StatementThis prospective longitudinal study systematically described the temporal changes of CT findings in COVID-19 pneumonia and summarized the CT findings at the time of hospital discharge. Key ResultsThe extent of CT abnormalities progressed rapidly after symptom onset, peaked during illness days 6-11, and followed by persistence of high levels.The predominant pattern of abnormalities after symptom onset was ground-glass opacity; the percentage of mixed pattern peaked during illness days 12-17, and became the second most prevalent pattern thereafter.Sixty-six of the 70 patients (94%) discharged had residual disease on final CT scans, with ground-glass opacity the most common pattern. Abbreviations: COVID-19: corona virus disease 2019 SARS-CoV-2: severe acute respiratory syndrome coronavirus 2 rRT-PCR: real-time reverse transcriptase-polymerase chain reaction I n P r e s s Abstract Background: CT may play a central role in the diagnosis and management of COVID-19 pneumonia.Purpose: To perform a longitudinal study to analyze the serial CT findings over time in patients with COVID-19 pneumonia. Materials and Methods:During January 16 to February 17, 2020, 90 patients (male:female, 33:57; mean age, 45 years) with COVID-19 pneumonia were prospectively enrolled and followed up until they were discharged or died, or until the end of the study. A total of 366 CT scans were acquired and reviewed by 2 groups of radiologists for the patterns and distribution of lung abnormalities, total CT scores and number of zones involved. Those features were analyzed for temporal change.Results: CT scores and number of zones involved progressed rapidly, peaked during illness days 6-11 (median: 5 and 5), and followed by persistence of high levels. The predominant pattern of abnormalities after symptom onset was ground-glass opacity (35/78 [45%] to 49/79 [62%] in different periods). The percentage of mixed pattern peaked (30/78 [38%]) on illness days 12-17, and became the second most predominant pattern thereafter. Pure ground-glass opacity was the most prevalent sub-type of groundglass opacity after symptom onset (20/50 [40%] to 20/28 [71%]). The percentage of ground-glass opacity with irregular linear opacity peaked on illness days 6-11 (14/50 [28%)]) and became the second most prevalent subtype thereafter. The distribution of lesions was predominantly bilateral and subpleural. 66/70 (94%) patients discharged had residual disease on final CT scans (median CT scores and zones involved: 4 and 4), with ground-glass opacity (42/70 [60%]) and pure ground-glass opacity (31/42 [74%]) the most common pattern and subtype. Conclusion:The extent of lung abnormalities on CT peaked during illness days 6-11. The temporal changes of the diverse CT manifestations followed a specific pattern, which might indicate the progression and recovery of the illness.

RuO2 nanocrystals are successfully impregnated into the surface carbon layer of the Li3V2(PO4)3/C cathode material by the precipitation method. Transmission electron microscopy shows that the RuO2 particles uniformly embed in the surface carbon layer. Cyclic voltammetry and electrochemical impedance spectroscopy indicate that the coexistence of carbon and RuO2 enables high conductivity for both Li ions and electrons and thus stabilizes the interfacial properties of the electrode, facilitates the charge transfer reactions, and improves the Li(+) diffusion in the electrode. As a result, the Li3V2(PO4)3 cathode coated with the binary surface layer shows improved rate capability and cycle stability. Particularly, the material containing 2.4 wt % Ru exhibits the best electrochemical performance and delivers a discharge capacity of 106 mAh g(-1) at the 10 C rate with a capacity retention of 98.4% after 100 cycles.

Background Drug repositioning, the strategy of unveiling novel targets of existing drugs could reduce costs and accelerate the pace of drug development. To elucidate the novel molecular mechanism of known drugs, considering the long time and high cost of experimental determination, the efficient and feasible computational methods to predict the potential associations between drugs and targets are of great aid. Methods A novel calculation model for drug-target interaction (DTI) prediction based on network representation learning and convolutional neural networks, called DLDTI, was generated. The proposed approach simultaneously fused the topology of complex networks and diverse information from heterogeneous data sources, and coped with the noisy, incomplete, and high-dimensional nature of large-scale biological data by learning the low-dimensional and rich depth features of drugs and proteins. The low-dimensional feature vectors were used to train DLDTI to obtain the optimal mapping space and to infer new DTIs by ranking candidates according to their proximity to the optimal mapping space. More specifically, based on the results from the DLDTI, we experimentally validated the predicted targets of tetramethylpyrazine (TMPZ) on atherosclerosis progression in vivo. Results The experimental results showed that the DLDTI model achieved promising performance under fivefold cross-validations with AUC values of 0.9172, which was higher than the methods using different classifiers or different feature combination methods mentioned in this paper. For the validation study of TMPZ on atherosclerosis, a total of 288 targets were identified and 190 of them were involved in platelet activation. The pathway analysis indicated signaling pathways, namely PI3K/Akt, cAMP and calcium pathways might be the potential targets. Effects and molecular mechanism of TMPZ on atherosclerosis were experimentally confirmed in animal models. Conclusions DLDTI model can serve as a useful tool to provide promising DTI candidates for experimental validation. Based on the predicted results of DLDTI model, we found TMPZ could attenuate atherosclerosis by inhibiting signal transductions in platelets. The source code and datasets explored in this work are available at https://github.com/CUMTzackGit/DLDTI.

Li-excess cathode material, Li 1.13 Ni 0.3 Mn 0.57 O 2 , was synthesized by the sol−gel method. The material has a reversible discharge capacity of 200 mAh g −1 at a current density of 40 mA g −1 . In situ synchrotron X-ray diffraction, electrochemical impedance spectroscopy (EIS), and the galvanostatic intermittent titration technique (GITT) were applied to study the relationships between structural changes and electrochemical kinetics of Li 1.13 Ni 0.3 Mn 0.57 O 2 during the first charge. When the charging potential was below 4.4 V, the c/a structural parameter of the material gradually increased, resulting in a higher layered character. The lithium diffusion coefficients during this process were about 10 −14 cm 2 s −1 . When the charging potential was increased to 4.8 V, the bulk of the material was still maintained in a layered structure with space group symmetry R3̅ m. The lithium diffusion coefficient and the charge transfer kinetics rapidly decreased because of the high kinetic barriers associated with concurrent Li + extraction, oxygen loss, and structural rearrangement. Both the lithium diffusion coefficient and the charge transfer kinetics show further decrease at the end of the first charge, indicating severely sluggish kinetics of the "Li-poor" Li 1.13−x Ni 0.3 Mn 0.57 O 2 phase.

In this work, a versatile platform for the highly efficient preparation of graphene quantum dots (GQDs) with diverse properties was developed. First of all, an excess amount of oxidants and an additional high temperature step of the Hummers' method for the synthesis of graphene oxide (GO) was applied to obtain nanosized graphene oxide (NGO). Then, high quality GQDs (quantum yields up to 18.2%) with different photoluminescence emission wavelengths, adjustable hydrophilicity-hydrophobicity, and selective cell organelle imaging capacity can be facilely achieved through a one-pot hydrothermal reaction between the NGO and ammonia, fatty primary amines, or amino-substituted organelle targetable compounds, respectively. The superior features of the as-developed method are extremely high conversion ratio (ca. 60 wt% from graphite to the functional GQDs) and great expandability. Such a high conversion ratio is deemed to be due to effectively decreasing aggregation of the NGO (in comparison with GO) during the post-treatment process. This work provides a robust strategy for the highly efficient preparation of GQDs with diverse properties and functions, and is believed to be beneficial for boosting their applications in the future.

In the era of Internet, recognizing pornographic images is of great significance for protecting children's physical and mental health. However, this task is very challenging as the key pornographic contents (e.g., breast and private part) in an image often lie in local regions of small size. In this paper, we model each image as a bag of regions, and follow a multiple instance learning (MIL) approach to train a generic region-based recognition model. Specifically, we take into account the regions' degree of pornography, and make three main contributions. First, we show that based on very few annotations of the key pornographic contents in a training image, we can generate a bag of properly sized regions, among which the potential positive regions usually contain useful contexts that can aid recognition. Second, we present a simple quantitative measure of a region's degree of pornography, which can be used to weigh the importance of different regions in a positive image. Third, we formulate the recognition task as a weighted MIL problem under the convolutional neural network framework, with a bag probability function introduced to combine the importance of different regions. Experiments on our newly collected large scale dataset demonstrate the effectiveness of the proposed method, achieving an accuracy with 97.52% true positive rate at 1% false positive rate, tested on 100K pornographic images and 100K normal images.

Background Glioblastomas have a high degree of malignancy, high recurrence rate, high mortality rate, and low cure rate. Searching for new markers of glioblastomas is of great significance for improving the diagnosis, prognosis and treatment of glioma. Methods Using the GEO public database, we combined 34 glioma microarray datasets containing 1893 glioma samples and conducted genetic data mining through statistical analysis, bioclustering, and pathway analysis. The results were validated in TCGA, CGGA, and internal cohorts. We further selected a gene for subsequent experiments and conducted cell proliferation and cell cycle analyses to verify the biological function of this gene. Results Eight glioblastoma-specific differentially expressed genes were screened using GEO. In the TCGA and CGGA cohorts, patients with high CBX3 , BARD1 , EGFR , or IFRD1 expression had significantly shorter survival but patients with high GUCY1A3 or MOBP expression had significantly longer survival than patients with lower expression of these genes. After reviewing the literature, we selected the CBX3 gene for further experiments. We confirmed that CBX3 was overexpressed in glioblastoma by immunohistochemical analysis of tissue microarrays and qPCR analysis of surgical specimens. The functional assay results showed that silencing CBX3 arrests the cell cycle in the G2/M phase, thereby weakening the cell proliferation ability. Conclusions We used a multidisciplinary approach to analyze glioblastoma samples in 34 microarray datasets, revealing novel diagnostic and prognostic biomarkers in patients with glioblastoma and providing a new direction for screening tumor markers. Electronic supplementary material The online version of this article (10.1186/s12967-019-1930-3) contains supplementary material, which is available to authorized users.

The aim of this study was to identify the relationships of epidermal growth factor receptor (EGFR) mutations and anaplastic large-cell lymphoma kinase (ALK) status with CT characteristics in adenocarcinoma using the largest patient cohort to date. In this study, preoperative chest CT findings prior to treatment were retrospectively evaluated in 827 surgically resected lung adenocarcinomas. All patients were tested for EGFR mutations and ALK status. EGFR mutations were found in 489 (59.1%) patients, and ALK positivity was found in 57 (7.0%). By logistic regression, the most significant independent prognostic factors of EGFR effective mutations were female sex, nonsmoker status, GGO air bronchograms and pleural retraction. For EGFR mutation prediction, receiver operating characteristic (ROC) curves yielded areas under the curve (AUCs) of 0.682 and 0.758 for clinical only or combined CT features, respectively, with a significant difference (p < 0.001). Furthermore, the exon 21 mutation rate in GGO was significantly higher than the exon 19 mutation rate(p = 0.029). The most significant independent prognostic factors of ALK positivity were age, solid-predominant-subtype tumours, mucinous lung adenocarcinoma, solid tumours and no air bronchograms on CT. ROC curve analysis showed that for predicting ALK positivity, the use of clinical variables combined with CT features (AUC = 0.739) was superior to the use of clinical variables alone (AUC = 0.657), with a significant difference (p = 0.0082). The use of CT features for patients may allow analyses of tumours and more accurately predict patient populations who will benefit from therapies targeting treatment.