Innervation of nociceptor neurons in the spleen promotes germinal center responses and humoral immunity.

Peripheral sensory neurons widely innervate various tissues to continuously monitor and respond to environmental stimuli. Whether peripheral sensory neurons innervate the spleen and modulate splenic immune response remains poorly defined. Here, we demonstrate that nociceptive sensory nerve fibers extensively innervate the spleen along blood vessels and reach B cell zones. The spleen-innervating nociceptors predominantly originate from left T8-T13 dorsal root ganglia (DRGs), promoting the splenic germinal center (GC) response and humoral immunity. Nociceptors can be activated by antigen-induced accumulation of splenic prostaglandin E2 (PGE2) and then release calcitonin gene-related peptide (CGRP), which further promotes the splenic GC response at the early stage. Mechanistically, CGRP directly acts on B cells through its receptor CALCRL-RAMP1 via the cyclic AMP (cAMP) signaling pathway. Activating nociceptors by ingesting capsaicin enhances the splenic GC response and anti-influenza immunity. Collectively, our study establishes a specific DRG-spleen sensory neural connection that promotes humoral immunity, suggesting a promising approach for improving host defense by targeting the nociceptive nervous system.

GADRP: graph convolutional networks and autoencoders for cancer drug response prediction.

Drug response prediction in cancer cell lines is of great significance in personalized medicine. In this study, we propose GADRP, a cancer drug response prediction model based on graph convolutional networks (GCNs) and autoencoders (AEs). We first use a stacked deep AE to extract low-dimensional representations from cell line features, and then construct a sparse drug cell line pair (DCP) network incorporating drug, cell line, and DCP similarity information. Later, initial residual and layer attention-based GCN (ILGCN) that can alleviate over-smoothing problem is utilized to learn DCP features. And finally, fully connected network is employed to make prediction. Benchmarking results demonstrate that GADRP can significantly improve prediction performance on all metrics compared with baselines on five datasets. Particularly, experiments of predictions of unknown DCP responses, drug-cancer tissue associations， and drug-pathway associations illustrate the predictive power of GADRP. All results highlight the effectiveness of GADRP in predicting drug responses, and its potential value in guiding anti-cancer drug selection.

DTI-HETA: prediction of drug-target interactions based on GCN and GAT on heterogeneous graph.

Drug-target interaction (DTI) prediction plays an important role in drug repositioning, drug discovery and drug design. However, due to the large size of the chemical and genomic spaces and the complex interactions between drugs and targets, experimental identification of DTIs is costly and time-consuming. In recent years, the emerging graph neural network (GNN) has been applied to DTI prediction because DTIs can be represented effectively using graphs. However, some of these methods are only based on homogeneous graphs, and some consist of two decoupled steps that cannot be trained jointly. To further explore GNN-based DTI prediction by integrating heterogeneous graph information, this study regards DTI prediction as a link prediction problem and proposes an end-to-end model based on HETerogeneous graph with Attention mechanism (DTI-HETA). In this model, a heterogeneous graph is first constructed based on the drug-drug and target-target similarity matrices and the DTI matrix. Then, the graph convolutional neural network is utilized to obtain the embedded representation of the drugs and targets. To highlight the contribution of different neighborhood nodes to the central node in aggregating the graph convolution information, a graph attention mechanism is introduced into the node embedding process. Afterward, an inner product decoder is applied to predict DTIs. To evaluate the performance of DTI-HETA, experiments are conducted on two datasets. The experimental results show that our model is superior to the state-of-the-art methods. Also, the identification of novel DTIs indicates that DTI-HETA can serve as a powerful tool for integrating heterogeneous graph information to predict DTIs.

An enhanced cascade-based deep forest model for drug combination prediction.

Combination therapy has shown an obvious curative effect on complex diseases, whereas the search space of drug combinations is too large to be validated experimentally even with high-throughput screens. With the increase of the number of drugs, artificial intelligence techniques, especially machine learning methods, have become applicable for the discovery of synergistic drug combinations to significantly reduce the experimental workload. In this study, in order to predict novel synergistic drug combinations in various cancer cell lines, the cell line-specific drug-induced gene expression profile (GP) is added as a new feature type to capture the cellular response of drugs and reveal the biological mechanism of synergistic effect. Then, an enhanced cascade-based deep forest regressor (EC-DFR) is innovatively presented to apply the new small-scale drug combination dataset involving chemical, physical and biological (GP) properties of drugs and cells. Verified by the dataset, EC-DFR outperforms two state-of-the-art deep neural network-based methods and several advanced classical machine learning algorithms. Biological experimental validation performed subsequently on a set of previously untested drug combinations further confirms the performance of EC-DFR. What is more prominent is that EC-DFR can distinguish the most important features, making it more interpretable. By evaluating the contribution of each feature type, GP feature contributes 82.40%, showing the cellular responses of drugs may play crucial roles in synergism prediction. The analysis based on the top contributing genes in GP further demonstrates some potential relationships between the transcriptomic levels of key genes under drug regulation and the synergism of drug combinations.

Machine learning methods, databases and tools for drug combination prediction.

Combination therapy has shown an obvious efficacy on complex diseases and can greatly reduce the development of drug resistance. However, even with high-throughput screens, experimental methods are insufficient to explore novel drug combinations. In order to reduce the search space of drug combinations, there is an urgent need to develop more efficient computational methods to predict novel drug combinations. In recent decades, more and more machine learning (ML) algorithms have been applied to improve the predictive performance. The object of this study is to introduce and discuss the recent applications of ML methods and the widely used databases in drug combination prediction. In this study, we first describe the concept and controversy of synergism between drug combinations. Then, we investigate various publicly available data resources and tools for prediction tasks. Next, ML methods including classic ML and deep learning methods applied in drug combination prediction are introduced. Finally, we summarize the challenges to ML methods in prediction tasks and provide a discussion on future work.

Computational methods, databases and tools for synthetic lethality prediction.

Synthetic lethality (SL) occurs between two genes when the inactivation of either gene alone has no effect on cell survival but the inactivation of both genes results in cell death. SL-based therapy has become one of the most promising targeted cancer therapies in the last decade as PARP inhibitors achieve great success in the clinic. The key point to exploiting SL-based cancer therapy is the identification of robust SL pairs. Although many wet-lab-based methods have been developed to screen SL pairs, known SL pairs are less than 0.1% of all potential pairs due to large number of human gene combinations. Computational prediction methods complement wet-lab-based methods to effectively reduce the search space of SL pairs. In this paper, we review the recent applications of computational methods and commonly used databases for SL prediction. First, we introduce the concept of SL and its screening methods. Second, various SL-related data resources are summarized. Then, computational methods including statistical-based methods, network-based methods, classical machine learning methods and deep learning methods for SL prediction are summarized. In particular, we elaborate on the negative sampling methods applied in these models. Next, representative tools for SL prediction are introduced. Finally, the challenges and future work for SL prediction are discussed.

Acute lung injury: a view from the perspective of necroptosis.

BACKGROUND: ALI/ARDS is a syndrome of acute onset characterized by progressive hypoxemia and noncardiogenic pulmonary edema as the primary clinical manifestations. Necroptosis is a form of programmed cell necrosis that is precisely regulated by molecular signals. This process is characterized by organelle swelling and membrane rupture, is highly immunogenic, involves extensive crosstalk with various cellular stress mechanisms, and is significantly implicated in the onset and progression of ALI/ARDS. METHODS: The current body of literature on necroptosis and ALI/ARDS was thoroughly reviewed. Initially, an overview of the molecular mechanism of necroptosis was provided, followed by an examination of its interactions with apoptosis, pyroptosis, autophagy, ferroptosis, PANOptosis, and NETosis. Subsequently, the involvement of necroptosis in various stages of ALI/ARDS progression was delineated. Lastly, drugs targeting necroptosis, biomarkers, and current obstacles were presented. CONCLUSION: Necroptosis plays an important role in the progression of ALI/ARDS. However, since ALI/ARDS is a clinical syndrome caused by a variety of mechanisms, we emphasize that while focusing on necroptosis, it may be more beneficial to treat ALI/ARDS by collaborating with other mechanisms.

[Longitudinal study on catch-up growth in preterm infants with small for gestational age at corrected ages 0-24 months]. / æ—©äº§å°äºŽèƒŽé¾„å„¿æ ¡æ­£0~24æœˆé¾„è¿½èµ¶ç”Ÿé•¿çš„çºµå‘ç ”ç©¶.

OBJECTIVES: To understand the growth and development status and differences between small for gestational age (SGA) and appropriate for gestational age (AGA) preterm infants during corrected ages 0-24 months, and to provide a basis for early health interventions for preterm infants. METHODS: A retrospective study was conducted, selecting 824 preterm infants who received regular health care at the Guangzhou Women and Children's Medical Center from July 2019 to July 2022, including 144 SGA and 680 AGA infants. The growth data of SGA and AGA groups at birth and corrected ages 0-24 months were analyzed and compared. RESULTS: The SGA group had significantly lower weight and length than the AGA group at corrected ages 0-18 months (P<0.05), while there were no significant differences between the two groups at corrected age 24 months (P>0.05). At corrected age 24 months, 85% (34/40) of SGA and 79% (74/94) of AGA preterm infants achieved catch-up growth. Stratified analysis by gestational age showed that there were significant differences in weight and length at corrected ages 0-9 months between the SGA subgroup with gestational age <34 weeks and the AGA subgroups with gestational age <34 weeks and 34 weeks (P<0.05). In addition, the weight and length of the SGA subgroup with gestational age 34 weeks showed significant differences compared to the AGA subgroups with gestational age <34 weeks and 34 weeks at corrected ages 0-18 months and corrected ages 0-12 months, respectively (P<0.05). Catch-up growth for SGA infants with gestational age <34 weeks and 34 weeks mainly occurred at corrected ages 0-12 months and corrected ages 0-18 months, respectively. CONCLUSIONS: SGA infants exhibit delayed early-life physical growth compared to AGA infants, but can achieve a higher proportion of catch-up growth by corrected age 24 months than AGA infants. Catch-up growth can be achieved earlier in SGA infants with a gestational age of <34 weeks compared to those with 34 weeks.

A splicing factor switch controls hematopoietic lineage specification of pluripotent stem cells.

Alternative splicing (AS) leads to transcriptome diversity in eukaryotic cells and is one of the key regulators driving cellular differentiation. Although AS is of crucial importance for normal hematopoiesis and hematopoietic malignancies, its role in early hematopoietic development is still largely unknown. Here, by using high-throughput transcriptomic analyses, we show that pervasive and dynamic AS takes place during hematopoietic development of human pluripotent stem cells (hPSCs). We identify a splicing factor switch that occurs during the differentiation of mesodermal cells to endothelial progenitor cells (EPCs). Perturbation of this switch selectively impairs the emergence of EPCs and hemogenic endothelial progenitor cells (HEPs). Mechanistically, an EPC-induced alternative spliced isoform of NUMB dictates EPC specification by controlling NOTCH signaling. Furthermore, we demonstrate that the splicing factor SRSF2 regulates splicing of the EPC-induced NUMB isoform, and the SRSF2-NUMB-NOTCH splicing axis regulates EPC generation. The identification of this splicing factor switch provides a new molecular mechanism to control cell fate and lineage specification.

A Genetic Algorithm-Based Ensemble Learning Framework for Drug Combination Prediction.

Combination therapy is a promising clinical treatment strategy for cancer and other complex diseases. Multiple drugs can target multiple proteins and pathways, greatly improving the therapeutic effect and slowing down drug resistance. To narrow the search space of synergistic drug combinations, many prediction models have been developed. However, drug combination datasets always have the characteristics of class imbalance. Synergistic drug combinations receive the most attention in clinical application but are in small numbers. To predict synergistic drug combinations in different cancer cell lines, in this study, we propose a genetic algorithm-based ensemble learning framework, GA-DRUG, to address the problems of class imbalance and high dimensionality of input data. The cell-line-specific gene expression profiles under drug perturbations are used to train GA-DRUG, which contains imbalanced data processing and the search of global optimal solutions. Compared to 11 state-of-the-art algorithms, GA-DRUG achieves the best performance and significantly improves the prediction performance in the minority class (Synergy). The ensemble framework can effectively correct the classification results of a single classifier. In addition, the cellular proliferation experiment performed on several previously unexplored drug combinations further confirms the predictive ability of GA-DRUG.

DEML: Drug Synergy and Interaction Prediction Using Ensemble-Based Multi-Task Learning.

Synergistic drug combinations have demonstrated effective therapeutic effects in cancer treatment. Deep learning methods accelerate identification of novel drug combinations by reducing the search space. However, potential adverse drug-drug interactions (DDIs), which may increase the risks for combination therapy, cannot be detected by existing computational synergy prediction methods. We propose DEML, an ensemble-based multi-task neural network, for the simultaneous optimization of five synergy regression prediction tasks, synergy classification, and DDI classification tasks. DEML uses chemical and transcriptomics information as inputs. DEML adapts the novel hybrid ensemble layer structure to construct higher order representation using different perspectives. The task-specific fusion layer of DEML joins representations for each task using a gating mechanism. For the Loewe synergy prediction task, DEML overperforms the state-of-the-art synergy prediction method with an improvement of 7.8% and 13.2% for the root mean squared error and the R2 correlation coefficient. Owing to soft parameter sharing and ensemble learning, DEML alleviates the multi-task learning 'seesaw effect' problem and shows no performance loss on other tasks. DEML has a superior ability to predict drug pairs with high confidence and less adverse DDIs. DEML provides a promising way to guideline novel combination therapy strategies for cancer treatment.

PDIA4 promotes glioblastoma progression via the PI3K/AKT/m-TOR pathway.

Protein disulfide isomerase A4 (PDIA4) is highly expressed in clear cell ovarian carcinoma and lung cancer. Through analysis of TCGA database and CGGA database, we noted that PDIA4 is a key promotor of glioblastoma (GBM). However, the detailed role and molecular mechanism of PDIA4 in GBM remain unclear. In this study, the expression pattern and biological role of PDIA4 in GBM was investigated. PDIA4 was overexpressed in GBM tumor samples and cell lines and positively correlated with pathological grades in glioma patients. In addition, downregulation of PDIA4 promoted apoptosis and inhibited proliferation of GBM. Meanwhile, there was a concurrent decrease in aerobic glycolysis metabolites. Mechanistically, PDIA4 downregulation promoted the apoptosis of GBM cells by increased the expression of apoptosis pathway proteins (caspase 3, caspase 9 and Bax). Downregulation of PDIA4 decreased energy demand and inhibited GBM growth in vitro and in vivo. Besides, such effect also inhibited the PI3K/AKT/m-TOR pathway by inhibiting protein phosphorylation levels of PI3K, AKT and m-TOR. After addition of PI3K/AKT/mTOR pathway activator 740Y-P, the effect of PDIA4 knockdown on GBM was reversed. Therefore, we believe that PDIA4 regulates the proliferation via activating the PI3K/AKT/m-TOR pathway and suppression of apoptosis in glioblastoma. It could be used as a potential target for the treatment of GBM.

NeRD: a multichannel neural network to predict cellular response of drugs by integrating multidimensional data.

BACKGROUND: Considering the heterogeneity of tumors, it is a key issue in precision medicine to predict the drug response of each individual. The accumulation of various types of drug informatics and multi-omics data facilitates the development of efficient models for drug response prediction. However, the selection of high-quality data sources and the design of suitable methods remain a challenge. METHODS: In this paper, we design NeRD, a multidimensional data integration model based on the PRISM drug response database, to predict the cellular response of drugs. Four feature extractors, including drug structure extractor (DSE), molecular fingerprint extractor (MFE), miRNA expression extractor (mEE), and copy number extractor (CNE), are designed for different types and dimensions of data. A fully connected network is used to fuse all features and make predictions. RESULTS: Experimental results demonstrate the effective integration of the global and local structural features of drugs, as well as the features of cell lines from different omics data. For all metrics tested on the PRISM database, NeRD surpassed previous approaches. We also verified that NeRD has strong reliability in the prediction results of new samples. Moreover, unlike other algorithms, when the amount of training data was reduced, NeRD maintained stable performance. CONCLUSIONS: NeRD's feature fusion provides a new idea for drug response prediction, which is of great significance for precise cancer treatment.

COMSUC: A web server for the identification of consensus molecular subtypes of cancer based on multiple methods and multi-omics data.

Extensive amounts of multi-omics data and multiple cancer subtyping methods have been developed rapidly, and generate discrepant clustering results, which poses challenges for cancer molecular subtype research. Thus, the development of methods for the identification of cancer consensus molecular subtypes is essential. The lack of intuitive and easy-to-use analytical tools has posed a barrier. Here, we report on the development of the COnsensus Molecular SUbtype of Cancer (COMSUC) web server. With COMSUC, users can explore consensus molecular subtypes of more than 30 cancers based on eight clustering methods, five types of omics data from public reference datasets or users' private data, and three consensus clustering methods. The web server provides interactive and modifiable visualization, and publishable output of analysis results. Researchers can also exchange consensus subtype results with collaborators via project IDs. COMSUC is now publicly and freely available with no login requirement at http://comsuc.bioinforai.tech/ (IP address: http://59.110.25.27/). For a video summary of this web server, see S1 Video and S1 File.

Expression and function analysis of microRNA in collagen-induced arthritis rats. / MicroRNAåœ¨èƒ¶åŽŸè¯±å¯¼åž‹å ³èŠ‚ç‚Žå¤§é¼ ä¸­çš„å¼‚å¸¸è¡¨è¾¾åŠåŠŸèƒ½åˆ†æž.

OBJECTIVES: Rheumatoid arthritis is a common autoimmune disease, and microRNAs (miRNAs) are involved in its pathogenesis. This study aims to examine the differentially expressed miRNAs in collagen-induced arthritis (CIA) rats, to analyze the biological functions and the related pathways of the miRNA target genes. METHODS: The total RNA in the synovium of experimental animals was extracted. The miRNA gene profile was obtained by miRNA microarray. Then the differentially expressed miRNAs were screened and the relevant target mRNAs were predicted. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed on the significantly differentially expressed miRNAs. RESULTS: There were 69 differentially expressed miRNAs including rno-miR-6215 and rno-miR-709 in CIA rats, of which 22 (31.9%) were up-regulated and 47 (68.1%) were down-regulated. GO and KEGG enrichment analysis showed that the up-regulated miRNA target genes were mainly enriched in cellular metabolism, and they were involved in MAPK and Wnt signaling pathways. The down-regulated miRNA target genes were mainly enriched in nervous system development, and they were involved in axon guidance signaling pathway. CONCLUSIONS: There are differentially expressed miRNAs in the CIA rat model, which may be involved in metabolism biological functions and signal pathways such as MAPK and Wnt.

Multi-dimensional data integration algorithm based on random walk with restart.

BACKGROUND: The accumulation of various multi-omics data and computational approaches for data integration can accelerate the development of precision medicine. However, the algorithm development for multi-omics data integration remains a pressing challenge. RESULTS: Here, we propose a multi-omics data integration algorithm based on random walk with restart (RWR) on multiplex network. We call the resulting methodology Random Walk with Restart for multi-dimensional data Fusion (RWRF). RWRF uses similarity network of samples as the basis for integration. It constructs the similarity network for each data type and then connects corresponding samples of multiple similarity networks to create a multiplex sample network. By applying RWR on the multiplex network, RWRF uses stationary probability distribution to fuse similarity networks. We applied RWRF to The Cancer Genome Atlas (TCGA) data to identify subtypes in different cancer data sets. Three types of data (mRNA expression, DNA methylation, and microRNA expression data) are integrated and network clustering is conducted. Experiment results show that RWRF performs better than single data type analysis and previous integrative methods. CONCLUSIONS: RWRF provides powerful support to users to decipher the cancer molecular subtypes, thus may benefit precision treatment of specific patients in clinical practice.

Comparisons of microbiological characteristics and antibiotic resistance of Klebsiella pneumoniae isolates from urban rodents, shrews, and healthy people.

BACKGROUND: The comparisons of molecular characterization and antibiotic resistance of Klebsiella pneumoniae (KP) isolates from humans and other animal hosts are not well studied. Our goal was to compare the molecular epidemiology of KP strains that were isolated from urban rodents, shrews, and healthy people. RESULTS: K. pneumoniae (KP) isolates were isolated from fecal samples of rodents, shrews and healthy adults in 2015 in southern China. In total, 465 fecal samples were collected, of which 85 from rodents, 105 from shrews, and 275 from healthy adults. Antimicrobial susceptibility and production of extended-spectrum ß-lactamases (ESBL) of the isolates were tested. PCR-based methods were used to detect specific genes, including ESBL genes (blaTEM, blaSHV, and blaCTX-M) in ESBL-producing isolates, capsular serotypes (K1, K2, K5, K20, K54, and K57) in hypervirulent KPs (hvKPs), and virulence genes (magA, wcaG, rmpA, uge, kfu, and aerobactin) in hvKP isolates. Multilocus sequence type (MLST) and pulsed-field gel electrophoresis (PFGE) were performed to exclude the homology of these isolates. The carriage rate of KP in urban rodents and shrews (78.42%) was higher than that in healthy adults (66.18%) (χ2 = 8.206, P = 0.004). The prevalence rates of ESBL-producing isolates among rodents, shrews, and humans were 7.94, 12.79, and 17.03%, respectively. The positive rates of CTX-M, TEM and SHV types in ESBL-producing isolates were 29.79, 27.66, and 17.02%, respectively. Serotype K1, K5, K20, and K57 were detected in both small mammals and humans. PFGE typing revealed thirty-six clusters. PFGE cluster A was clustered by samples of shrews and healthy adult, with a similarity of 88.4%. MLST typing revealed thirty-eight types. ST23 and ST35 were detected in samples of shrews and healthy adults. ST37 was detected in samples of 2 rodents and a healthy adult. CONCLUSIONS: Overlapping serotypes of hvKP were observed in both the animals and humans. The same PFGE or MLST types were also found in isolates derived humans, rodents and shrews. Therefore, urban rodents and shrews might play a certain role in the transmission of drug-resistant and hypervirulent KP.

Composition of gut and oropharynx bacterial communities in Rattus norvegicus and Suncus murinus in China.

BACKGROUND: Rattus norvegicus and Suncus murinus are important reservoirs of zoonotic bacterial diseases. An understanding of the composition of gut and oropharynx bacteria in these animals is important for monitoring and preventing such diseases. We therefore examined gut and oropharynx bacterial composition in these animals in China. RESULTS: Proteobacteria, Firmicutes and Bacteroidetes were the most abundant phyla in faecal and throat swab samples of both animals. However, the composition of the bacterial community differed significantly between sample types and animal species. Firmicutes exhibited the highest relative abundance in throat swab samples of R. norvegicus, followed by Proteobacteria and Bacteroidetes. In throat swab specimens of S. murinus, Proteobacteria was the predominant phylum, followed by Firmicutes and Bacteroidetes. Firmicutes showed the highest relative abundance in faecal specimens of R. norvegicus, followed by Bacteroidetes and Proteobacteria. Firmicutes and Proteobacteria had almost equal abundance in faecal specimens of S. murinus, with Bacteroidetes accounting for only 3.07%. The family Streptococcaceae was most common in throat swab samples of R. norvegicus, while Prevotellaceae was most common in its faecal samples. Pseudomonadaceae was the predominant family in throat swab samples of S. murinus, while Enterobacteriaceae was most common in faecal samples. We annotated 33.28% sequences from faecal samples of S. murinus as potential human pathogenic bacteria, approximately 3.06-fold those in R. norvegicus. Potential pathogenic bacteria annotated in throat swab samples of S. murinus were 1.35-fold those in R. norvegicus. CONCLUSIONS: Bacterial composition of throat swabs and faecal samples from R. norvegicus differed from those of S. murinus. Both species carried various pathogenic bacteria, therefore both should be closely monitored in the future, especially for S. murinus.

Trends in urban/rural inequalities in physical growth among Chinese children over three decades of urbanization in Guangzhou: 1985-2015.

BACKGROUND: Great growth inequalities between urban and rural areas have been reported in China over the past years. By examining urban/rural inequalities in physical growth among children < 7 years old over the past three decades from 1985 to 2015 in Guangzhou, we analyzed altering trends of anthropometric data in children and their association with economic development during the period of rapid urbanization in Guangzhou. METHODS: The height, body weight and nutrition status of children under 7 years old were obtained from two successive cross-sectional surveys and one health surveillance system. Student's t-test, Spearman's rank-order correlation and polynomial regression were used to assess the difference in physical growth between children in urban and rural areas and the association between socioeconomic index and secular growth changes. RESULTS: A height and weight difference was found between urban and rural children aged 0-6 years during the first two decades of our research (1985-2005), which gradually narrowed in both sex groups over time. By the end of 2015, elder boys (age group ≥5 year) and girls (age group ≥4 year) in rural areas were taller than their counterparts in urban areas (p < 0.05).The same trend could be witnessed in the weight of children aged 6 years, with a - 1.30 kg difference (P = 0.03) for boys, and a - 0.05 difference (P = 0.82) for girls. When GDP increased, the gap in boys' weight-for-age z-score (WAZ from 0.25 to 0.01) and height-for-age z-score (HAZ from 0.55 to 0.03) between urban and rural areas diminished, and disappeared when the GDP per capita (USD) approached 25,000. In either urban or rural areas, the urbanization rate and GDP were positively associated with the prevalence of obesity (all R > 0.90 with P < 0.05) and negatively correlated with the prevalence of stunted growth (all R < -0.87 with P < 0.05). CONCLUSION: Growth inequalities gradually decreased with economic development and urbanization, while new challenges such as obesity emerged. To eliminate health problems due to catch-up growth among rural children, comprehensive intervention programs for early child growth should be promoted in rural areas.

A Molecular Epidemiological Investigation of Carriage of the Adeno-Associated Virus in Murine Rodents and House Shrews in China.

OBJECTIVE: To investigate the prevalence of the adeno-associated virus (AAV) in murine rodents and house shrews in 4 provinces of China. METHODS: A total of 469 murine rodents and 19 house shrews were captured between May 2015 and May 2017. Cap gene of AAV sequences was obtained to evaluate the genetic characteristics of rat AAV. RESULTS: Rat AAVs were found in 54.7% (267/488) of throat swabs, 14.3% (70/488) of fecal samples, and 18.4% (41/223) of serum samples. Rat AAVs were detected in 3 species of murine rodents including Rattus norvegicus (34.8%), R. tanezumi (43.0%), and R. losea (2.3%), and house shrews (Suncus murinus) (26.1%) from the selected sampling sites. Fourteen near-full-length Cap gene sequences, ranging in length from 2,156 to 2,169 nt, were isolated from the fecal samples of R. norvegicus and R. tanezumi. These 14 sequences shared a high identity of 97.4% at the nucleotide level and 99.1% at the amino acid level. Phylogenetic analysis showed that the rat AAV formed a distinct clade, distinguishable from the AAV discovered in humans and in other animals. CONCLUSIONS: A high prevalence of rat AAV that was highly conserved within the Cap gene was found in 3 common murine rodents and house shrews in China.