Activity

Predicting cell locations is important since with the understanding of cell locations, we may estimate the function of cells and their integration with the spatial environment. Thus, the DREAM challenge on single-cell transcriptomics required participants to predict the locations of single cells in the Drosophila embryo using single-cell transcriptomic data.

We have developed over 50 pipelines by combining different ways of preprocessing the RNA-seq data, selecting the genes, predicting the cell locations and validating predicted cell locations, resulting in the winning methods which were ranked second in sub-challenge 1, first in sub-challenge 2 and third in sub-challenge 3. In this paper, we present an R package, SCTCwhatateam, which includes all the methods we developed and the Shiny web application to facilitate the research on single-cell spatial reconstruction. All the data and the example use cases are available in the Supplementary data.

We have developed over 50 pipelines by combining different ways of preprocessing the RNA-seq data, selecting the genes, predicting the cell locations and validating predicted cell locations, resulting in the winning methods which were ranked second in sub-challenge 1, first in sub-challenge 2 and third in sub-challenge 3. In this paper, we present an R package, SCTCwhatateam, which includes all the methods we developed and the Shiny web application to facilitate the research on single-cell spatial reconstruction. All the data and the example use cases are available in the Supplementary data.Atomic charges play a very important role in drug-target recognition. C25-140 in vitro However, computation of atomic charges with high-level quantum mechanics (QM) calculations is very time-consuming. A number of machine learning (ML)-based atomic charge prediction methods have been proposed to speed up the calculation of high-accuracy atomic charges in recent years. However, most of them used a set of predefined molecular properties, such as molecular fingerprints, for model construction, which is knowledge-dependent and may lead to biased predictions due to the representation preference of different molecular properties used for training. To solve the problem, we present a new architecture based on graph convolutional network (GCN) and develop a high-accuracy atomic charge prediction model named DeepAtomicCharge. The new GCN architecture is designed with only the atomic properties and the connection information between the atoms in molecules and can dynamically learn and convert molecules into appropriate atomic features without any prior knowledge of the molecules. Using the designed GCN architecture, substantial improvement is achieved for the prediction accuracy of atomic charges. The average root-mean-square error (RMSE) of DeepAtomicCharge is 0.0121 e, which is obviously more accurate than that (0.0180 e) reported by the previous benchmark study on the same two external test sets. Moreover, the new GCN architecture needs much lower storage space compared with other methods, and the predicted DDEC atomic charges can be efficiently used in large-scale structure-based drug design, thus opening a new avenue for high-performance atomic charge prediction and application.The present study evaluated the antifungal activity of the chelators deferiprone (DFP) and ethylenediaminetetraacetic acid (EDTA) and their effect on biofilm formation of the S. schenckii complex. Eighteen strains of Sporothrix spp. (seven S. brasiliensis, three S. globosa, three S. mexicana and five Sporothrix schenckii sensu stricto) were used. Minimum inhibitory concentration (MIC) values for EDTA and DFP against filamentous forms of Sporothrix spp. ranged from 32 to 128 μg/ml. For antifungal drugs, MIC values ranged from 0.25 to 4 μg/ml for amphotericin B, from 0.25 to 4 μg/ml for itraconazole, and from 0.03 to 0.25 μg/ml for terbinafine. The chelators caused inhibition of Sporothrix spp. in yeast form at concentrations ranging from 16 to 64 μg/ml (for EDTA) and 8 to 32 μg/ml (for DFP). For antifungal drugs, MIC values observed against the yeast varied from 0.03 to 0.5 μg/ml for AMB, 0.03 to 1 μg/ml for ITC, and 0.03 to 0.13 μg/ml for TRB. Both DFP and EDTA presented synergistic interaction with antifungals against Sporothrix spp. in both filamentous and yeast form. Biofilms formed in the presence of the chelators (512 μg/ml) showed a reduction of 47% in biomass and 45% in metabolic activity. Our data reveal that DFP and EDTA reduced the growth of planktonic cells of Sporothrix spp., had synergistic interaction with antifungal drugs against this pathogen, and reduced biofilm formation of Sporothrix spp.

Our data reveal that iron chelators deferiprone and ethylenediaminetetraacetic acid reduced the growth of planktonic cells of Sporothrix spp. as well as had synergistic interaction with antifungal drugs against this pathogen and reduced biofilm formation of Sporothrix spp.

Our data reveal that iron chelators deferiprone and ethylenediaminetetraacetic acid reduced the growth of planktonic cells of Sporothrix spp. as well as had synergistic interaction with antifungal drugs against this pathogen and reduced biofilm formation of Sporothrix spp.

Human papillomavirus (HPV) infection is involved in cervical cancer development, and hence understanding its prevalence and genotype distribution is important. However, there are few reports on the prevalence and genotype distribution of HPV in the city of Huzhou in China.

In this retrospective cross-sectional study, 11,506 women who visited Huzhou Maternity & Child Health Care Hospital between January 2018 and October 2019 were enrolled. The results of HPV genotyping and cytology tests were analyzed.

The overall prevalence of HPV infection was 15.5%. The rate of high-risk (HR) HPV infection (13.5%) was higher than that of single low-risk (LR) HPV infection (2.0%) (p<0.05). The five most common HPV genotypes were HPV52 (3.3%), 16 (1.9%), 58 (1.7%), 53 (1.5%), and 81 (1.2%). The infection rate of HPV peaked in women aged 16-24 and women aged ≥55. The infection rate of HPV58 or HPV81 appeared as a single peak in women aged ≥55. The rates of HR-HPV and LR-HPV infection were higher in subjects with abnormal cytology (p<0.