Discovery of Oogenesis Biomarkers from Mouse Oocytes Using a Single-Cell Proteomics Approach.

We established an efficient and simplified single-cell proteomics (ES-SCP) workflow to realize proteomics profiling at the single-oocyte level. With the ES-SCP workflow, we constructed a deep coverage proteome library during oocyte maturation, which contained more than 6000 protein groups, and identified and quantified more than 4000 protein groups from a pool of only 15 oocytes at germinal vesicle (GV), GV breakdown (GVBD), and metaphase II (MII) stages. More than 1500 protein groups can be identified from single oocytes. We found that marker proteins including maternal factors and mRNA regulators, such as ZAR1, TLE6, and BTG4, showed significant variations in abundance during oocyte maturation, and it was discovered that maternal mRNA degradation was indispensable during oocyte maturation. Proteomics analysis from single oocytes revealed that changes in antioxidant factors, maternal factors, mRNA stabilization, and energy metabolism were the factors that affect the oocyte quality during ovary aging. Our data laid the foundation for future innovations in assisted reproduction.

In vivo promotion of primordial follicle activation by stem cell factor treatment in mice with premature ovarian insufficiency and advanced age.

Dormant primordial follicles (PFs) are the most abundant reproductive resource in mammalian ovaries. With advances in the mechanism of study of the regulation of PF activation, PFs have been used to improve fertility in clinical practice. As a central controlling element of follicle activation signaling, the pre-granulosa cell-secreted stem cell factor (SCF; also known as KIT ligand, KITL), which initiates the growth of dormant oocytes, is an ideal natural activator that stimulates follicle activation. However, no systematic study has been conducted to identify the activating effect of SCF in vivo and in vitro. In this study, by combining an in vitro whole ovary culture system and several mouse models, we provide a series of experimental evidence that SCF is an efficient activator for improving PF activation in mouse ovaries. Our in vitro study showed that SCF increased phosphatidylinositol 3-kinase (PI3K) signaling and PF activation ratio in neonatal ovaries. In vivo ovarian non-invasive topical administrations of SCF to the ovaries efficiently improved follicle activation and development, oocyte retrieval ratio and fertility in inducible premature ovarian insufficiency mouse models and aged mice. Our study suggests that SCF is an efficient growth factor that can be applied to improve PF activation.

Structure-aware siamese graph neural networks for encounter-level patient similarity learning.

Patient similarity learning has attracted great research interest in biomedical informatics. Correctly identifying the similarity between a given patient and patient records in the database could contribute to clinical references for diagnosis and medication. The sparsity of underlying relationships between patients poses difficulties for similarity learning, which becomes more challenging when considering real-world Electronic Health Records (EHRs) with a large number of missing values. In the paper, we organize EHRs as a graph and propose a novel deep learning framework, Structure-aware Siamese Graph neural Networks (SSGNet), to perform robust encounter-level patient similarity learning while capturing the intrinsic graph structure and mitigating the influence from missing values. The proposed SSGNet regards each patient encounter as a node, and learns the node embeddings and the similarity between nodes simultaneously via Graph Neural Networks (GNNs) with siamese architecture. Further, SSGNet employs a low-rank and contrastive objective to optimize the structure of the patient graph and enhance model capacity. The extensive experiments were conducted on two publicly available datasets and a real-world dataset regarding IgA nephropathy from Peking University First Hospital, in comparison with multiple baseline and state-of-the-art methods. The significant improvement in Accuracy, Precision, Recall and F1 score on the patient encounter pairwise similarity classification task demonstrates the superiority of SSGNet. The mean average precision (mAP) of SSGNet on the similar encounter retrieval task is also better than other competitors. Furthermore, SSGNet's stable similarity classification accuracies at different missing rates of data validate the effectiveness and robustness of our proposal.

Integrated Application of Low-Intensity Pulsed Ultrasound in Diagnosis and Treatment of Atrophied Skeletal Muscle Induced in Tail-Suspended Rats.

Long-term exposure to microgravity leads to muscle atrophy, which is primarily characterized by a loss of muscle mass and strength and reduces one's functional capability. A weightlessness-induced muscle atrophy model was established using the tail suspension test to evaluate the intervention or therapeutic effect of low-intensity pulsed ultrasound (LIPUS) on muscle atrophy. The rats were divided into five groups at random: the model group (B), the normal control group (NC), the sham-ultrasound control group (SUC), the LIPUS of 50 mW/cm2 radiation group (50 UR), and the LIPUS of 150 mW/cm2 radiation group (150 UR). Body weight, gastrocnemius weight, muscle force, and B-ultrasound images were used to evaluate muscle atrophy status. Results showed that the body weight, gastrocnemius weight, and image entropy of the tail suspension group were significantly lower than those of the control group (p < 0.01), confirming the presence of muscle atrophy. Although the results show that the muscle force and two weights of the rats stimulated by LIPUS are still much smaller than those of the NC group, they are significantly different from those of the pure tail suspension B group (p < 0.01). On day 14, the gastrocnemius forces of the rats exposed to 50 mW/cm2 and 150 mW/cm2 LIPUS were 150% and 165% of those in the B group. The gastrocnemius weights were both 135% of those in the B group. This suggests that ultrasound can, to a certain extent, prevent muscular atrophy.

Skeletal Muscle Fatigue State Evaluation with Ultrasound Image Entropy.

Muscle fatigue often occurs over a long period of exercise, and it can increase the risk of muscle injury. Evaluating the state of muscle fatigue can avoid unnecessary overtraining and injury of the muscle. Ultrasound imaging can non-invasively visualize muscle tissue in real-time. Image entropy is commonly used to characterize the texture of an image. In this study, we evaluated changes in the ultrasound image entropy (USIE) during the fatigue process. Twelve volunteers performed static sustained contractions of biceps brachii at four different intensities (20%, 30%, 40%, and 50% of maximal voluntary contraction torque). The ultrasound images and surface electromyography (sEMG) signals were acquired during exercise to fatigue. We found that (1) the root-mean-square of the sEMG signal increased, the USIE decreased significantly with time during the sustained contractions; (2) the maximum endurance time (MET) and the decline percentage of USIE were significantly different (p < .05) among the four contraction intensities; (3) the decline slope of USIE of the same volunteer was basically the same at different contraction intensities. The USIE could be a new method for the evaluation of skeletal muscle fatigue state.

Graph-guided deep hashing networks for similar patient retrieval.

With the rapid growth and widespread application of electronic health records (EHRs), similar patient retrieval has become an important task for downstream clinical decision support such as diagnostic reference, treatment planning, etc. However, the high dimensionality, large volume, and heterogeneity of EHRs pose challenges to the efficient and accurate retrieval of patients with similar medical conditions to the current case. Several previous studies have attempted to alleviate these issues by using hash coding techniques, improving retrieval efficiency but merely exploring underlying characteristics among instances to preserve retrieval accuracy. In this paper, drug categories of instances recorded in EHRs are regarded as the ground truth to determine the pairwise similarity, and we consider the abundant semantic information within such multi-labels and propose a novel framework named Graph-guided Deep Hashing Networks (GDHN). To capture correlation dependencies among the multi-labels, we first construct a label graph where each node represents a drug category, then a graph convolution network (GCN) is employed to derive the multi-label embedding of each instance. Thus, we can utilize the learned multi-label embeddings to guide the patient hashing process to obtain more informative and discriminative hash codes. Extensive experiments have been conducted on two datasets, including a real-world dataset concerning IgA nephropathy from Peking University First Hospital, and a publicly available dataset from MIMIC-III, compared with traditional hashing methods and state-of-the-art deep hashing methods using three evaluation metrics. The results demonstrate that GDHN outperforms the competitors at different hash code lengths, validating the superiority of our proposal.

Leveraging Summary Guidance on Medical Report Summarization.

This study presents three deidentified large medical text datasets, named DISCHARGE, ECHO and RADIOLOGY, which contain 50 K, 16 K and 378 K pairs of report and summary that are derived from MIMIC-III, respectively. We implement convincing baselines of automated abstractive summarization on the created datasets with pre-trained encoder-decoder language models, including BERT2BERT, BERTShare, RoBERTaShare, Pegasus, ProphetNet, T5-large, BART and GSUM. Further, based on the BART model, we leverage the sampled summaries from the training set as prior knowledge guidance, for encoding additional contextual representations of the guidance with the encoder and enhancing the decoding representations in the decoder. The experimental results confirm the improvement of ROUGE scores and BERTScore made by the proposed method.

Mapping and spatiotemporal dynamics of land-use and land-cover change based on the Google Earth Engine cloud platform from Landsat imagery: A case study of Zhoushan Island, China.

Land resources are an essential foundation for socioeconomic development. Island land resources are limited, the type changes are particularly frequent, and the environment is fragile. Therefore, large-scale, long-term, and high-accuracy land-use classification and spatiotemporal characteristic analysis are of great significance for the sustainable development of islands. Based on the advantages of remote sensing indices and principal component analysis in accurate classification, and taking Zhoushan Archipelago, China, as the study area, in this work long-term satellite remote sensing data were used to perform land-use classification and spatiotemporal characteristic analysis. The classification results showed that the land-use types could be exactly classified, with the overall accuracy and Kappa coefficient greater than 94% and 0.93, respectively. The results of the spatiotemporal characteristic analysis showed that the built-up land and forest land areas increased by 90.00 km2 and 36.83 km2, respectively, while the area of the cropland/grassland decreased by 69.77 km2. The areas of the water bodies, tidal flats, and bare land exhibited slight change trends. The spatial coverage of Zhoushan Island continuously expanded toward the coast, encroaching on nearby sea areas and tidal flats. The cropland/grassland was the most transferred-out area, at up to 108.94 km2, and built-up land was the most transferred-in areas, at up to 73.31 km2. This study provides a data basis and technical support for the scientific management of land resources.

CEHMR: Curriculum learning enhanced hierarchical multi-label classification for medication recommendation.

The medication recommendation (MR) or medication combination prediction task aims to predict effective prescriptions given accurate patient representations derived from electronic health records (EHRs), which contributes to improving the quality of clinical decision-making, especially for patients with multi-morbidity. Although in recent years deep learning technology has achieved great success in MR, the performance of current multi-label based MR solutions is unsatisfactory. They mainly focus on improving the patient representation module and modeling the medication label dependencies such as drug-drug interaction (DDI) correlation and co-occurrence relationship. However, the hierarchical dependency among medication labels and diversity of difficulty among MR training examples lack sufficient consideration. In this paper, we propose a framework of Curriculum learning Enhanced Hierarchical multi-label classification for MR (CEHMR). Motivated by the category hierarchy of medications which organizes standard medication codes in a hierarchical structure, we utilize it to provide more trustworthy prior knowledge for modeling label dependency. Specifically, we design a hierarchical multi-label classifier with a learnable gate fusion layer, to simultaneously capture the level-independent (local) and level-dependent (global) hierarchical information in the medication hierarchy. In addition, to overcome the diversity of training example difficulties, and progressively achieve a smoother training process, we introduce a bootstrap-based curriculum learning strategy. Hence, the example difficulty can be measured based on the predictive performance of the MR model, and then all training examples would be retrained from easy to hard under the guidance of a predefined training scheduler. Experiments on the real-world medical MIMIC-III database demonstrate that the proposed framework can achieve state-of-the-art performance compared with seven representative baselines, and extensive ablation studies validate the effectiveness of each component of CEHMR.

Shaoyao Gancao Decoction protects against dextran sulfate sodium-induced ulcerative colitis by down-regulating ferroptosis.

OBJECTIVES: Shaoyao Gancao Decoction (SGD) is a well-known Chinese herbal prescription used to treat ulcerative colitis (UC). This study was designed to evaluate the effect of SGD in dextran sulfate sodium-induced UC and to reveal the potential mechanism. METHODS: A UC mouse model was established by the administration of dextran sulfate sodium. The mice were given SGD extract intragastrically for 7 days. Histological pathology, inflammatory factors, and ferroptosis regulators were determined in vivo. In addition, ferroptotic Caco-2 cells were prepared to investigate the underlying mechanism of the effects of SGD. KEY FINDINGS: The results showed that SGD reduced the disease activity index, the level of inflammatory factors, and histological damage in mice with UC. Moreover, SGD down-regulated the level of ferroptosis in cells in colon tissue, as evidenced by a reduced iron overload, decreased glutathione depletion, and a lower level of malondialdehyde production, compared with the model group. Correspondingly, similar effects of SGD on ferroptosis were observed in Erastin-treated Caco-2 cells. The results of our in vitro reactive oxygen species assays and the changes in mitochondrial structure observed by scanning electron microscopy also supported these results. CONCLUSION: Taken together, these findings suggest that SGD protected against UC by down-regulating ferroptosis in colonic tissue.

Lineage tracing of mutant granulosa cells reveals in vivo protective mechanisms that prevent granulosa cell tumorigenesis.

Ovarian granulosa cell tumors (GCTs) originate from granulosa cells (GCs) and represent the most common sex cord-stromal tumor in humans. However, the developmental regulations and molecular mechanisms underlying their etiology are largely unknown. In the current study, we combined a multi-fluorescent reporter mouse model with a conditional knockout mouse model, in which the tumor suppressor genes Pten and p27 were deleted in GCs, to perform cell lineage tracing of mutant GCs. We found that only 30% of ovaries with substantial mutant GCs developed into GCTs that derived from a single mutant GC. In-depth molecular analysis of the process of tumorigenesis demonstrated that up-regulation of immune evasion genes Cd24a and Cd47 led, in part, to the transition of mutant GCs to GCTs. Therefore, treatment with the Cd47 inhibitor RRX-001 was tested and found to efficiently suppress the growth of GCTs in vivo. Together, our study has revealed an immune evasion mechanism via CD24/CD47 upregulation to GCT formation, shedding light on the future potential clinical therapies for GCTs.

Analyzing the cellular and molecular atlas of ovarian mesenchymal cells provides a strategy against female reproductive aging.

Ovarian mesenchymal cells (oMCs) constitute a distinct microenvironment that supports folliculogenesis under physiological conditions. Supplementation of exogenous non-ovarian mesenchymal-related cells has been reported to be an efficient approach to improve ovarian functions. However, the development and cellular and molecular characteristics of endogenous oMCs remain largely unexplored. In this study, we surveyed the single-cell transcriptomic landscape to dissect the cellular and molecular changes associated with the aging of oMCs in mice. Our results showed that the oMCs were composed of five ovarian differentiated MC (odMC) populations and one ovarian mesenchymal progenitor (oMP) cell population. These cells could differentiate into various odMCs via an oMP-derived route to construct the ovarian stroma structures. Comparative analysis revealed that ovarian aging was associated with decreased quantity of oMP cells and reduced quality of odMCs. Based on the findings of bioinformatics analysis, we designed different strategies involving supplementation with young oMCs to examine their effects on female fertility and health. Our functional investigations revealed that oMCs supplementation prior to ovarian senescence was the optimal method to improve female fertility and extend the reproductive lifespan of aged females in the long-term.

Simulated microgravity reduces quality of ovarian follicles and oocytes by disrupting communications of follicle cells.

Ovarian follicles are the fundamental structures that support oocyte development, and communications between oocytes and follicle somatic cells are crucial for oogenesis. However, it is unknown that whether exposure to microgravity influences cellular communications and ovarian follicle development, which might be harmful for female fertility. By 3D culturing of ovarian follicles under simulated microgravity (SMG) conditions in a rotating cell culture system, we found that SMG treatment did not affect the survival or general growth of follicles but decreased the quality of cultured follicles released oocytes. Ultrastructure detections by high-resolution imaging showed that the development of cellular communicating structures, including granulosa cell transzonal projections and oocyte microvilli, were markedly disrupted. These abnormalities caused chaotic polarity of granulosa cells (GCs) and a decrease in oocyte-secreted factors, such as Growth Differentiation Factor 9 (GDF9), which led to decreased quality of oocytes in these follicles. Therefore, the quality of oocytes was dramatically improved by the supplementations of GDF9 and NADPH-oxidase inhibitor apocynin. Together, our results suggest that exposure to simulated microgravity impairs the ultrastructure of ovarian follicles. Such impairment may affect female fertility in space environment.

Relationship between Dynamic Changes of Microcirculation Flow, Tissue Perfusion Parameters, and Lactate Level and Mortality of Septic Shock in ICU.

Background: Septic shock is a common clinical critical disease with high mortality, hemodynamic instability, and easy to be complicated with multiple organ failure. The rapid progress of the patient's condition poses a serious threat to patient's safety. Aim: To investigate the relationship between the dynamic monitoring of microcirculation perfusion parameters and blood lactic acid level and the prognosis of patients with infection shock in ICU. Methods: A total of 104 patients with septic shock admitted to ICU of Affiliated Hai'an Hospital of Nantong University from February 2018 to June 2021 were selected for clinical research. According to the survival situation of patients after 28 days of treatment, they were divided into the death group (n = 48) and the survival group (n = 56). The central venous-arterial carbon dioxide partial pressure difference (Pcv-aCO2), the ratio of central venous-arterial carbon dioxide partial pressure difference to arterial central venous oxygen content difference (Pcv-aCO2/Ca-cvO2), and blood lactic acid level were retrospectively analyzed and compared between the two groups on the first, third, and seventh days after admission to ICU. The odds ratio (OR) of three indexes affecting the prognosis of patients with septic shock was analyzed by univariate and multivariate mathematical models, and the value of three indexes in predicting the prognosis of patients was analyzed by receiver operating curve (ROC). Results: Pcv-aCO2 and lactic acid in the death group were higher than those in the survival group on the 1st, 3rd, and 7th day of ICU stay (P < 0.05). The Pcv-aCO2/Ca-cvO2 of the death group was higher than that of the survival group on the 3rd and 7th day of ICU stay (P < 0.05). Logistic model results showed that age, SOFA score, APACHE II score, the number of multiple organ failure (MODS), intracranial infection, the increase of Pcv-aCO2, Pcv-aCO2/Ca-cvO2, and the increase of lactic acid were independent risk factors for death in patients with septic shock (OR values were 1.519, 1.808, 1.781, 1.912, 2.069, 1.848, 1.781, and 1.642, respectively, P < 0.05). The results showed that the AUC value of Pcv-aCO2 in predicting death was 0.943, and the sensitivity and specificity were 93.72% and 83.09%, respectively. The AUC value of Pcv-aCO2/Ca-cvO2 for predicting death was 0.887, and the sensitivity and specificity were 81.63% and 77.56%, respectively. The AUC value of lactic acid in predicting death of patients was 0.825, and the sensitivity and specificity were 71.66% and 82.09%, respectively. Conclusion: Changes of microcirculation flow tissue perfusion parameters and blood lactic acid level changes are closely related to the prognosis of patients with septic shock, which is of great value in the evaluation of the prognosis of patients with septic shock.

Imaging and tracing the pattern of adult ovarian angiogenesis implies a strategy against female reproductive aging.

Robust angiogenesis is continuously active in ovaries to remodel the ovary-body connections in mammals, but understanding of this unique process remains elusive. Here, we performed high-resolution, three-dimensional ovarian vascular imaging and traced the pattern of ovarian angiogenesis and vascular development in the long term. We found that angiogenesis was mainly active on ovarian follicles and corpus luteum and that robust angiogenesis constructs independent but temporary vascular networks for each follicle. Based on the pattern of ovarian angiogenesis, we designed an angiogenesis-blocking strategy by axitinib administration to young females, and we found that the temporary suppression of angiogenesis paused ovarian development and kept the ovarian reserve in the long term, leading to postponed ovarian senescence and an extension of the female reproductive life span. Together, by uncovering the detailed model of physiological ovarian angiogenesis, our experiments suggest a potential approach to delay female reproductive aging through the manipulation of angiogenesis.

Identification of misdiagnosis by deep neural networks on a histopathologic review of breast cancer lymph node metastases.

The frozen section (FS) diagnoses of pathology experts are used in China to determine whether sentinel lymph nodes of breast cancer have metastasis during operation. Direct implementation of a deep neural network (DNN) in clinical practice may be hindered by misdiagnosis of the algorithm, which affects a patient's treatment decision. In this study, we first obtained the prediction result of the commonly used patch-DNN, then we present a relative risk classification and regression tree (RRCART) to identify the misdiagnosed whole-slide images (WSIs) and recommend them to be reviewed by pathologists. Applying this framework to 2362 WSIs of breast cancer lymph node metastasis, test on frozen section results in the mean area under the curve (AUC) reached 0.9851. However, the mean misdiagnosis rate (0.0248), was significantly higher than the pathologists' misdiagnosis rate (p < 0.01). The RRCART distinguished more than 80% of the WSIs as a high-accuracy group with an average accuracy reached to 0.995, but the difference with the pathologists' performance was not significant (p > 0.01). However, the other low-accuracy group included most of the misdiagnoses of DNN models. Our research shows that the misdiagnosis from deep learning model can be further enriched by our method, and that the low-accuracy WSIs must be selected for pathologists to review and the high-accuracy ones may be ready for pathologists to give diagnostic reports.

Generating an Adjustable Focused Field With an Annular Shape Using a Cylindrical Acoustic Transducer Array.

We present the focusing structure of a cylindrical acoustic transducer array consisting of many annular piezoelectric wafer elements operating in the radial vibration mode. Using Huygens' principle, we calculated the delay parameters associated with the excitation signal of each element. Given the respective delay rules, the array transducer produces an adjustable acoustic focused field in the form of a 3-D circular ring. From a theoretical analysis, we designed and fabricated an array transducer with 64 elements and measured its actual field distribution. Simulation and actual experimental results show that the proposed circular cylindrical array transducer controls the annular acoustic focused field well. The sound field intensity of the annular focus region is related to the number of excited array elements, and the radial and axial positions of the annular focus region obey the delay rules of the excitation signal. These acoustic field control methods may be applied in ultrasound detection when scanning a circular sound field.

Distributed Agent Based Interoperable Virtual EMR System for Healthcare System Integration.

One of the major problems in health care system integration is the formidable cost of mediating between myriad vendors and policy makers for updating existing heterogeneous systems to support a great variety of standards or interfaces. To provide cost-effective healthcare system integration solution, this paper presents a Graphical User Interface state model (GUISM) for automatically exchanging information with existing healthcare software through their GUIs with no modifications needed to them. This can save the huge cost of upgrading, testing and redeploying the existing systems. By using the GUISM model, distributed agents are deployed to the client computers interacting with the local electronic medical system (EMR) for communicating with other EMR systems. The whole system is called virtual EMR system and each client in this system can request needed patient healthcare information without knowing the actual location of the data.

Preliminary analysis on distribution characteristics and risk factors of uric acid in overweight and obese children aged 7-13 in Tangshan City / 中国学校卫生

Objective@#To describe the distribution characteristics of uric acid and associated factors among overweight and obese children in Tangshan City, so as to provide reference for the prevention of childhood hyperuricemia and related diseases.@*Methods@#A total of 543 overweight and obese school-age children in Tangshan from 2018 to 2019 were selected, 503 children of normal weight were selected as the control group. Height, weight, waist circumference(WC), blood pressure(BP) were measured, then the Body mass index(BMI) and waist-to-height ratio(WHtR) were calculated. Uric acid(UA), fasting blood glucose(FPG), total cholesterol(TC), triglyceride(TG), high density lipoprotein cholesterol(HDL-C) and low density lipoprotein cholesterol(LDL-C) were measured. The distribution characteristics of uric acid level were described by age groups, and the relationship between uric acid and related indicators were analyzed.@*Results@#The mean uric acid of normal weight group,overweight and obese group were(278.15±89.68, 322.72±89.50)μmol/L respectively, the difference was statistically significant(t=-8.04, P<0.01). The detection rates of hyperuricemia in normal weight group, overweight and obesity group were 11.33% and 23.94% respectively, with statistically significant difference(χ2=28.28, P<0.01). UA level was positively correlated with age, BMI, body weight, TC, LDL-C(r=0.12，0.17，0.28，0.14，0.23，P<0.01), and negatively correlated with HDL-C(r=-0.25，P<0.01). Age, BMI, WHtR, TC, LDL-C were the influencing factors of hyper-uricemia[OR(95%CI)=0.82(0.72-0.94), 1.13(1.08-1.18), 0.38(0.23-0.64), 6.79(2.15-21.44), 0.04(0.01-0.14).@*Conclusion@#The urea level and high uric acid detection rate of overweight and obese children in Tangshan were higher than those of normal weight children. Age, obesity and dyslipidemia were influencing factors of hyperuricemia in obese and overweight children.

[Isolation and identification of a Banna virus strain from mosquitoes in Yunnan province].

OBJECTIVE: To isolate and identify Banna virus (BAV) from mosquitoes collected in Mengla county of Yunnan province. METHODS: Mosquito samples were collected in houses and stock yards in Mengla county, 2008. Mosquitoes were homogenized and incubated onto both C6/36 and BHK21 cells. The new isolate was identified by using ELISA and RT-PCR. The sequences of segment 5, 8 and 11 of BAV were amplified by RT-PCR and determined. Phylogenetic analysis on the new BAV were performed using MEGA4 program. RESULTS: 1731 mosquitoes representing 7 species were collected with one strain of BAV isolated and identified. Phylogenetic analysis based on sequences of segment 8 showed the new isolate was closed to BAV strain isolated in Yunnan, but segment 11 sequence was closed to Vietnam strain. CONCLUSION: Results of phylogenetic analysis implied that the BAV re-assortment might have been occurred both in Chinese and Vietnam strains.