Extreme terahertz magnon multiplication induced by resonant magnetic pulse pairs

Nonlinear interactions of spin-waves and their quanta, magnons, have emerged as prominent candidates for interference-based technology, ranging from quantum transduction to antiferromagnetic spintronics. Yet magnon multiplication in the terahertz (THz) spectral region represents a major challenge. Intense, resonant magnetic fields from THz pulse-pairs with controllable phases and amplitudes enable high order THz magnon multiplication, distinct from non-resonant nonlinearities such as the high harmonic generation by below-band gap electric fields. Here, we demonstrate exceptionally high-order THz nonlinear magnonics. It manifests as 7th-order spin-wave-mixing and 6th harmonic magnon generation in an antiferromagnetic orthoferrite. We use THz two-dimensional coherent spectroscopy to achieve high-sensitivity detection of nonlinear magnon interactions up to six-magnon quanta in strongly-driven many-magnon correlated states. The high-order magnon multiplication, supported by classical and quantum spin simulations, elucidates the significance of four-fold magnetic anisotropy and Dzyaloshinskii-Moriya symmetry breaking. Moreover, our results shed light on the potential quantum fluctuation properties inherent in nonlinear magnons.

Identifying ARG-carrying bacteriophages in a lake replenished by reclaimed water using deep learning techniques

As important mobile genetic elements, phages support the spread of antibiotic resistance genes (ARGs). Previous analyses of metaviromes or metagenome-assembled genomes (MAGs) failed to assess the extent of ARGs transferred by phages, particularly in the generation of antibiotic pathogens. Therefore, we have developed a bioinformatic pipeline that utilizes deep learning techniques to identify ARG-carrying phages and predict their hosts, with a special focus on pathogens. Using this method, we discovered that the predominant types of ARGs carried by temperate phages in a typical landscape lake, which is fully replenished by reclaimed water, were related to multidrug resistance and β-lactam antibiotics. MAGs containing virulent factors (VFs) were predicted to serve as hosts for these ARG-carrying phages, which suggests that the phages may have the potential to transfer ARGs. In silico analysis showed a significant positive correlation between temperate phages and host pathogens (R = 0.503, p < 0.001), which was later confirmed by qPCR. Interestingly, these MAGs were found to be more abundant than those containing both ARGs and VFs, especially in December and March. Seasonal variations were observed in the abundance of phages harboring ARGs (from 5.62 % to 21.02 %) and chromosomes harboring ARGs (from 18.01 % to 30.94 %). In contrast, the abundance of plasmids harboring ARGs remained unchanged. In summary, this study leverages deep learning to analyze phage-transferred ARGs and demonstrates an alternative method to track the production of potential antibiotic-resistant pathogens by metagenomics that can be extended to microbiological risk assessment.

[Schistosomiasis control in Sichuan Province since the 12th Five - Year Plan period: progress and prospects]

An ambitious goal has been set for elimination of schistosomiasis in all endemic counties (districts) in Sichuan Province by 2023. To achieve this goal, and to continue to consolidate the control achievements, it is necessary to understand the current endemic status of schistosomiasis, identify the challenges and analyze the experiences and lessons from the schistosomiasis control program, and develop targeted control strategies and interventions in the province. This paper reviews the progress of schistosomiasis control in Sichuan Province since the 12th Five-Year Plan period, analyzes the challenges in the schistosomiasis elimination program, and proposes recommendations for future directions and priorities.

GDmicro: classifying host disease status with GCN and deep adaptation network based on the human gut microbiome data

MOTIVATION

With advances in metagenomic sequencing technologies, there are accumulating studies revealing the associations between the human gut microbiome and some human diseases. These associations shed light on using gut microbiome data to distinguish case and control samples of a specific disease, which is also called host disease status classification. Importantly, using learning-based models to distinguish the disease and control samples is expected to identify important biomarkers more accurately than abundance-based statistical analysis. However, available tools have not fully addressed two challenges associated with this task: limited labeled microbiome data and decreased accuracy in cross-studies. The confounding factors, such as the diet, technical biases in sample collection/sequencing across different studies/cohorts often jeopardize the generalization of the learning model.

RESULTS

To address these challenges, we develop a new tool GDmicro, which combines semi-supervised learning and domain adaptation to achieve a more generalized model using limited labeled samples. We evaluated GDmicro on human gut microbiome data from 11 cohorts covering 5 different diseases. The results show that GDmicro has better performance and robustness than state-of-the-art tools. In particular, it improves the AUC from 0.783 to 0.949 in identifying inflammatory bowel disease. Furthermore, GDmicro can identify potential biomarkers with greater accuracy than abundance-based statistical analysis methods. It also reveals the contribution of these biomarkers to the host's disease status.

AVAILABILITY AND IMPLEMENTATION

https://github.com/liaoherui/GDmicro.

Adaptive Radiotherapy Guided by PET/CT in Patients with Locally Advanced Non-Small Cell Lung Cancer: A Phase II Randomized Study

PURPOSE/OBJECTIVE(S)

The aim of this study was to determine whether adaptive radiotherapy guided by functional imaging with flourine-18 fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) can improve local tumor control in patients with locally advanced non-small cell lung cancer (LA-NSCLC).

MATERIALS/METHODS

This was a phase II randomized study comparing the efficacy and safety between PET-guided adaptive radiotherapy and conventional radiotherapy. The primary end point was 2-year local-regional tumor control (LRTC) rate. Secondary end points included local-regional progression-free survival (LR-PFS), progression-free survival (PFS), overall survival (OS), and radiation-related toxicities.

RESULTS

Between November 2012 and June 2017, 72 patients were 1:1 randomized to adaptive and conventional arms. The 2- and 5-year LRTC rates were 63.2% and 58.0% versus 43.0% and 37.6% (P = 0.035) in the adaptive and conventional arms, respectively. The median LR-PFS (14.3 versus 12.0 months; P = 0.010) and PFS (12.8 versus 8.9 months; P = 0.034) were significantly longer in the adaptive arm than in the conventional arm. The median OS was 36.3 months in the adaptive arm and 28.8 months in the conventional arm (P = 0.266). The esophageal volume of receiving ≥60 Gy (V60) in the adaptive arm was lower than that in the conventional arm (P = 0.011), while the V30 for the heart in the adaptive arm was lower than that in the conventional arm (P = 0.077). Other radiological metrological parameters of tumor, organs at risk, and the incidence of ≥grade 2 radiation-related toxicities were not significantly different between the 2 arms.

CONCLUSION

Compared with conventional radiotherapy, PET-guided adaptive radiotherapy significantly improved the 2-year LRTC rate, LR-PFS, and PFS without increased risks of radiation-related toxicities in patients with LA-NSCLC.

PhaGenus: genus-level classification of bacteriophages using a Transformer model

MOTIVATION

Bacteriophages (phages for short), which prey on and replicate within bacterial cells, have a significant role in modulating microbial communities and hold potential applications in treating antibiotic resistance. The advancement of high-throughput sequencing technology contributes to the discovery of phages tremendously. However, the taxonomic classification of assembled phage contigs still faces several challenges, including high genetic diversity, lack of a stable taxonomy system and limited knowledge of phage annotations. Despite extensive efforts, existing tools have not yet achieved an optimal balance between prediction rate and accuracy.

RESULTS

In this work, we develop a learning-based model named PhaGenus, which conducts genus-level taxonomic classification for phage contigs. PhaGenus utilizes a powerful Transformer model to learn the association between protein clusters and support the classification of up to 508 genera. We tested PhaGenus on four datasets in different scenarios. The experimental results show that PhaGenus outperforms state-of-the-art methods in predicting low-similarity datasets, achieving an improvement of at least 13.7%. Additionally, PhaGenus is highly effective at identifying previously uncharacterized genera that are not represented in reference databases, with an improvement of 8.52%. The analysis of the infants' gut and GOV2.0 dataset demonstrates that PhaGenus can be used to classify more contigs with higher accuracy.

PLASMe: a tool to identify PLASMid contigs from short-read assemblies using transformer

Plasmids are mobile genetic elements that carry important accessory genes. Cataloging plasmids is a fundamental step to elucidate their roles in promoting horizontal gene transfer between bacteria. Next generation sequencing (NGS) is the main source for discovering new plasmids today. However, NGS assembly programs tend to return contigs, making plasmid detection difficult. This problem is particularly grave for metagenomic assemblies, which contain short contigs of heterogeneous origins. Available tools for plasmid contig detection still suffer from some limitations. In particular, alignment-based tools tend to miss diverged plasmids while learning-based tools often have lower precision. In this work, we develop a plasmid detection tool PLASMe that capitalizes on the strength of alignment and learning-based methods. Closely related plasmids can be easily identified using the alignment component in PLASMe while diverged plasmids can be predicted using order-specific Transformer models. By encoding plasmid sequences as a language defined on the protein cluster-based token set, Transformer can learn the importance of proteins and their correlation through positionally token embedding and the attention mechanism. We compared PLASMe and other tools on detecting complete plasmids, plasmid contigs, and contigs assembled from CAMI2 simulated data. PLASMe achieved the highest F1-score. After validating PLASMe on data with known labels, we also tested it on real metagenomic and plasmidome data. The examination of some commonly used marker genes shows that PLASMe exhibits more reliable performance than other tools.

PhaBOX: a web server for identifying and characterizing phage contigs in metagenomic data

Motivation

There is accumulating evidence showing the important roles of bacteriophages (phages) in regulating the structure and functions of the microbiome. However, lacking an easy-to-use and integrated phage analysis software hampers microbiome-related research from incorporating phages in the analysis.

Results

In this work, we developed a web server, PhaBOX, which can comprehensively identify and analyze phage contigs in metagenomic data. It supports integrated phage analysis, including phage contig identification from the metagenomic assembly, lifestyle prediction, taxonomic classification, and host prediction. Instead of treating the algorithms as a black box, PhaBOX also supports visualization of the essential features for making predictions. The web server is designed with a user-friendly graphical interface that enables both informatics-trained and nonspecialist users to analyze phages in microbiome data with ease.

Availability and implementation

The web server of PhaBOX is available via: https://phage.ee.cityu.edu.hk. The source code of PhaBOX is available at: https://github.com/KennthShang/PhaBOX.

[Analysis of therapeutic mechanism of Liushen Wan against colitis-associated colorectal cancer based on network pharmacology and validation in mice]

OBJECTIVE

To explore the therapeutic mechanism of Liushen Wan (LSW) against colitis-associated colorectal cancer (CAC) by network pharmacology.

METHODS

TCMSP, BATMAN-TCM, CNKI, PubMed, Genecards, OMIM, and TTD databases were used to obtain the related targets of LSW and CAC. The common targets of LSW and CAC were obtained using Venny online website. The PPI network was constructed using Cytoscape 3.8.2 to screen the core targets of LSW in the treatment of CAC. GO and KEGG enrichment analysis were conducted using DAVID database. The therapeutic effect of LSW on CAC was evaluated in a C57BL/6J mouse model of AOM/DSS-induced CAC by observing the changes in body weight, disease activity index, colon length, and size and number of the tumor. HE staining and RT-qPCR were used to analyze the effect of LSW on inflammatory mediators. Immunohistochemistry and TUNEL staining were used to evaluate the effect of LSW on the proliferation and apoptosis of AOM/DSS-treated colon tumor cells. Immunohistochemistry and Western blotting were used to detect the effects of LSW on the expression of TLR4 proteins in CAC mice.

RESULTS

Network pharmacology analysis identified 69 common targets of LSW and CAC, and 33 hub targets were screened in the PPI network. KEGG pathway enrichment analysis suggested that the effect of LSW on CAC was mediated by the Toll-like receptor signaling pathway. In the mouse model of AOM/DSS-induced CAC, LSW significantly inhibited colitis-associated tumorigenesis, reduced tumor number and tumor load (P < 0.05), obviously improved histopathological changes in the colon, downregulated the mRNA levels of proinflammatory cytokines, and inhibited the proliferation (P < 0.01) and promoted apoptosis of colon tumor cells (P < 0.001). LSW also significantly decreased TLR4 protein expression in the colon tissue (P < 0.05).

CONCLUSION

LSW can inhibit CAC in mice possibly by regulating the expression of TLR4 to reduce intestinal inflammation, inhibit colon tumor cell proliferation and promote their apoptosis.

PhaVIP: Phage VIrion Protein classification based on chaos game representation and Vision Transformer

MOTIVATION

As viruses that mainly infect bacteria, phages are key players across a wide range of ecosystems. Analyzing phage proteins is indispensable for understanding phages' functions and roles in microbiomes. High-throughput sequencing enables us to obtain phages in different microbiomes with low cost. However, compared to the fast accumulation of newly identified phages, phage protein classification remains difficult. In particular, a fundamental need is to annotate virion proteins, the structural proteins, such as major tail, baseplate, etc. Although there are experimental methods for virion protein identification, they are too expensive or time-consuming, leaving a large number of proteins unclassified. Thus, there is a great demand to develop a computational method for fast and accurate phage virion protein (PVP) classification.

RESULTS

In this work, we adapted the state-of-the-art image classification model, Vision Transformer, to conduct virion protein classification. By encoding protein sequences into unique images using chaos game representation, we can leverage Vision Transformer to learn both local and global features from sequence "images". Our method, PhaVIP, has two main functions: classifying PVP and non-PVP sequences and annotating the types of PVP, such as capsid and tail. We tested PhaVIP on several datasets with increasing difficulty and benchmarked it against alternative tools. The experimental results show that PhaVIP has superior performance. After validating the performance of PhaVIP, we investigated two applications that can use the output of PhaVIP: phage taxonomy classification and phage host prediction. The results showed the benefit of using classified proteins over all proteins.

AVAILABILITY AND IMPLEMENTATION

The web server of PhaVIP is available via: https://phage.ee.cityu.edu.hk/phavip. The source code of PhaVIP is available via: https://github.com/KennthShang/PhaVIP.

HOTSPOT: Hierarchical hOst predicTion for aSsembled Plasmid cOntigs with Transformer

MOTIVATION

As prevalent extrachromosomal replicons in many bacteria, plasmids play an essential role in their hosts' evolution and adaptation. The host range of a plasmid refers to the taxonomic range of bacteria in which it can replicate and thrive. Understanding host ranges of plasmids sheds light on studying the roles of plasmids in bacterial evolution and adaptation. Metagenomic sequencing has become a major means to obtain new plasmids and derive their hosts. However, host prediction for assembled plasmid contigs still needs to tackle several challenges: different sequence compositions and copy numbers between plasmids and the hosts, high diversity in plasmids, and limited plasmid annotations. Existing tools have not yet achieved an ideal tradeoff between sensitivity and precision on metagenomic assembled contigs.

RESULTS

In this work, we construct a hierarchical classification tool named HOTSPOT, whose backbone is a phylogenetic tree of the bacterial hosts from phylum to species. By incorporating the state-of-the-art language model, Transformer, in each node's taxon classifier, the top-down tree search achieves an accurate host taxonomy prediction for the input plasmid contigs. We rigorously tested HOTSPOT on multiple datasets, including RefSeq complete plasmids, artificial contigs, simulated metagenomic data, mock metagenomic data, the Hi-C dataset, and the CAMI2 marine dataset. All experiments show that HOTSPOT outperforms other popular methods.

AVAILABILITY

The source code of HOTSPOT is available via: https://github.com/Orin-beep/HOTSPOT.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

[Effects of three medical nutrition therapies for weight loss on metabolic parameters and androgen level in overweight/obese patients with polycystic ovary syndrome]

Objective: To investigate the effects of calorie-restricted diet (CRD), high protein diet (HPD), high protein, and high dietary fiber diet (HPD+HDF) on metabolic parameters and androgen level in overweight/obese patients with polycystic ovary syndrome(PCOS). Methods: Ninety overweight/obese patients with PCOS from Peking University First Hospital from October 2018 to February 2020 were given medical nutrition weight loss therapy for 8 weeks and were randomly divided into CRD group, HPD group, and HPD+HDF group, with 30 patients in each group. Body composition, insulin resistance, and androgen level were detected before and after weight loss, and the efficacy of three weight loss therapies was compared through variance analysis and Kruskal-Wallis H test. Results: Eight patients in CRD group quit because they could not strictly complete the follow-up, therefore at the end of weight loss, 22, 30, and 30 patients in CRD group, HPD group and HPD+HDF group, respectively, were included in the final analysis. The baseline ages of the three groups were (31±2) years, (32±5) years and (31±5) years, respectively (P=0.952). After weight loss, the relevant indicators in HPD group and HPD+HDF group decreased more than those in CRD group. The body weight of CRD group, HPD group and HPD+HDF group decreased by 4.20 (11.92, 1.80), 5.00 (5.10, 3.32) and 6.10 (8.10, 3.07) kg, respectively (P=0.038); BMI of the three groups decreased by 0.80 (1.70, 0.40), 0.90 (1.23, 0.50) and 2.20 (3.30, 1.12) kg/m², respectively (P=0.002); homeostatic model assessment-insulin resistance(HOMA-IR) index decreased by 0.48(1.93, 0.05), 1.21(2.91, 0.18) and 1.22(1.75, 0.89), respectively (P=0.196); and free androgen index(FAI) decreased by 0.23(0.67, -0.04), 0.41(0.64, 0.30) and 0.44(0.63, 0.24), respectively (P=0.357). Conclusions: The three medical nutrition therapies can effectively reduce the weight of overweight/obese patients with PCOS, and improve insulin resistance and hyperandrogenism. Compared with CRD group, HPD group, and HPD+HDF group have better fat-reducing effect, and can better preserve muscle and basal metabolic rate while losing weight.

A179 MECHANISMS OF ACTION INVOLVED IN THE WOUND HEALING EFFECT OF THE DIETARY FIBRE RHAMNOGALACTURONAN

Background

Inflammatory bowel diseases (IBD) are chronic and relapsing inflammatory conditions associated with impaired intestinal epithelial barrier. Mucosal healing is the primary goal for IBD treatment since it is a good predictor of clinical remission. We previously showed the direct beneficial effects of rhamnogalacturonan (RGal), a polysaccharide isolated from the plant Acmella oleracea, on intestinal epithelial barrier function with participation of TLR4 and PKC activation. RNAseq data and pathway analysis have indicated the involvement of the canonical nuclear factor kB (NF-kB) signaling pathway.

Purpose

We hypothesize that RGal increases intestinal epithelial wound healing through FAK-Src/PI3K/NF-kB signaling pathways.

Method

Caco-2 and T84 cells, and human primary cell monolayers grown from ulcerative colitis patient-derived organoids, were wounded and treated with vehicle (media or 0.5% DMSO in media) or RGal (1000 μg/ml) for 48 h. Wound healing was assessed using either the IncuCyte or ImageXpress Pico live cell imaging systems. Proliferation and apoptosis of cells were evaluated using EdU and TUNEL assays, respectively. Inhibitors were added at the same time (transcription inhibitor Actinomycin D, 5 μg/ml) or 1 h (FAK inhibitor FAK-14, 10 μM, Src inhibitor PP2, 5 μM, PI3K inhibitor LY294002, 20 μM, NF-kB inhibitors Bay 11-7082 and JSH-23, 10 and 20 μM, respectively, or COX-2 inhibitor NS-398, 20 μM) before RGal treatment. Unwounded Caco-2 monolayers treated with RGal (1000 μg/ml) were collected for Western blotting for COX-2 protein.

Result(s)

In the wound healing assay, RGal (1000 μg/ml) enhanced wound healing by 12.5% at 48 h in Caco-2 cells and by 14.7% at 24 h in T84 cells, compared to control group. RGal (1000 μg/ml) also accelerated the wound closure in colonoid monolayers obtained from ulcerative colitis patient biopsies by 81.3% at 48 h. Neither proliferation nor apoptosis were involved in the RGal effect on wound healing. Treatment of cells with FAK14 (10 μM), PP2 (5 μM), and PI3K (20 μM) significantly prevented the RGal-induced wound healing. Actinomycin D (5 μg/ml), Bay 11-7082 (10 μM) or JSH-23 (20 μM) treatment significantly reversed the effect of RGal on wound healing, showing that the response was transcriptionally dependent and involved NF-kB signaling. Treatment of cells with NS-398 (20 μM) also reversed the effect of RGal on wound healing. COX-2 protein expression was significantly increased at 6 and 12 h after RGal addition to Caco-2 monolayers.

Conclusion(s)

These data suggest that the plant-based polysaccharide RGal increases intestinal epithelial cell wound healing by increasing cell migration. The RGal effect is dependent on the activation of the FAK-Src/PI3K signaling pathways and subsequently the transcription factor NF-kB and downstream COX-2 protein expression and activity. Our findings show a novel mechanism of action of RGal in wound healing that could help in the resolution of intestinal inflammation and mucosal healing.

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Disclosure of Interest

None Declared

Virus classification for viral genomic fragments using PhaGCN2

Viruses are the most ubiquitous and diverse entities in the biome. Due to the rapid growth of newly identified viruses, there is an urgent need for accurate and comprehensive virus classification, particularly for novel viruses. Here, we present PhaGCN2, which can rapidly classify the taxonomy of viral sequences at the family level and supports the visualization of the associations of all families. We evaluate the performance of PhaGCN2 and compare it with the state-of-the-art virus classification tools, such as vConTACT2, CAT and VPF-Class, using the widely accepted metrics. The results show that PhaGCN2 largely improves the precision and recall of virus classification, increases the number of classifiable virus sequences in the Global Ocean Virome dataset (v2.0) by four times and classifies more than 90% of the Gut Phage Database. PhaGCN2 makes it possible to conduct high-throughput and automatic expansion of the database of the International Committee on Taxonomy of Viruses. The source code is freely available at https://github.com/KennthShang/PhaGCN2.0.

PhaTYP: predicting the lifestyle for bacteriophages using BERT

Bacteriophages (or phages), which infect bacteria, have two distinct lifestyles: virulent and temperate. Predicting the lifestyle of phages helps decipher their interactions with their bacterial hosts, aiding phages' applications in fields such as phage therapy. Because experimental methods for annotating the lifestyle of phages cannot keep pace with the fast accumulation of sequenced phages, computational method for predicting phages' lifestyles has become an attractive alternative. Despite some promising results, computational lifestyle prediction remains difficult because of the limited known annotations and the sheer amount of sequenced phage contigs assembled from metagenomic data. In particular, most of the existing tools cannot precisely predict phages' lifestyles for short contigs. In this work, we develop PhaTYP (Phage TYPe prediction tool) to improve the accuracy of lifestyle prediction on short contigs. We design two different training tasks, self-supervised and fine-tuning tasks, to overcome lifestyle prediction difficulties. We rigorously tested and compared PhaTYP with four state-of-the-art methods: DeePhage, PHACTS, PhagePred and BACPHLIP. The experimental results show that PhaTYP outperforms all these methods and achieves more stable performance on short contigs. In addition, we demonstrated the utility of PhaTYP for analyzing the phage lifestyle on human neonates' gut data. This application shows that PhaTYP is a useful means for studying phages in metagenomic data and helps extend our understanding of microbial communities.

Phage family classification under Caudoviricetes: A review of current tools using the latest ICTV classification framework

Bacteriophages, which are viruses infecting bacteria, are the most ubiquitous and diverse entities in the biosphere. There is accumulating evidence revealing their important roles in shaping the structure of various microbiomes. Thanks to (viral) metagenomic sequencing, a large number of new bacteriophages have been discovered. However, lacking a standard and automatic virus classification pipeline, the taxonomic characterization of new viruses seriously lag behind the sequencing efforts. In particular, according to the latest version of ICTV, several large phage families in the previous classification system are removed. Therefore, a comprehensive review and comparison of taxonomic classification tools under the new standard are needed to establish the state-of-the-art. In this work, we retrained and tested four recently published tools on newly labeled databases. We demonstrated their utilities and tested them on multiple datasets, including the RefSeq, short contigs, simulated metagenomic datasets, and low-similarity datasets. This study provides a comprehensive review of phage family classification in different scenarios and a practical guidance for choosing appropriate taxonomic classification pipelines. To our best knowledge, this is the first review conducted under the new ICTV classification framework. The results show that the new family classification framework overall leads to better conserved groups and thus makes family-level classification more feasible.

HaploDMF: viral haplotype reconstruction from long reads via deep matrix factorization

MOTIVATION

Lacking strict proofreading mechanisms, many RNA viruses can generate progeny with slightly changed genomes. Being able to characterize highly similar genomes (i.e. haplotypes) in one virus population helps study the viruses' evolution and their interactions with the host/other microbes. High-throughput sequencing data has become the major source for characterizing viral populations. However, the inherent limitation on read length by next-generation sequencing makes complete haplotype reconstruction difficult.

RESULTS

In this work, we present a new tool named HaploDMF that can construct complete haplotypes using third-generation sequencing (TGS) data. HaploDMF utilizes a deep matrix factorization model with an adapted loss function to learn latent features from aligned reads automatically. The latent features are then used to cluster reads of the same haplotype. Unlike existing tools whose performance can be affected by the overlap size between reads, HaploDMF is able to achieve highly robust performance on data with different coverage, haplotype number and error rates. In particular, it can generate more complete haplotypes even when the sequencing coverage drops in the middle. We benchmark HaploDMF against the state-of-the-art tools on simulated and real sequencing TGS data on different viruses. The results show that HaploDMF competes favorably against all others.

AVAILABILITY AND IMPLEMENTATION

The source code and the documentation of HaploDMF are available at https://github.com/dhcai21/HaploDMF.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

[Analysis of hepatic pathological inflammation and fibrosis condition and its influencing factors in 721 patients with chronic hepatitis B with normal ALT]

Objective: To analyze the hepatic pathological inflammation and fibrosis condition in order to explore the relationship with related clinical indicators in patients with chronic hepatitis B patients with normal alanine aminotransferase (ALT). Methods: 721 cases of chronic hepatitis B with normal ALT who were initially diagnosed in the Department of Infectious Diseases of Henan Provincial People's Hospital from August 2016 to December 2019 were retrospectively collected. Liver biopsy was performed in all patients. General data of patients such as gender, age, liver function indexes, blood routine indexes, HBsAg level, HBeAg status, HBV DNA level, spleen thickness and prothrombin time were collected. Univariate and multivariate analysis methods were used to determine the influencing factors of inflammation and fibrosis degree with liver biopsy. A receiver operating characteristic curve (ROC) was used to evaluate the established multi-factor prediction model. Alpha=0.05 was considered as a standard orientation of test. Results: The average age of 721 cases with chronic hepatitis B was 36.1±9.7 years, and the male to female ratio was 1.28/1, with inflammation and fibrosis grade mainly concentrated in G1S1 (349 cases), G1S2 (132 cases), G2S2 (119 cases), and G2S1 (57 cases). Among them, there were 349 (48.4%) cases of G1S1, and 372 (51.6%) cases of G/S≥2. The main manifestations were mild to moderate inflammation and fibrosis, and only 64 (8.88%) cases had severe G/S≥3. HBsAg level (stratified with 4 log10 IU/ml as the boundary) analyzed in 721 cases were correlated with the relevant clinical indicators stratification and liver pathological inflammation and fibrosis, and the difference was statistically significant (inflammation grade, χ²=6.182, P=0.013; Fibrosis grade, χ²=36.534, P=0.001). Univariate analysis of the relevant clinical indicators that may influence the patient's liver pathological G/S ≥2 showed the patient's age, albumin, γ- glutamyltransferase (GGT), platelet, prothrombin time (PT), spleen thickness and HBsAg level were all statistically significant (P<0.05), while multivariate analysis showed that age, GGT, PT, and spleen thickness had statistical differences (P<0.05). The prediction model was established in accordance to multivariate analysis, and the area under the ROC curve was 0.642. Maximization of the sum of sensitivity and specificity as cut-off value of Logit P=0.497, the diagnostic sensitivity, specificity, and Youden's index were 60.6%, 64.5%, and 0.252, respectively. Conclusion: More than half of patients with chronic hepatitis B with normal ALT have significant inflammation and fibrosis and require timely antiviral therapy. Age, GGT, PT and spleen thickness can help comprehensively evaluate the liver inflammation and fibrosis status among patients, but the lack of accurate prediction models suggests that more effective indicators that can help predict the inflammation and fibrosis status of such patients have yet to be discovered. Therefore, liver biopsy should still be actively performed in patients with normal ALT to confirm the diagnosis and timely treatment.

Accurate identification of bacteriophages from metagenomic data using Transformer

Motivation

Bacteriophages are viruses infecting bacteria. Being key players in microbial communities, they can regulate the composition/function of microbiome by infecting their bacterial hosts and mediating gene transfer. Recently, metagenomic sequencing, which can sequence all genetic materials from various microbiome, has become a popular means for new phage discovery. However, accurate and comprehensive detection of phages from the metagenomic data remains difficult. High diversity/abundance, and limited reference genomes pose major challenges for recruiting phage fragments from metagenomic data. Existing alignment-based or learning-based models have either low recall or precision on metagenomic data.

Results

In this work, we adopt the state-of-the-art language model, Transformer, to conduct contextual embedding for phage contigs. By constructing a protein-cluster vocabulary, we can feed both the protein composition and the proteins’ positions from each contig into the Transformer. The Transformer can learn the protein organization and associations using the self-attention mechanism and predicts the label for test contigs. We rigorously tested our developed tool named PhaMer on multiple datasets with increasing difficulty, including quality RefSeq genomes, short contigs, simulated metagenomic data, mock metagenomic data and the public IMG/VR dataset. All the experimental results show that PhaMer outperforms the state-of-the-art tools. In the real metagenomic data experiment, PhaMer improves the F1-score of phage detection by 27%.

CHERRY: a Computational metHod for accuratE pRediction of virus-pRokarYotic interactions using a graph encoder-decoder model

Prokaryotic viruses, which infect bacteria and archaea, are key players in microbial communities. Predicting the hosts of prokaryotic viruses helps decipher the dynamic relationship between microbes. Experimental methods for host prediction cannot keep pace with the fast accumulation of sequenced phages. Thus, there is a need for computational host prediction. Despite some promising results, computational host prediction remains a challenge because of the limited known interactions and the sheer amount of sequenced phages by high-throughput sequencing technologies. The state-of-the-art methods can only achieve 43% accuracy at the species level. In this work, we formulate host prediction as link prediction in a knowledge graph that integrates multiple protein and DNA-based sequence features. Our implementation named CHERRY can be applied to predict hosts for newly discovered viruses and to identify viruses infecting targeted bacteria. We demonstrated the utility of CHERRY for both applications and compared its performance with 11 popular host prediction methods. To our best knowledge, CHERRY has the highest accuracy in identifying virus–prokaryote interactions. It outperforms all the existing methods at the species level with an accuracy increase of 37%. In addition, CHERRY’s performance on short contigs is more stable than other tools.

A64 THE DIETARY FIBRE RHAMNOGALACTURONAN PROMOTES INTESTINAL EPITHELIAL CELL MIGRATION THROUGH THE NF-κB SIGNALING PATHWAY

Background

Damaged intestinal epithelial barrier is characteristic of inflammatory bowel diseases (IBD) and mucosal healing is the primary goal for IBD treatment. We previously showed the direct beneficial effects of rhamnogalacturonan (RGal), a polysaccharide isolated from the plant Acmella oleracea, on intestinal epithelial barrier function with participation of TLR4 and PKC activation. We also observed that RGal accelerates wound healing in human colonic epithelial Caco-2 cells. RNAseq data and pathway analysis have indicated the involvement of the canonical nuclear factor kB (NF-kB) signaling pathway.

Aims

We hypothesize that RGal increases intestinal epithelial wound healing through NF-kB signaling pathway.

Methods

Caco-2 cells monolayers were scratched and treated with vehicle (media or 0.5% DMSO in media) or RGal (1000 mg/ml) for 48 h. Wound healing was assessed using the IncuCyte live cell imaging system. Proliferation and apoptosis of cells were evaluated using EdU and TUNEL assays, respectively. Inhibitors were added at the same time (transcription inhibitor Actinomycin D, 5 mg/ml) or 1 h (NF-kB inhibitors Bay 11–7082 and JSH-23, 10 and 20 mM, respectively, or COX-2 inhibitor NS-398, 20 mM) before RGal treatment. Unwounded Caco-2 monolayers treated with RGal (1000 mg/ml) were collected for Western blotting for COX-2 protein.

Results

In the wound healing assay, RGal at a concentration of 1000 µg/ml enhanced wound healing by 12.5% at 48 h compared to control group, under 10% serum conditions. Neither proliferation nor apoptosis were involved in the RGal effect on wound healing, suggesting the response was due solely to cell migration. Actinomycin D (5 mg/ml), Bay 11–7082 (10 mM) or JSH-23 (20 mM) treatment significantly reversed the effect of RGal on wound healing, showing that the response was transcriptionally dependent and involved NF-kB signaling. Treatment of cells with NS-398 (20 mM) also reversed the effect of RGal on wound healing. COX-2 protein expression was significantly increased at 6 and 12 h after RGal addition to Caco-2 monolayers.

Conclusions

These data suggest that the plant-based polysaccharide RGal increases intestinal epithelial cell wound healing by increasing cell migration. The RGal effect is dependent on the activation of the transcription factor NF-kB and downstream COX-2 protein expression and activity. Our findings show a novel mechanism of action of RGal in wound healing that could help in the resolution of intestinal inflammation and mucosal healing.

Funding Agencies

NSERC

[Analysis of clinical features and etiological diagnostic indices of reproductive age women with hyperandrogenism]

Objective: To investigate the clinical features and the value of different diagnostic indices for etiology in reproductive age women with hyperandrogenism. Methods: The medical records of 96 reproductive age women with hyperandrogenism in the multi-disciplinary team of Peking University First Hospital from January 2020 to April 2021 were collected. The patients were divided into four groups based on final diagnosis: congenital adrenal hyperplasia (CAH) (n=8), polycystic ovary syndrome (PCOS) (n=67), idiopathic hyperandrogenism (n=13) and other specific diseases (n=8), respectively. The indices related to androgens in different groups were compared, and then their efficiency for diagnosis of CAH and PCOS were analyzed with receiver operator characteristic curve (ROC curve). Results: A total of 96 patients with hyperandrogenism were recruited, with the age of 19-45 (29±6) years old. Overall, 4.2% (4/96) of the patients were with single clinical hyperandrogenism, 56.3% (54/96) were with single laboratory hyperandrogenaemia and 39.6% (38/96) were with both. The breakdown into laboratory hyperandrogenaemia subtypes was as follows: only T elevation 22.8% (21/92), only A2 elevation 7.6% (7/92), none DHEAS elevation, only FAI elevation 5.4% (5/92) and elevation of more than one of the androgen indices mentioned above accounted for 64.1% (59/92). In the reasons of consultation, simple irregular menstruation (36.0%, 32/89) or accompanied by clinical hyperandrogenism with or without infertility (36.0%, 32/89) were the most common. As for primary visiting departments, Obstetrics and Gynecology accounted for 53.2% (51/96), and then Endocrinology as 39.5% (38/96). The 17-OHP level of CAH, PCOS and idiopathic hyperandrogenism group was 20.0 (8.2, 33.1), 1.1 (0.8, 1.4), 0.9 (0.8, 1.3) ng/ml, respectively. The androstenedione level in these groups was 6.3 (4.6, 8.7), 3.8 (2.9, 4.8) and 3.2 (2.7, 3.7) ng/ml, respectively. The 17-OHP and androstenedione levels of CAH group were significantly higher than that in PCOS or idiopathic hyperandrogenism group (all P<0.05). The ratio of LH and FSH in these three groups was 0.8(0.5, 1.0), 1.3(0.6, 1.9) and 0.6(0.3, 0.7), respectively. The ratio of LH and FSH was significantly higher in PCOS than that in idiopathic hyperandrogenism group (P=0.024), but yet there was no significant difference compared with CAH group (P>0.05). The AUC of ROC curve of 17-OHP for CAH diagnosis was 0.94, followed by androstenedione 0.83, whereas LH/FSH for PCOS diagnosis was only 0.63. Conclusions: Among the reasons of consultation in reproductive age women who visited our multi-disciplinary team for female hyperandrogenism, simple irregular menstruation or accompanied by clinical hyperandrogenism with or without infertility are the most common. PCOS accounts for the majority of different androgen excess disorders. 17-OHP is the most valuable parameter for the diagnosis of CAH and secondly androstenedione.

RdRp-based sensitive taxonomic classification of RNA viruses for metagenomic data

With advances in library construction protocols and next-generation sequencing technologies, viral metagenomic sequencing has become the major source for novel virus discovery. Conducting taxonomic classification for metagenomic data is an important means to characterize the viral composition in the underlying samples. However, RNA viruses are abundant and highly diverse, jeopardizing the sensitivity of comparison-based classification methods. To improve the sensitivity of read-level taxonomic classification, we developed an RNA-dependent RNA polymerase (RdRp) gene-based read classification tool RdRpBin. It combines alignment-based strategy with machine learning models in order to fully exploit the sequence properties of RdRp. We tested our method and compared its performance with the state-of-the-art tools on the simulated and real sequencing data. RdRpBin competes favorably with all. In particular, when the query RNA viruses share low sequence similarity with the known viruses (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\sim 0.4$\end{document}), our tool can still maintain a higher F-score than the state-of-the-art tools. The experimental results on real data also showed that RdRpBin can classify more RNA viral reads with a relatively low false-positive rate. Thus, RdRpBin can be utilized to classify novel and diverged RNA viruses.

Predicting the hosts of prokaryotic viruses using GCN-based semi-supervised learning

Background

Prokaryotic viruses, which infect bacteria and archaea, are the most abundant and diverse biological entities in the biosphere. To understand their regulatory roles in various ecosystems and to harness the potential of bacteriophages for use in therapy, more knowledge of viral-host relationships is required. High-throughput sequencing and its application to the microbiome have offered new opportunities for computational approaches for predicting which hosts particular viruses can infect. However, there are two main challenges for computational host prediction. First, the empirically known virus-host relationships are very limited. Second, although sequence similarity between viruses and their prokaryote hosts have been used as a major feature for host prediction, the alignment is either missing or ambiguous in many cases. Thus, there is still a need to improve the accuracy of host prediction.

Results

In this work, we present a semi-supervised learning model, named HostG, to conduct host prediction for novel viruses. We construct a knowledge graph by utilizing both virus-virus protein similarity and virus-host DNA sequence similarity. Then graph convolutional network (GCN) is adopted to exploit viruses with or without known hosts in training to enhance the learning ability. During the GCN training, we minimize the expected calibrated error (ECE) to ensure the confidence of the predictions. We tested HostG on both simulated and real sequencing data and compared its performance with other state-of-the-art methods specifically designed for virus host classification (VHM-net, WIsH, PHP, HoPhage, RaFAH, vHULK, and VPF-Class).

Conclusion

HostG outperforms other popular methods, demonstrating the efficacy of using a GCN-based semi-supervised learning approach. A particular advantage of HostG is its ability to predict hosts from new taxa.

Supplementary Information

The online version contains supplementary material available at (10.1186/s12915-021-01180-4).

[Analysis of the clinical effects and outcome of patients with double-hit high-risk multiple myeloma]

Objective: To compare the clinical features, clinical efficacy, and prognosis of patients with double-hit and non-double-hit high-risk multiple myeloma (MM) and explored the clinical significance of high-risk cell karyotype in MM development. Methods: The clinical data of 73 high-risk MM patients admitted to the Department of Hematology of Fujian Provincial Hospital from January 2011 to February 2019 were retrospectively analyzed. Interphase fluorescence in situ hybridization was used to detect their karyotypes. Based on mSMART 3.0 risk stratification, we divided the patients into a double-hit group (28 cases) and a non-double-hit group (45 cases). Results: Fifteen patients in the double-hit group and 26 in the non-double-hit group received bortezomib-based chemotherapy. The median progression-free survival (PFS) in the double-hit and the non-double-hit groups was 8.0 months and 22.0 months, and the median overall survival (OS) was 10.0 months and not reached, respectively. Ten patients in the double-hit group and 12 in the non-double-hit group received bortezomib combined with lenalidomide (RVD) chemotherapy. The median PFS in the double-hit group and the non-double-hit group was 12.0 months and 24.0 months, and the median OS was 14.0 months and not reached, correspondingly. Both the PFS and OS of the double-hit group were significantly shorter than those of the non-double-hit group (P<0.05). Univariate analysis results indicated that cytogenetic abnormalities, revised-international staging system (R-ISS), β2 microglobulin, and calcium had significant effects on PFS in high-risk MM patients (P<0.05). The cytogenetic abnormalities, R-ISS, and β2 microglobulin were associated with OS in high-risk MM patients (P=0.001). Multivariate Cox regression analysis showed that the cytogenetic grouping was an independent prognostic factor for OS and PFS in high-risk MM patients. The risk of disease progression was 3.160 times (95% CI: 1.364-7.318) and the risk of death was 2.966 times higher (95%CI: 1.205-7.306) in the double-hit group than those in the non-double-hit group. Calcium was an independent risk factor for PFS in the high-risk MM patients. Notably, the risk of disease progression in patients with calcium levels≥ 2.75 mmol/L was 2.667 times higher than that in patients with calcium<2.75 mmol/L (95% CI: 1.209-5.883). Conclusions: Double-hit patients are a highly specific group with worse high-risk MM prognosis. In such patients, the relapse is more common, the disease progression is faster, and the survival time is shorter than those in the non-double-hit patients.

[The clinical effects of oral contraceptive pretreatment on the outcome of gonadotropin releasing hormone antagonist protocol in non-polycystic ovary syndrome patients]

Objective: To evaluate the clinical effect of oral contraceptive (OC) pretreatment on the outcome of gonadotropin releasing hormone antagonist (GnRH-a) protocol in patients with non-polycystic ovary syndrome. Methods: From January 2017 to May 2019, a total of 436 patients undergoing in vitro fertilization and embryo transfer/Intracytoplasmic sperm injection (IVF-ET/ICSI) treatment in Peking University First Hospital reproductive center clinic were included in this retrospective cohort study. A total of 144 patients (147 cycles) used OC pretreatment prior to GnRH-a protocol and 292 patients (306 cycles) used GnRH-a protocol without OC pretreatment. The drug usage as well as pregnant outcomes between groups were examined. The primary outcome was the cumulative clinical pregnancy rate of oocyte retrieval cycle and the secondary outcome included the number of oocytes, MⅡ oocytes, embryos and clinical pregnancy rate of fresh embryo transfer cycle. Results: The median ages (and Q₁, Q₃) of OC pretreatment group and non-OC group were 33 (30,36) and 34 (30,38) years old, respectively. The number of MⅡ oocytes was higher in OC pretreatment group (7/9) than in non-OC group (6/8) (P=0.002). The significant difference were not found in the cumulative clinical pregnancy rate of each oocyte retrieval cycle (61.7% vs 54.6%), the clinical pregnancy rate of fresh embryo transfer cycle (34.4% vs 35.6%), and the number of oocytes (9 vs 8) and embryos (6 vs 6) between groups. Conclusion: Our findings suggest that compared to non-OC pretreatment group, pretreatment with OC is associated with more MⅡ oocytes, and with an increasing trend of the cumulative clinical pregnancy rate in non-polycystic ovary syndrome patients undergoing fresh IVF-ET/ICSI.

Bacteriophage classification for assembled contigs using graph convolutional network

Motivation

Bacteriophages (aka phages), which mainly infect bacteria, play key roles in the biology of microbes. As the most abundant biological entities on the planet, the number of discovered phages is only the tip of the iceberg. Recently, many new phages have been revealed using high-throughput sequencing, particularly metagenomic sequencing. Compared to the fast accumulation of phage-like sequences, there is a serious lag in taxonomic classification of phages. High diversity, abundance and limited known phages pose great challenges for taxonomic analysis. In particular, alignment-based tools have difficulty in classifying fast accumulating contigs assembled from metagenomic data.

Results

In this work, we present a novel semi-supervised learning model, named PhaGCN, to conduct taxonomic classification for phage contigs. In this learning model, we construct a knowledge graph by combining the DNA sequence features learned by convolutional neural network and protein sequence similarity gained from gene-sharing network. Then we apply graph convolutional network to utilize both the labeled and unlabeled samples in training to enhance the learning ability. We tested PhaGCN on both simulated and real sequencing data. The results clearly show that our method competes favorably against available phage classification tools.

Availability and implementation

The source code of PhaGCN is available via: https://github.com/KennthShang/PhaGCN.

[Immunotherapy the road of hope for patients with advanced liver cancer in the new era]

Liver cancer prevention has always been a key issue in the follow-up diagnosis and treatment of viral hepatitis. Insidious onset, high morbidity, monotherapy, short survival time, and high mortality are the outstanding problems encountered in the diagnosis and treatment of advanced liver cancer. In recent years, with the clinical application of targeted drugs and immune checkpoint inhibitors, phased progress has been made in the diagnosis and treatment of advanced liver cancer, especially the accessibility of drug prices under the new medical insurance has provided more and more patients the opportunity to achieve a longer survival time. In this paper, the hot issues in the diagnosis and treatment of patients with advanced liver cancer in the immunotherapy era are discussed.

[Analysis of bone mineral density and its influencing factors in 211 patients with chronic hepatitis B treated with long-term entecavir monotherapy]

Objective: To investigate the changes of bone mineral density and its related influencing factors in chronic hepatitis B patients treated with long-term entecavir monotherapy. Methods: 211 cases with chronic hepatitis B treated with entecavir monotherapy in the Department of Infectious Diseases of Henan Provincial People's Hospital from June 2018 to September 2019 were retrospectively collected. Age, gender, body mass index, number of years of medication use, presence or absence of liver cirrhosis and current bone mineral density level (using dual-energy X-ray detection, taking lumbar L1 ~ 4 and left femur as observation region) and other related data were collected. 211 cases general situation was descriptively analyzed by case-control study design. Two independent sample t-tests were used to compare the differences in serum calcium, phosphorus, and renal function levels in patients with different medication durations. Univariate logistic regression was used to screen the influencing factors of bone mineral density level. Significant variables of univariate analysis were included in multivariate logistic regression to obtain the independent influencing factors leading to the decrease of bone mineral density level. The test level was set as α = 0.05. Results: The average age of 211 cases with chronic hepatitis B was (42.36 ± 11.10) years. The average medication time use was (2.52 ± 1.94) years. The body mass index (23.95 ± 3.11), and male-to-female ratio was 2.25/1. The incidence of liver cirrhosis was 35.5%. The incidence of low bone mass in the two observation sites (lumbar spine L1~4 and left femur) was 24.6% and 29.4%, respectively. There were statistically significant differences in serum calcium, phosphorus and renal function levels among patients with different entecavir treatment duration (≥3 years and < 3 years) (P < 0.05). Univariate analysis result showed that the influencing factors of BMD were age, the number of years of medication use, gender, liver cirrhosis (L1~4 of the lumbar spine region) and age, the number of years of medication, and gender (left femoral region). The variables that entered the two models after the multivariate analysis were age (L1~4 region of lumbar spine: OR = 2.225, left femur OR = 1.660), gender (L1~4 region of lumbar spine: OR = 3.048, left femur OR = 2.496), number of years of medication use (L1~4 region of lumbar spine: OR = 1.387, left femur OR = 1.276). Conclusion: Age, gender, and the number of years of medication use are independent factors that influence the bone mineral density of patients with chronic hepatitis B treated with long-term entecavir. Low bone mass risk at the two observation sites is 2.225 and 1.66 times the normal level for every 10 years of age increase. Compared with men, the risk of low bone mass at the two observation sites is 3.048 and 2.496 times for women, and for every additional year of medication use, the risk of low bone mass at the two observation sites is 1.387 and 1.276 times the normal level. Female patients with older age and prolonged medication use are at high risk of developing bone mineral density reduction.

[Differential proteomic screening of plasma exosomes before and after magnesium isoglycyrrhizinate treatment in chronic hepatitis B]

Objective: To screen the differential proteomic of plasma exosomes before and after magnesium isoglycyrrhizinate (MgIG) treatment in chronic hepatitis B patients. Methods: Plasma samples were collected from 36 cases with chronic hepatitis B before and after MgIG treatment (2 ml/case). Plasma exosomes were extracted by ultracentrifugation. Exosomal particles concentration and inner diameter were detected by Nanosight NS300 particle size analyzer. Three cases of plasma exosomes were randomly selected before and after MgIG treatment. Proteins were extracted after lysis and digested with trypsin. Label-free differential proteomics analysis was performed by liquid chromatography-tandem mass spectrometry to screen out differential proteins that changed more than 1.5 times. Enzyme linked immunosorbent assay (ELISA) was used to verify the quantitative differential protein expression (n = 30). Measurement data were compared by paired sample t-test. Results: The average particle concentration of the extracted exosomes was 2.2×10(9)/ml, and the average size was (107 ± 52) nm, which was consistent with the theoretical value of plasma exosome size, proving that the plasma exosomes were successfully extracted. Proteomics results showed that before and after MgIG treatment in chronic hepatitis B patients, a total of 153 differentially expressed proteins were screened, including 85 up-regulated and 68 down-regulated proteins. Enzyme-linked immunosorbent assay results showed that compared with the MgIG before and after treatment group of chronic hepatitis B patients, the differences in the concentrations of hepatocyte growth factor activator and hepatocyte growth factor like protein in plasma exosomes were statistically significant (P < 0.05). Hepatocyte growth factor activator concentration in the plasma exosomes before and after MgIG treatment group was (45.9 ± 9.4) μg/ml and (13.9 ± 2.0) μg/ml, respectively, and it was down-regulated by about 3 times. Hepatocyte growth factor-like protein concentration in the plasma exosomes before and after MgIG treatment group was (23.4 ± 4.9) μg/ml and (13.8 ± 2.2) μg/ml, respectively, and it was down-regulated by about 2 times. Enzyme-linked immunosorbent assay results had consistency with the proteomics results. Conclusion: This study successfully screened the differential proteomic of plasma exosomes before and after MgIG treatment in chronic hepatitis B, and provided experimental basis for studying the molecular mechanism of MgIG treatment for chronic hepatitis B.

Improving protein domain classification for third-generation sequencing reads using deep learning

BACKGROUND

With the development of third-generation sequencing (TGS) technologies, people are able to obtain DNA sequences with lengths from 10s to 100s of kb. These long reads allow protein domain annotation without assembly, thus can produce important insights into the biological functions of the underlying data. However, the high error rate in TGS data raises a new challenge to established domain analysis pipelines. The state-of-the-art methods are not optimized for noisy reads and have shown unsatisfactory accuracy of domain classification in TGS data. New computational methods are still needed to improve the performance of domain prediction in long noisy reads.

RESULTS

In this work, we introduce ProDOMA, a deep learning model that conducts domain classification for TGS reads. It uses deep neural networks with 3-frame translation encoding to learn conserved features from partially correct translations. In addition, we formulate our problem as an open-set problem and thus our model can reject reads not containing the targeted domains. In the experiments on simulated long reads of protein coding sequences and real TGS reads from the human genome, our model outperforms HMMER and DeepFam on protein domain classification.

CONCLUSIONS

In summary, ProDOMA is a useful end-to-end protein domain analysis tool for long noisy reads without relying on error correction.

[Novel vector preS1-tp fusion protein effectively inhibits hepatitis B virus replication and cccDNA synthesis by mediating hepatitis B virus targeting sequence small interfering RNA]

Objective: To study the use of preS1-tp fusion protein as a novel vector to mediate the entry of small interfering RNA (siRNA) targeting the carboxy-terminal nuclear localization signal (NLS) region of hepatitis B virus (HBV) core protein into HBV-infected hepatocytes, and to further explore the HBV replication inhibition and covalently closed circular DNA synthesis. Methods: HepG2.2.15 cells expressing the human sodium taurocholate co-transporting polypeptide were established on the basis of lentivirus infection system. siRNA against HBV NLS region was designed and synthesized. PreS1-tp fusion protein expression and purification was observed to test its ability to cell entry and DNA binding. NLS siRNA were delivered into HepG2.2.15- sodium taurocholate co-transporting polypeptide cells by preS1-tp fusion protein as a vector to observe the effects of NLS siRNA on HBV replication and covalently closed circular DNA levels. Analysis of variance was used for comparison between multiple groups, and the measurement data differences between groups were analyzed by t-test. Results: HepG2.2.15-sodium taurocholate co-transporting polypeptide cell line was successfully constructed. Screened synthetic HBV NLS siRNA had significantly inhibited HBV replication. The preS1-tp fusion protein was expressed and purified on a large-scale. The fusion protein as a vector for HBV NLS siRNA had targeted delivery. The result showed that the fusion protein had effectively targeted siRNA to Hepg2.2.15-sodium taurocholate co-transporting polypeptide cell, which not only had effectively inhibited the expression of HBV mRNA, HBsAg and HBeAg, but also had significantly reduced the levels of HBV DNA and covalently closed circular DNA. Conclusion: The preS1-tp fusion protein constructed in this study uses the dual functional characteristics of preS1 binding to hepatocyte HBV receptor, and tp binding to nucleic acids, and targets HBV NLS siRNA against HBV-infected cells and block rcDNA from being transported to nucleus. siRNA plays a role in inhibiting HBV replication and covalently close circular DNA synthesis, providing a new strategy for the treatment of chronic hepatitis B caused by HBV infection, and a new research perspective for the complete elimination of HBV from the body.

Long noncoding RNA OR3A4 promotes cisplatin resistance of non-small cell lung cancer by upregulating CDK1

Since this article has been suspected of research misconduct and the corresponding authors did not respond to our request to prove originality of data and figures, "Long noncoding RNA OR3A4 promotes cisplatin resistance of non-small cell lung cancer by upregulating CDK1, by J. Shang, Y.-D. Xu, Y.-Y. Zhang, M. Li, published in Eur Rev Med Pharmacol Sci 2019; 23 (10): 4220-4225-DOI: 10.26355/eurrev_201905_17926-PMID: 31173293" has been withdrawn. The Publisher apologizes for any inconvenience this may cause. https://www.europeanreview.org/article/17926.

Application of a microbial consortium improves the growth of Camellia sinensis and influences the indigenous rhizosphere bacterial communities

AIMS

To investigate the role of a microbial consortium in influencing of Camellia sinensis growth and rhizosphere bacteria microbial community structure.

METHODS AND RESULTS

Based on glasshouse trials, the microbial consortium TCM was selected for a field trial. TCM significantly increased bud density (67·53%), leaf area (31·15%) and hundred-bud weight (22·5%) compared with the control treatment (P < 0·01) during 180 days. Furthermore, TCM-treated soil showed a significant increase (P < 0·05) in organic matter (60·89%), total nitrogen (66·22%), total phosphorus (3·34%), available phosphorus (3·82%), available potassium (9·24%) and 2-3 mm water-stable aggregates (77·93%). Molecular ecological network analysis of the rhizobacteria indicated an increase in modularity and the number of community, connection and nodes after TCM application. Several plant growth-promoting bacteria were categorized as hubs or indicators, such as Haliangium, Catenulispora and Gemmatimonas, and showed intensive connections with other bacteria.

CONCLUSIONS

The TCM consortium enhances the effectiveness of soil mineral nutrition, influences the indigenous rhizobacterial community, alters the rhizobacterial network structure in the rhizosphere and promotes the growth of C. sinensis.

SIGNIFICANCE AND IMPACT OF THE STUDY

The TCM growth-promoting mechanism was closely related to rhizosphere bacterial diversity; therefore, strengthening rhizobacterial interactions may help promote C. sinensis growth, which could be a sustainable approach for improving C. sinensis growth and health in tea plantations.

[Short-term effect of sodium zirconium cyclosilicate on potassium lowering in chronic kidney disease patients with hyperkalemia]

Objective: To investigate the efficacy of sodium zirconium cyclosilicate on emergency correction of hyperkalemia in chronic kidney disease patients. Methods: Patients with chronic kidney disease who were admitted to the Department of Nephrology of the First Affiliated Hospital of Zhengzhou University from May to June 2020 were selected. Those who had hyperkalemia and took sodium zirconium cyclosilicate powder were finally included. The patient's clinical data and laboratory results were collected. Results: A total of 24 results were included from 21 patients. The age of patients was (48.9±13.5) years old. Fourteen patients were male, and 7 patients were female. After 2 hours of administration, the venous potassium level decreased from (5.85±0.52) mmol/L to (5.15±0.43) mmol/L (P<0.001, n=21), with an average decline of (0.71±0.43) mmol/L. Meanwhile, the arterial potassium level decreased from (5.50±0.40) mmol/L to (4.88±0.33) mmol/L (P<0.001, n=10), with an average decline of (0.62±0.29) mmol/L. Based on the initial venous potassium level, the patients were further divided into three groups. The average potassium decrease in<5.5 mmol/L group (4 patients), 5.5-<6.0 mmol/L group (11 patients) and ≥6.0 mmol/L group (6 patients) was (0.46±0.26) mmol/L, (0.62±0.38) mmol/L and (1.04±0.45) mmol/L, respectively. There was statistically significant difference of potassium reduction among the three groups (P=0.045). Moreover, the extent of potassium reduction was positively correlated with baseline venous potassium level (r=0.603, P=0.004, n=21). The study did not reveal any treatment-related adverse event. Conclusion: Sodium zirconium cyclosilicate powder can rapidly and effectively reduce the serum potassium level in chronic kidney disease patients with hyperkalemia.

A low-cost laboratory-based method for predicting newly diagnosed biopsy-proven diabetic nephropathy in people with type 2 diabetes

AIMS

To identify significant prognostic factors for newly diagnosed biopsy-proven diabetic nephropathy using routine laboratory measures, from which to derive a low-cost explanatory model, and to use this model to examine associations between the potential low-cost test panels and the risk of diabetic nephropathy in people with type 2 diabetes with normal kidney function.

METHOD

A population-based case-control study was undertaken to test the association between diabetic nephropathy and 47 laboratory variables using a 'hypothesis-free' strategy and five routinely recorded factors in diabetes care (BMI, systolic and diastolic blood pressure, HbA_1c , fasting glucose). Factors that were significant after Bonferroni correction were included in different test panels and used to develop diabetic nephropathy (outcome) explanatory models. Models were derived using risk-set sampling among 950 biopsy-proven diabetic nephropathy cases newly diagnosed in the period between 2012 and 2018 and among 4750 age- and gender-matched controls.

RESULTS

A total of 15 Bonferroni-corrected significant laboratory predictors in the three test panels (blood cell, serum electrolytes and blood coagulation) were identified through multivariable analysis and used to develop the three explanatory models. The optimism-adjusted C-statistics and calibration slope were 0.725 (95% CI 0.723-0.728) and 0.978 (95% CI 0.912-0.999) for the blood cell model, 0.688 (95% CI 0.686-0.690) and 0.923 (95% CI 0.706-0.977) for the serum electrolytes model, 0.648 (95% CI 0.639-0.658) and 0.914 (95% CI 0.641-1.115) for the blood coagulation model, respectively.

CONCLUSIONS

A total of 15 predictors were significantly associated with newly diagnosed biopsy-proven diabetic nephropathy in type 2 diabetes. The blood cell model appeared to be the low-cost model with the best predictive ability.

LncRNA SNHG14 promotes the development of cervical cancer and predicts poor prognosis

OBJECTIVE

The aim of this study was to explore the role of long non-coding RNA (LncRNA) small nucleolar RNA host gene 14 (SNHG14) in cervical cancer, and to further understand the possible underlying mechanism.

PATIENTS AND METHODS

Quantitative Real Time-Polymerase Chain Reaction (qRT-PCR) was performed to detect the expression of SNHG14 in cervical cancer. The relationship between SNHG14 expression with clinic-pathological features and prognosis of patients was analyzed. Cell Counting Kit-8 (CCK-8), 5-Ethynyl-2'-deoxyuridine (EdU) and flow cytometry were used to evaluate the proliferation and apoptosis of cells. At the same time, the changes in the expression of apoptosis-related proteins after SNHG14 knockdown were detected.

RESULTS

Compared with normal cervical tissues, the expression of SNHG14 was significantly higher in cervical cancer tissues. The prognosis of patients with higher expression of SNHG14 was worse than those with a lower level. The relationship between the expression of SNHG14 and clinicopathological features of patients with cervical cancer was further analyzed. The results demonstrated that a higher expression level of SNHG14 indicated later tumor stage and higher incidence of lymph node metastasis. Compared with normal cervical epithelial cell line End1/E6E7, the level of SNHG14 in cervical cancer cell lines (including SW756, SiHa and HeLa) was markedly up-regulated. Among them, SW756 and SiHa cells exhibited the highest level of SNHG14. After knocking down SNHG14, the viability and proliferation ability of SW756 and SiHa cells were remarkably decreased, while cell apoptosis was increased. Subsequently, we investigated the possible underlying mechanism. The results found that the knockdown of SNHG14 enhanced the activation of caspases-3, and increased the protein expression of Bax, JAK2 and STAT3, whereas decreased the expression of Bal-2 and Bid.

CONCLUSIONS

LncRNA SNHG14 was highly expressed in cervical tumor tissues or cells, which could promote the progression of cervical cancer. Furthermore, SNHG14 might be associated with the activation of the JAK-STAT pathway.

LncRNA SNHG5 promotes cisplatin resistance in gastric cancer via inhibiting cell apoptosis

OBJECTIVE

To elucidate the function of long non-coding RNA (lncRNA) SNHG5 in cisplatin-resistant gastric cancer (GC), and its potential mechanism.

PATIENTS AND METHODS

We detected the expressions of SNHG5, apoptosis-specific genes (Bax and Bcl-2) and drug resistance-specific genes (MDR1 and MRP1) in cisplatin-sensitive and cisplatin-resistant GC patients. The expression levels were also detected in cisplatin-resistant GC cell lines (BGC823/DDP, SGC7901/DDP) and GC cell lines (BGC823 and SGC7901). Through the liposome transfection, the regulatory effects of SNHG5 on proliferative potential and apoptosis were examined by cytotoxicity assay and flow cytometry assay, respectively. The protein levels of apoptosis-related genes and drug resistance-related genes influenced by SNHG5 were detected by Western blot.

RESULTS

Compared with cisplatin-sensitive GC patients, SNHG5 expression was remarkably higher in cisplatin-resistant GC patients. Besides, higher SNHG5 expression was observed in BGC823/DDP and SGC7901/DDP cells relative to that of their parental cells. Proliferative rate (OD450) and IC50 decreased, but the apoptotic rate increased in BGC823/DDP and SGC7901/DDP cells with SNHG5 knockdown. It is found that SNHG5 overexpression reduced cisplatin sensitivity in BGC823 and SGC7901 cells. Decreased cisplatin cytotoxicity, elevated IC50 and inhibited apoptotic rate were observed in GC cells overexpressing SNHG5. Moreover, the expression levels of Bax, MDR1 and MRP1 were upregulated, while Bcl-2 downregulated in BGC823 and SGC7901 cells overexpressing SNHG5.

CONCLUSIONS

SNHG5 is highly expressed in cisplatin-resistant GC. SNHG5 promotes cisplatin resistance in GC by regulating apoptosis-related genes and drug resistance-related genes.

LncRNA DANCR accelerates the development of multidrug resistance of gastric cancer

OBJECTIVE

The development of multidrug resistance (MDR) is a key issue for tumor recurrence and metastasis, leading to treatment failure of gastric cancer (GC). Long non-coding RNA (lncRNA) DANCR has been shown to be highly expressed in GC patients, which accelerates growth and metastasis of GC cells. This study aims to elucidate the role of DANCR in regulating MDR of GC.

PATIENTS AND METHODS

The mRNA level of DANCR in GC patients with or without DDP-resistance was determined by quantitative Real-time polymerase chain reaction (qRT-PCR). DANCR expression in GC cell lines (SGC7901, BGC823) and cisplatin (DDP)-resistant cell lines (SGC7901/DDP, BGC823/DDP) was determined as well. Knockdown or overexpression of DANCR in GC cells with or without DDP-resistance was achieved by siRNA interference technology or stable transfection of lentivirus, respectively. The regulatory effects of DANCR on cytotoxicity and apoptosis were examined by cytotoxicity assay and flow cytometry method (FCM), respectively. In addition, we detected the expressions of MDR1, MRP1, mechanistic target of rapamycin (mTOR) and hypoxia inducible factor-1α (HIF-1α) in GC cells overexpressing DANCR by qRT-PCR and Western blot.

RESULTS

DANCR expression remained high in DDP-resistant GC tissues or cells. SGC7901/DDP and BGC823/DDP cells transfected with si-DANCR presented decreased survival and increased apoptosis. On the contrary, SGC7901/DDP and BGC823/DDP cells overexpressing DANCR showed increased survival and decreased apoptosis. In addition, DANCR overexpression could upregulate expressions of MDR1 and MRP1 in DDP-induced SGC901 and BGC823 cells.

CONCLUSIONS

Upregulation of DANCR can accelerate the MDR development of GC, which may become a potential target for treating GC with MDR.

[Analysis of clinical characteristics of 49 cases of COVID-19]

Objective: To analyze the epidemiological and clinical characteristics, and imaging features of patients with COVID-19 in Henan Province People's Hospital. Methods: The epidemiology, clinical symptoms, laboratory and radiologic data of 49 patients with COVID-19 infection admitted to the department of infectious disease in our hospital from January 23, 2020 to February 22, 2020 were retrospectively analyzed. All analyses were performed with SPSS software, version 22.0. Results: A total of 49 patients with COVID-19 were enrolled, of which 28 were ordinary, 16 were severe, and 5 were critical in disease severity. The average ages of the 3 groups were (46±19) , (60±16) and (68±20) years, with statistical differences (P=0.015). Common symptoms at the onset were fever (41 patients), dry cough (35 patients), and fatigue (21 patients). Epidemiological investigations found that 31 (63%) patients had direct or indirect contact with confirmed cases, and 14 cases were family clustered. Laboratory test results showed that the lymphocyte counts progressively decreased [0.85 (0.5-1.6) ×10(9)/L,0.51 (0.4-0.9) ×10(9)/L and 0.43 (0.47-0.61) ×10(9)/L, respectively], while LDH [162 (145.1-203.5) U/L,265 (195.3-288.4) U/L and 387 (312.3-415.5) U/L, respectively] and D-dimer [0.15 (0.09-0.40) mg/L,0.4 (0.2-0.6) mg/L and 0.9 (0.5-1.4) mg/L, respectively] were significantly increased (P<0.05), in all the 3 groups. The levels of IL-6 [(43.2±15.4) μg/L, (78.5±31.2) μg/L and (132.4±47.9) μg/L, respectively] and IL-10 [(3.5±3.2) μg/L, (7.6±6.4) μg/L and (9.4±7.2) μg/L respectively] increased significantly with disease severity. Pulmonary imaging of ordinary patients mainly showed unilateral or bilateral multiple infiltrates, while severe and critically ill patients showed diffuse exudation and consolidation of both lungs, and a few patients showed signs of "white lungs". Conclusions: Patients with COVID-19 has a definite history of contact with diagnosed patients, and has family aggregation. The clinical symptoms were mainly fever and dry cough. Laboratory results showed that lymphocyte count, LDH, D-dimer, interleukin-6 and interleukin-10 levels had a significant correlation with the severity of the disease, which could be used as markers for disease progression and prognosis. Pulmonary imaging showed unilateral or bilateral ground glass infiltration. In severe and critically ill patients, diffuse infiltration and consolidation or even "white lung" were present.

Biological Toxicity Effects of Soil Pollution Caused by Galvanized Wastewater Based on Vibrio Qinghaiensis sp.-Q67

Objective To establish a rapid diagnosis method for the biological toxicity of soil, accurately and rapidly evaluate the toxicity of contaminated sites and identify the dominant pollutants. Methods Take the soil pollution of a galvanized factory as an example, while the metal concentration level was analyzed and detected, a rapid biological toxicity detection method based on the acute toxicity test of luminescent bacteria （Vibrio qinghaiensis sp.-Q67） was established, and the dominant pollutants were identified by stepwise multiple regression. Results The pollutants came from wastewater and metal plating fragments directly discharged from the manufacturing line of the factory. The concentration of those pollutants was correlated with the acute toxicity of Vibrio qinghaiensis sp.-Q67. The dominant pollutants in the study were zinc （Zn）, aluminum （Al） and copper （Cu）. Conclusion The luminescent bacteria toxicity test method based on Vibrio qinghaiensis sp.-Q67 can conveniently and rapidly assess the degree of toxic damage of polluted soil and identify the dominant pollutants and can be applied to the acute toxicity evaluation of polluted soil.

Effect of miR-34a on resistance to sunitinib in breast cancer by regulating the Wnt/β-catenin signaling pathway

OBJECTIVE

The aim of this study was to investigate the influence of micro ribonucleic acid (miR)-34a on resistance to sunitinib in breast cancer, and to explore its possible underlying mechanism.

MATERIALS AND METHODS

Breast cancer MCF-7 cells were transfected with miR-34a inhibitor or mimics to downregulate or upregulate the expression of miR-34a. Then, the transfected cells were treated with sunitinib. Next, transwell assay was applied to detect the changes in cell invasion ability. Cell viability was measured via cell counting kit-8 (CCK8) assay. Dual-Luciferase reporter gene assay was employed to determine the interaction between miR-34a and the Wnt/β-catenin signaling pathway. The immunoblotting assay was used to measure the expression changes of proteins in the pathway.

RESULTS

The overexpression of miR-34a significantly reduced the invasive ability of MCF-7 cells after treatment with sunitinib. After miR-34a expression was downregulated, the sensitivity of MCF-7 cells to sunitinib was significantly lowered. MiR-34a interacted with the 3'-untranslated region (3'-UTR) on Wnt1. Meanwhile, the overexpression of miR-34a remarkably downregulated the messenger RNA (mRNA) and the protein levels of Wnt1, whereas upregulated the expressions of Wnt1 and β-catenin.

CONCLUSIONS

MiR-34a affects the sensitivity to sunitinib in breast cancer by regulating the Wnt/β-catenin signaling pathway.

CHEER: HierarCHical taxonomic classification for viral mEtagEnomic data via deep leaRning

The fast accumulation of viral metagenomic data has contributed significantly to new RNA virus discovery. However, the short read size, complex composition, and large data size can all make taxonomic analysis difficult. In particular, commonly used alignment-based methods are not ideal choices for detecting new viral species. In this work, we present a novel hierarchical classification model named CHEER, which can conduct read-level taxonomic classification from order to genus for new species. By combining k-mer embedding-based encoding, hierarchically organized CNNs, and carefully trained rejection layer, CHEER is able to assign correct taxonomic labels for reads from new species. We tested CHEER on both simulated and real sequencing data. The results show that CHEER can achieve higher accuracy than popular alignment-based and alignment-free taxonomic assignment tools. The source code, scripts, and pre-trained parameters for CHEER are available via GitHub:https://github.com/KennthShang/CHEER.

[Analysis of clinical features of 29 patients with 2019 novel coronavirus pneumonia]

Objective: To analyze the clinical characteristics of 2019 novel coronavirus (2019-nCoV) pneumonia and to investigate the correlation between serum inflammatory cytokines and severity of the disease. Methods: 29 patients with 2019-ncov admitted to the isolation ward of Tongji hospital affiliated to Tongji medical college of Huazhong University of Science and Technology in January 2020 were selected as the study subjects. Clinical data were collected and the general information, clinical symptoms, blood test and CT imaging characteristics were analyzed. According to the relevant diagnostic criteria, the patients were divided into three groups: mild (15 cases), severe (9 cases) and critical (5 cases). The expression levels of inflammatory cytokines and other markers in the serum of each group were detected, and the changes of these indicators of the three groups were compared and analyzed, as well as their relationship with the clinical classification of the disease. Results: (1) The main symptoms of 2019-nCoV pneumonia was fever (28/29) with or without respiratory and other systemic symptoms. Two patients died with underlying disease and co-bacterial infection, respectively. (2) The blood test of the patients showed normal or decreased white blood cell count (23/29), decreased lymphocyte count (20/29), increased hypersensitive C reactive protein (hs-CRP) (27/29), and normal procalcitonin. In most patients,serum lactate dehydrogenase (LDH) was significantly increased (20/29), while albumin was decreased(15/29). Alanine aminotransferase (ALT), aspartate aminotransferase (AST), total bilirubin (Tbil), serum creatinine (Scr) and other items showed no significant changes. (3) CT findings of typical cases were single or multiple patchy ground glass shadows accompanied by septal thickening. When the disease progresses, the lesion increases and the scope expands, and the ground glass shadow coexists with the solid shadow or the stripe shadow. (4) There were statistically significant differences in the expression levels of interleukin-2 receptor (IL-2R) and IL-6 in the serum of the three groups (P<0.05), among which the critical group was higher than the severe group and the severe group was higher than the mildgroup. However, there were no statistically significant differences in serum levels of tumor necrosis factor-alpha (TNF-α), IL-1, IL-8, IL-10, hs-CRP, lymphocyte count and LDH among the three groups (P>0.05). Conclusion: The clinical characteristics of 2019-nCoV pneumonia are similar to those of common viral pneumonia. High resolution CT is of great value in the differential diagnosis of this disease. The increased expression of IL-2R and IL-6 in serum is expected to predict the severity of the 2019-nCoV pneumonia and the prognosis of patients.

[Analysis of clinical features of 29 patients with 2019 novel coronavirus pneumonia]

Objective: To analyze the clinical characteristics of 2019 novel coronavirus (2019-nCoV) pneumonia and to investigate the correlation between serum inflammatory cytokines and severity of the disease. Methods: 29 patients with 2019-ncov admitted to the isolation ward of Tongji hospital affiliated to Tongji medical college of Huazhong University of Science and Technology in January 2020 were selected as the study subjects. Clinical data were collected and the general information, clinical symptoms, blood test and CT imaging characteristics were analyzed. According to the relevant diagnostic criteria, the patients were divided into three groups: mild (15 cases), severe (9 cases) and critical (5 cases). The expression levels of inflammatory cytokines and other markers in the serum of each group were detected, and the changes of these indicators of the three groups were compared and analyzed, as well as their relationship with the clinical classification of the disease. Results: (1) The main symptoms of 2019-nCoV pneumonia was fever (28/29) with or without respiratory and other systemic symptoms. Two patients died with underlying disease and co-bacterial infection, respectively. (2) The blood test of the patients showed normal or decreased white blood cell count (23/29), decreased lymphocyte count (20/29), increased hypersensitive C reactive protein (hs-CRP) (27/29), and normal procalcitonin. In most patients,serum lactate dehydrogenase (LDH) was significantly increased (20/29), while albumin was decreased(15/29). Alanine aminotransferase (ALT), aspartate aminotransferase (AST), total bilirubin (Tbil), serum creatinine (Scr) and other items showed no significant changes. (3) CT findings of typical cases were single or multiple patchy ground glass shadows accompanied by septal thickening. When the disease progresses, the lesion increases and the scope expands, and the ground glass shadow coexists with the solid shadow or the stripe shadow. (4) There were statistically significant differences in the expression levels of interleukin-2 receptor (IL-2R) and IL-6 in the serum of the three groups (P<0.05), among which the critical group was higher than the severe group and the severe group was higher than the mildgroup. However, there were no statistically significant differences in serum levels of tumor necrosis factor-alpha (TNF-α), IL-1, IL-8, IL-10, hs-CRP, lymphocyte count and LDH among the three groups (P>0.05). Conclusion: The clinical characteristics of 2019-nCoV pneumonia are similar to those of common viral pneumonia. High resolution CT is of great value in the differential diagnosis of this disease. The increased expression of IL-2R and IL-6 in serum is expected to predict the severity of the 2019-nCoV pneumonia and the prognosis of patients.