) and right superior parietal gyrus (SPG) were observed. The correlation analysis revealed that FC of right MTG-right PreCG was negatively correlated with MAST scores, and FC of right IPL-left IPL was positively correlated with ADS scores. Conclusions: Alcohol use disorder is associated with aberrant regional activities in multiple brain areas. Binarized DC, weighted DC and FC analyses may be useful biological indicators for the detection of regional brain activities in patients with AUD. Intergroup differences in FC have also been observed in AUD patients, and these variations were connected to the severity of the symptoms. The AUD patients with lower FC value of the right IPL - left IPL has a lighter dependence on alcohol. This difference in symptom severity may be a compensation for cognitive impairment, indicating a difference in pathological pathways. Future AUD research will now have a fresh path thanks to these discoveries.

Background: Freezing of gait (FOG) in multiple system atrophy (MSA) is characterized by a higher risk of falls and a reduced quality of life; however, the mechanisms underlying these effects have yet to be identified by neuroimaging. The aim of this study was to investigate the differences in functional network when compared between MSA patients with and without freezing.Methods: Degree centrality (DC) based on the resting-state functional magnetic resonance imaging was computed in 65 patients with MSA and 36 healthy controls. Brain regions with statistically different DC values between groups were selected as seed points for a second seed-based functional connectivity (FC) analysis. The relationships between brain activity (DC and FC alterations) and the severity of freezing symptoms were then investigated in the two groups of patients with MSA.Results: Compared to MSA patients without FOG symptoms (MSA-nFOG), patients with MSA-FOG showed an increased DC in the left middle temporal gyrus but a reduced DC in the right superior pole temporal gyrus, left anterior cingulum cortex, left thalamus, and right middle frontal gyrus. Furthermore, in patients with MSA-FOG, the DC in the left thalamus was negatively correlated with FOG scores. Using the left thalamus as a seed, secondary seed-based functional connectivity analysis revealed that patients with MSA-FOG commonly showed the left thalamus-based FC abnormalities in regions related to cognition and emotion. In contrast to the patients with MSA-nFOG, patients with MSA-FOG showed an increased FC between the left thalamus and the left middle temporal gyrus (MTG), right inferior parietal lobule (IPL), bilateral cerebellum_8, and left precuneus.Conclusion: Freezing of gait is associated with centrality of the impaired thalamus network. Abnormal FC between the thalamus and left MTG, right IPL, bilateral cerebellum_8, and left precuneus was involved in FOG. These results provide new insight into the pathophysiological mechanism of FOG in MSA.

On the basis of Covid-19-induced pulmonary pathological and vascular changes, we hypothesized that the anti-VEGF drug bevacizumab might be beneficial for treating Covid-19 patients. We recruited 26 patients from 2-centers (China and Italy) with confirmed severe Covid-19, with respiratory rate ≥ 30 times/min, oxygen saturation ≤ 93% with ambient air, or partial arterial oxygen pressure to fraction of inspiration O ₂ ratio (PaO ₂ /FiO ₂ ) >100mmHg and ≤ 300 mmHg, and diffuse pneumonia confirmed by chest radiological imaging. This trial was conducted from Feb 15 to April 5, 2020, and followed up for 28 days. Relative to comparable control patients with severe Covid-19 admitted in the same centers, bevacizumab showed clinical efficacy by improving oxygenation and shortening oxygen-support duration. Among 26 hospitalized patients with severe Covid-19 (median age, 62 years, 20 [77%] males), bevacizumab plus standard care markedly improved the PaO ₂ /FiO ₂ ratios at days 1 and 7 (elevated values, day 1, 50.5 [4.0,119.0], p <0.001; day 7, 111.0 [85.0,165.0], p <0.001). By day 28, 24 (92%) patients showed improvement in oxygen-support status, 17 (65%) patients were discharged, and none showed worsen oxygen-support status nor died. Significant reduction of lesion areas and ratios were shown in chest CT or X-ray analysis within 7 days. Of 14 patients with fever, body temperature normalized within 72 hours in 13 (93%) patients. Lymphocyte counts in peripheral blood were significantly increased and CRP levels were markedly decreased as shown in available data. Our findings suggested bevacizumab plus standard care was highly beneficial for treating patients with severe Covid-19. Clinical efficacy of bevacizumab warrants double blind, randomized, placebo-controlled trials. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04275414 , URL: https://clinicaltrials.gov/ct2/show/NCT04275414 .