Attention-based Temporal Graph Representation Learning for EEG-based Emotion Recognition.

Due to the objectivity of emotional expression in the central nervous system, EEG-based emotion recognition can effectively reflect humans' internal emotional states. In recent years, convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have made significant strides in extracting local features and temporal dependencies from EEG signals. However, CNNs ignore spatial distribution information from EEG electrodes; moreover, RNNs may encounter issues such as exploding/vanishing gradients and high time consumption. To address these limitations, we propose an attention-based temporal graph representation network (ATGRNet) for EEG-based emotion recognition. Firstly, a hierarchical attention mechanism is introduced to integrate feature representations from both frequency bands and channels ordered by priority in EEG signals. Second, a graph convolutional neural network with top-k operation is utilized to capture internal relationships between EEG electrodes under different emotion patterns. Next, a residual-based graph readout mechanism is applied to accumulate the EEG feature node-level representations into graph-level representations. Finally, the obtained graph-level representations are fed into a temporal convolutional network (TCN) to extract the temporal dependencies between EEG frames. We evaluated our proposed ATGRNet on the SEED, DEAP and FACED datasets. The experimental findings show that the proposed ATGRNet surpasses the state-of-the-art graph-based mehtods for EEG-based emotion recognition.

Identifying autism spectrum disorder from multi-modal data with privacy-preserving.

The application of deep learning models to precision medical diagnosis often requires the aggregation of large amounts of medical data to effectively train high-quality models. However, data privacy protection mechanisms make it difficult to perform medical data collection from different medical institutions. In autism spectrum disorder (ASD) diagnosis, automatic diagnosis using multimodal information from heterogeneous data has not yet achieved satisfactory performance. To address the privacy preservation issue as well as to improve ASD diagnosis, we propose a deep learning framework using multimodal feature fusion and hypergraph neural networks for disease prediction in federated learning (FedHNN). By introducing the federated learning strategy, each local model is trained and computed independently in a distributed manner without data sharing, allowing rapid scaling of medical datasets to achieve robust and scalable deep learning predictive models. To further improve the performance with privacy preservation, we improve the hypergraph model for multimodal fusion to make it suitable for autism spectrum disorder (ASD) diagnosis tasks by capturing the complementarity and correlation between modalities through a hypergraph fusion strategy. The results demonstrate that our proposed federated learning-based prediction model is superior to all local models and outperforms other deep learning models. Overall, our proposed FedHNN has good results in the work of using multi-site data to improve the performance of ASD identification.

An adaptive weighted ensemble learning network for diabetic retinopathy classification.

Diabetic retinopathy (DR) is one of the leading causes of blindness. However, because the data distribution of classes is not always balanced, it is challenging for automated early DR detection using deep learning techniques. In this paper, we propose an adaptive weighted ensemble learning method for DR detection based on optical coherence tomography (OCT) images. Specifically, we develop an ensemble learning model based on three advanced deep learning models for higher performance. To better utilize the cues implied in these base models, a novel decision fusion scheme is proposed based on the Bayesian theory in terms of the key evaluation indicators, to dynamically adjust the weighting distribution of base models to alleviate the negative effects potentially caused by the problem of unbalanced data size. Extensive experiments are performed on two public datasets to verify the effectiveness of the proposed method. A quadratic weighted kappa of 0.8487 and an accuracy of 0.9343 on the DRAC2022 dataset, and a quadratic weighted kappa of 0.9007 and an accuracy of 0.8956 on the APTOS2019 dataset are obtained, respectively. The results demonstrate that our method has the ability to enhance the ovearall performance of DR detection on OCT images.

An Interpretable Deep Embedding Model for Few and Imbalanced Biomedical Data.

In healthcare, training examples are usually hard to obtain (e.g., cases of a rare disease), or the cost of labelling data is high. With a large number of features ( p) be measured in a relatively small number of samples ( N), the "big p, small N" problem is an important subject in healthcare studies, especially on the genomic data. Another major challenge of effectively analyzing medical data is the skewed class distribution caused by the imbalance between different class labels. In addition, feature importance and interpretability play a crucial role in the success of solving medical problems. Therefore, in this paper, we present an interpretable deep embedding model (IDEM) to classify new data having seen only a few training examples with highly skewed class distribution. IDEM model consists of a feature attention layer to learn the informative features, a feature embedding layer to directly deal with both numerical and categorical features, a siamese network with contrastive loss to compare the similarity between learned embeddings of two input samples. Experiments on both synthetic data and real-world medical data demonstrate that our IDEM model has better generalization power than conventional approaches with few and imbalanced training medical samples, and it is able to identify which features contribute to the classifier in distinguishing case and control.

Unaligned Multimodal Sequences for Depression Assessment From Speech.

A growing area of mental health research pertains to how an individual's degree of depression might be automatically assessed through analyzing multimodal-based objective markers. However, when combined with machine learning, this research can be challenging due to the existence of unaligned multimodal sequences and the limited amount of annotated training data. In this paper, a novel cross-modal framework for automatic depression severity assessment is proposed. The low-level descriptions (LLDs) from multiple clues (such as text, audio and video) are extracted, after which multimodal fusion via cross-modal attention mechanism is utilized to facilitate the learning of more accurate feature representations. For the features extracted from each modality, the cross-modal attention mechanism is utilized to continuously update the input sequence of the target mode, until the score of the patient's health questionnaire (PHQ-8) can finally be obtained. Moreover, Self-Attention Generative Adversarial Networks (SAGAN) is employed to increase the amount of training data available for depression severity analysis. Experimental results on the depression sub-challenge dataset of the Audio/Visual Emotion Challenge (AVEC 2017 and AVEC 2019) demonstrate the effectiveness of our proposed method.

EEG Emotion Recognition Based on Self-attention Dynamic Graph Neural Networks.

In recent years, due to the fundamental role played by the central nervous system in emotion expression, electroencephalogram (EEG) signals have emerged as the most robust signals for use in emotion recognition and inference. Current emotion recognition methods mainly employ deep learning technology to learn the spatial or temporal representation of each channel, then obtain complementary information from different EEG channels by adopting a multi-modal fusion strategy. However, emotional expression is usually accompanied by the dynamic spatio-temporal evolution of functional connections in the brain. Therefore, the effective learning of more robust long-term dynamic representations for the brain's functional connection networks is a key to improving the EEG-based emotion recognition system. To address these issues, we propose a brain network representation learning method that employs self-attention dynamic graph neural networks to obtain the spatial structure information and temporal evolution characteristics of brain networks. Experimental results on the AMIGOS dataset show that the proposed method is superior to the state-of-the-art methods.

A Comprehensive Review of Artificial Intelligence in Prevention and Treatment of COVID-19 Pandemic.

The unprecedented outbreak of the Corona Virus Disease 2019 (COVID-19) pandemic has seriously affected numerous countries in the world from various aspects such as education, economy, social security, public health, etc. Most governments have made great efforts to control the spread of COVID-19, e.g., locking down hard-hit cities and advocating masks for the population. However, some countries and regions have relatively poor medical conditions in terms of insufficient medical equipment, hospital capacity overload, personnel shortage, and other problems, resulting in the large-scale spread of the epidemic. With the unique advantages of Artificial Intelligence (AI), it plays an extremely important role in medical imaging, clinical data, drug development, epidemic prediction, and telemedicine. Therefore, AI is a powerful tool that can help humans solve complex problems, especially in the fight against COVID-19. This study aims to analyze past research results and interpret the role of Artificial Intelligence in the prevention and treatment of COVID-19 from five aspects. In this paper, we also discuss the future development directions in different fields and prove the validity of the models through experiments, which will help researchers develop more efficient models to control the spread of COVID-19.

Emotion Recognition of Chinese Paintings at the Thirteenth National Exhibition of Fines Arts in China Based on Advanced Affective Computing.

Today, with the rapid development of economic level, people's esthetic requirements are also rising, they have a deeper emotional understanding of art, and the voice of their traditional art and culture is becoming higher. The study expects to explore the performance of advanced affective computing in the recognition and analysis of emotional features of Chinese paintings at the 13th National Exhibition of Fines Arts. Aiming at the problem of "semantic gap" in the emotion recognition task of images such as traditional Chinese painting, the study selects the AlexNet algorithm based on convolutional neural network (CNN), and further improves the AlexNet algorithm. Meanwhile, the study adds chi square test to solve the problems of data redundancy and noise in various modes such as Chinese painting. Moreover, the study designs a multimodal emotion recognition model of Chinese painting based on improved AlexNet neural network and chi square test. Finally, the performance of the model is verified by simulation with Chinese painting in the 13th National Exhibition of Fines Arts as the data source. The proposed algorithm is compared with Long Short-Term Memory (LSTM), CNN, Recurrent Neural Network (RNN), AlexNet, and Deep Neural Network (DNN) algorithms from the training set and test set, respectively, The emotion recognition accuracy of the proposed algorithm reaches 92.23 and 97.11% in the training set and test set, respectively, the training time is stable at about 54.97 s, and the test time is stable at about 23.74 s. In addition, the analysis of the acceleration efficiency of each algorithm shows that the improved AlexNet algorithm is suitable for processing a large amount of brain image data, and the acceleration ratio is also higher than other algorithms. And the efficiency in the test set scenario is slightly better than that in the training set scenario. On the premise of ensuring the error, the multimodal emotion recognition model of Chinese painting can achieve high accuracy and obvious acceleration effect. More importantly, the emotion recognition and analysis effect of traditional Chinese painting is the best, which can provide an experimental basis for the digital understanding and management of emotion of quintessence.

Combining a parallel 2D CNN with a self-attention Dilated Residual Network for CTC-based discrete speech emotion recognition.

A challenging issue in the field of the automatic recognition of emotion from speech is the efficient modelling of long temporal contexts. Moreover, when incorporating long-term temporal dependencies between features, recurrent neural network (RNN) architectures are typically employed by default. In this work, we aim to present an efficient deep neural network architecture incorporating Connectionist Temporal Classification (CTC) loss for discrete speech emotion recognition (SER). Moreover, we also demonstrate the existence of further opportunities to improve SER performance by exploiting the properties of convolutional neural networks (CNNs) when modelling contextual information. Our proposed model uses parallel convolutional layers (PCN) integrated with Squeeze-and-Excitation Network (SEnet), a system herein denoted as PCNSE, to extract relationships from 3D spectrograms across timesteps and frequencies; here, we use the log-Mel spectrogram with deltas and delta-deltas as input. In addition, a self-attention Residual Dilated Network (SADRN) with CTC is employed as a classification block for SER. To the best of the authors' knowledge, this is the first time that such a hybrid architecture has been employed for discrete SER. We further demonstrate the effectiveness of our proposed approach on the Interactive Emotional Dyadic Motion Capture (IEMOCAP) and FAU-Aibo Emotion corpus (FAU-AEC). Our experimental results reveal that the proposed method is well-suited to the task of discrete SER, achieving a weighted accuracy (WA) of 73.1% and an unweighted accuracy (UA) of 66.3% on IEMOCAP, as well as a UA of 41.1% on the FAU-AEC dataset.

Epidemiologic characteristics and influencing factors of cluster infection of COVID-19 in Jiangsu Province.

To understand the characteristics and influencing factors related to cluster infections in Jiangsu Province, China, we investigated case reports to explore transmission dynamics and influencing factors of scales of cluster infection. The effectiveness of interventions was assessed by changes in the time-dependent reproductive number (Rt). From 25th January to 29th February, Jiangsu Province reported a total of 134 clusters involving 617 cases. Household clusters accounted for 79.85% of the total. The time interval from onset to report of index cases was 8 days, which was longer than that of secondary cases (4 days) (χ2 = 22.763, P < 0.001) and had a relationship with the number of secondary cases (the correlation coefficient (r) = 0.193, P = 0.040). The average interval from onset to report was different between family cluster cases (4 days) and community cluster cases (7 days) (χ2 = 28.072, P < 0.001). The average time interval from onset to isolation of patients with secondary infection (5 days) was longer than that of patients without secondary infection (3 days) (F = 9.761, P = 0.002). Asymptomatic patients and non-familial clusters had impacts on the size of the clusters. The average reduction in the Rt value in family clusters (26.00%, 0.26 ± 0.22) was lower than that in other clusters (37.00%, 0.37 ± 0.26) (F = 4.400, P = 0.039). Early detection of asymptomatic patients and early reports of non-family clusters can effectively weaken cluster infections.

Metabolic syndrome, hypertension, and hyperglycemia were positively associated with knee osteoarthritis, while dyslipidemia showed no association with knee osteoarthritis.

OBJECTIVE: Metabolic syndrome (MetS) is a clustering of at least three of the following four medical conditions: obesity, hypertension, dyslipidemia, and hyperglycemia. We aimed to discover the relationships between these diseases and osteoarthritis (OA) of the knee. METHODS: We searched four databases (EMBASE, PubMed, Cochrane Library, and MEDLINE), as well as articles on websites and conference materials. Study effect estimates and their 95% confidence intervals (CIs) were extracted and calculated. Sensitivity analyses were undertaken to determine inter-study heterogeneity. Finally, we tested for publication bias to determine whether the outcome of the meta-analysis was robust. RESULTS: A total of 1609 articles were identified, 40 of which were included. In radiological studies, the relationships with OA were increased for people with the following diseases: metabolic syndrome (OR 1.418, 95% CI 1.162 to 1.730), hypertension (OR 1.701, 95% CI 1.411 to 2.052), and hyperglycemia (OR 1.225, 95% CI 1.054 to 1.424). In symptomatic studies, the outcomes were similar in metabolic syndrome (OR 1.174, 95% CI 1.034 to 1.332) and hypertension (OR 1.324, 95% CI 1.186 to 1.478) studies, while there were no associations in hyperglycemia (OR 0.975, 95% CI 0.860 to 1.106) studies. There was no correlation between dyslipidemia and OA, whether in radiological studies (OR 1.216, 95% CI 0.968 to 1.529) or symptomatic studies (OR 1.050, 95% CI 0.961 to 1.146). CONCLUSIONS: In both studies, metabolic syndrome and hypertension were positively associated with knee OA, and dyslipidemia showed no correlations. Hyperglycemia was associated with OA in radiological studies, while results were reversed in symptomatic studies. Key Points • The hypothesis that metabolic syndrome and its components increase the risk for knee osteoarthritis is attractive; thus, this meta-analysis may help us find out the answer. • There were lots of large-scale studies here, and the total participants were considerable; and this meta-analysis was relatively robust because of reasonable heterogeneity and publication bias. • Targeted education and effective management of risk factors may be helpful for reducing the prevalence of knee osteoarthritis.

Classification of Lung Nodules Based on Deep Residual Networks and Migration Learning.

The classification process of lung nodule detection in a traditional computer-aided detection (CAD) system is complex, and the classification result is heavily dependent on the performance of each step in lung nodule detection, causing low classification accuracy and high false positive rate. In order to alleviate these issues, a lung nodule classification method based on a deep residual network is proposed. Abandoning traditional image processing methods and taking the 50-layer ResNet network structure as the initial model, the deep residual network is constructed by combining residual learning and migration learning. The proposed approach is verified by conducting experiments on the lung computed tomography (CT) images from the publicly available LIDC-IDRI database. An average accuracy of 98.23% and a false positive rate of 1.65% are obtained based on the ten-fold cross-validation method. Compared with the conventional support vector machine (SVM)-based CAD system, the accuracy of our method improved by 9.96% and the false positive rate decreased by 6.95%, while the accuracy improved by 1.75% and 2.42%, respectively, and the false positive rate decreased by 2.07% and 2.22%, respectively, in contrast to the VGG19 model and InceptionV3 convolutional neural networks. The experimental results demonstrate the effectiveness of our proposed method in lung nodule classification for CT images.

End-to-End Automatic Pronunciation Error Detection Based on Improved Hybrid CTC/Attention Architecture.

Advanced automatic pronunciation error detection (APED) algorithms are usually based on state-of-the-art automatic speech recognition (ASR) techniques. With the development of deep learning technology, end-to-end ASR technology has gradually matured and achieved positive practical results, which provides us with a new opportunity to update the APED algorithm. We first constructed an end-to-end ASR system based on the hybrid connectionist temporal classification and attention (CTC/attention) architecture. An adaptive parameter was used to enhance the complementarity of the connectionist temporal classification (CTC) model and the attention-based seq2seq model, further improving the performance of the ASR system. After this, the improved ASR system was used in the APED task of Mandarin, and good results were obtained. This new APED method makes force alignment and segmentation unnecessary, and it does not require multiple complex models, such as an acoustic model or a language model. It is convenient and straightforward, and will be a suitable general solution for L1-independent computer-assisted pronunciation training (CAPT). Furthermore, we find that find that in regards to accuracy metrics, our proposed system based on the improved hybrid CTC/attention architecture is close to the state-of-the-art ASR system based on the deep neural network-deep neural network (DNN-DNN) architecture, and has a stronger effect on the F-measure metrics, which are especially suitable for the requirements of the APED task.

HNGF6A Inhibits Oxidative Stress-Induced MC3T3-E1 Cell Apoptosis and Osteoblast Phenotype Inhibition by Targeting Circ_0001843/miR-214 Pathway.

Humanin (HN), a mitochondrial derived peptide, plays cyto-protective role under various stress. In this study, we aimed to investigate the effects of HNGF6A, an analogue of HN, on osteoblast apoptosis and differentiation and the underlying mechanisms. Cell proliferation of murine osteoblastic cell line MC3TC-E1 was examined by CCK8 assay and Edu staining. Cell apoptosis was detected by Annexin V assay under H2O2 treatment. The differentiation of osteoblast was determined by Alizarin red S staining. We also tested the expression of osteoblast phenotype related protein by real-time PCR and Western blot. The interaction between Circ_0001843 and miR-214, miR-214 and TAFA5 was examined by luciferase report assay. Circ_0001843 was inhibited by siRNA and miR-214 was suppressed by miR-214 inhibitor to determine the effects of Circ_0001843 and miR-214 on cell proliferation, apoptosis, and differentiation. HNGF6A, an analogue of HN, exerted cyto-protection and osteogenesis-promotion in MC3T3-E1 cells. The expression of osteoblast phenotype related protein was significantly induced by HNGF6A. Additionally, HNGF6A treatment decreased Circ_0001843 and increased miR-214 levels, as well as inhibited the phosphorylation of p38 and JNK. We further found that Circ_0001843 directly bound with miR-214, which in turn inhibited the phosphorylation of p38 and JNK. Furthermore, both Circ_0001843 overexpression and miR-214 knockdown significantly decreased the cyto-protection and osteogenic promotion of HNGF6A. In summary, our data showed that HNGF6A protected osteoblasts from oxidative stress-induced apoptosis and osteoblast phenotype inhibition by targeting Circ_0001843/miR-214 pathway and the downstream kinases, p38 and JNK.

Wavelet-based method for removing global physiological noise in functional near-infrared spectroscopy.

Functional near-infrared spectroscopy (fNIRS) is a fast-developing non-invasive functional brain imaging technology widely used in cognitive neuroscience, clinical research and neural engineering. However, it is a challenge to effectively remove the global physiological noise in the fNIRS signal. The global physiological noise in fNIRS arises from multiple physiological origins in both superficial tissues and the brain. It has complex temporal, spatial and frequency characteristics, casting significant influence on the results. In the present study, we developed a novel wavelet-based method for fNIRS global physiological noise removal. The method is data-driven and does not rely on any additional hardware or subjective noise component selection procedure. It consists of two steps. Firstly, we use wavelet transform coherence to automatically detect the time-frequency points contaminated by the global physiological noise. Secondly, we decompose the fNIRS signal by using the wavelet transform, and then suppress the wavelet energy of the contaminated time-frequency points. Finally, we transform the signal back to a time series. We validated the method by using simulation and real data at both task- and resting-state. The results showed that our method can effectively remove the global physiological noise from the fNIRS signal and improve the spatial specificity of the task activation and the resting-state functional connectivity pattern.

Tricycloalternarene Analogs from a Symbiotic Fungus Aspergillus sp. D and Their Antimicrobial and Cytotoxic Effects.

Bioassay-guided fractionation of the crude extract of fermentation broth of one symbiotic strain Aspergillus sp. D from the coastal plant Edgeworthia chrysantha Lindl. led to isolation of one new meroterpenoid, tricycloalternarene 14b (1), together with four known analogs (2-5), tricycloalternarenes 2b (2), 3a (3), 3b (4), and ACTG-toxin F (5). Their chemical structures were unambiguously established on the basis of NMR, mass spectrometry, and optical rotation data analysis, as well as by comparison with literature data. Biological assays indicated that compound 2 exhibited potent in vitro cytotoxicity against human lung adenocarcinoma A549 cell line with an IC50 value of 2.91 µM, and compound 5 had a moderate inhibitory effect on Candida albicans, with an MIC value of 15.63 µM. The results indicated that this symbiotic strain D is an important producer of tricycloalternarene derivatives, with potential therapeutic application in treatment of cancer and pathogen infection.

Cytotoxic Natural Products from Marine Sponge-Derived Microorganisms.

A growing body of evidence indicates that marine sponge-derived microbes possess the potential ability to make prolific natural products with therapeutic effects. This review for the first time provides a comprehensive overview of new cytotoxic agents from these marine microbes over the last 62 years from 1955 to 2016, which are assorted into seven types: terpenes, alkaloids, peptides, aromatics, lactones, steroids, and miscellaneous compounds.

[Experimental study of osteogenic induction of fetal mouse liver mesenchymal stem cells in vitro and their biologic attachment properties to true bone ceramic].

OBJECTIVE: To study the culture and purification of the fetal mouse liver mesenchymal stem cells (MSCs) in vitro and to investigate their differentiation potential and the composite ability with true bone ceramic(TBC). METHODS: The single cell suspension of MSCs was primarily cultured and passaged, which was prepared from the fetal mouse liver; the flow cytometry was applied to detect CD29, CD34, CD44 and CD45. The osteogenic differentiation was induced in chemical inducing system; the osteogenic induction potency was tested. The purified fetal mouse liver MSCs were compounded with TBC covered with collagen type I in vitro and the cell attachment and proliferation to the TBC were observed. RESULTS: The primary MSCs of fetal mouse liver were easy to culture in vitro. They proliferated well and were easy to subcultured. The proliferation ability of primary and passaged MSCs was similar. Flow cytometric analysis showed the positive results for CD29, CD44 and the negative results for CD34, CD45. After 7 days of induction, the MSCs expressed collagen type I and alkaline phosphatase(ALP) highly. After 14 days of induction, the fixed quantity of ALP increased significantly. After 28 days of induction, calcium accumulation was observed by Von Kossa's staining. Many liver MSCs attached to the surface of TBC. CONCLUSION: The MSCs of the fetal mouse liver can be obtained, subcultured and purified easily. After culturing in chemical inducing system, the MSCs of fetal mouse liver can be successfully induced to osteoblast-like cells, attach to the surface of TBC and proliferate well.

[Analysis of the different effects of murine bone marrow endothelial cell conditioned medium on the growth of embryonic and adult hematopoietic stem/progenitor cells in vitro].

In the present study, the effects of murine bone marrow endothelial cell conditioned medium (mBMEC-CM) on the growth of yolk sac and bone marrow hematopoietic stem/progenitor cells (HSPC) were investigated. Nonadherent cells of yolk sac and bone marrow were collected for semisolid culture assay of CFU-GM and HPP-CFC after being cultured in DMEM with 10% FBS, 10% mBMEC-CM and/or FL (5 ng/ml), TPO (2 ng/ml) for 24 hours. The number of CFU-GM and HPP-CFC was counted by day 7 and 14 respectively. Atlas cDNA Expression Array was used for analysis of cytokine receptor expression of yolk sac and bone marrow HSPC. The results showed that mBMEC-CM could support the expansion of CFU-GM and HPP-CFC in liquid culture system. The expansion effects of mBMEC-CM were enhanced by combination with FL and TPO. mBMEC-CM was more effective on expansion of bone marrow CFU-GM and HPP-CFC than that of yolk sac CFU-GM and HPP-CFC. The differential expression of cytokine receptors were detected between yolk sac and bone marrow HSPC. PDGF-Rbeta, PDGF-Ralpha and corticotropin releasing factor receptor (CRFR) were only expressed in yolk sac hematopoietic cells while IFN-gammaR, GM-CSFR, Dopamine D2R and follicle-stimulating hormone receptor were only expressed in bone marrow hematopoietic cells. In conclusion, mBMEC-CM could support the growth and proliferation of yolk sac and bone marrow HSPC, and this effect was further enhanced by addition of FL and TPO. mBMEC-CM was more effective on expansion of bone marrow HSPC than on expansion of yolk sac HSPC. The comparative study indicated that the different expressions of cytokine receptors existed between yolk sac and bone marrow hematopoietic cells, which might lead to the difference in expansion in vitro between embryonic and adult HSPC.

[Effects of murine bone marrow endothelial cell conditioned medium on the growth of yolk sac hematopoietic stem cells and progenitors].

OBJECTIVE: To investigate the effects of murine bone marrow endothelial cell conditioned medium (mBMEC-CM) on the growth of yolk sac hematopoietic progenitors. METHODS: The serum-free mBMEC-CM was obtained from subcultures of murine endothelial cell line derived from bone marrow which was established in our laboratory. The murine yolk sacs were harvested on day 8.5 postcoitus (pc) and incubated with 0.1% collagenase in 10% fetal calf serum at 37 degrees C for 40 minutes. Yolk sac cells were incubated in tissue culture dishes at 37 degrees C for 1 hour. Nonadherent cells were collected for semisolid culture assay of granulocyte-macrophage colony forming unit (CFU-GM) and high proliferative potential-colony forming cell (HPP-CFC) after being cultured in DMEM with 10% mBMEC-CM and 10% FBS for 24 hours. The number of CFU-GM and HPP-CFC was counted at day 7 and day 14 respectively. RESULTS: The growth of CFU-GM and HPP-CFC was supported by mBMEC-CM with GM-CSF. mBMEC-CM could induce the proliferation and differentiation of yolk sac hematopoietic stem cells and progenitors in liquid culture system. The percentages of CFU-GM and HPP-CFC compared with the 0 hour control were (119.5 +/- 5.7)% and (130.8 +/- 9.8)% respectively after 24 hours liquid culture (P < 0.05). The expansion effects of mBMEC-CM on CFU-GM and HPP-CFC were enhanced by compounded with flt3 ligand (FL) and thrombopoietin (TPO). The percentages of CFU-GM and HPP-CFC compared with the 0 hour control were (132.0 +/- 6.2)% and (176.9 +/- 12.8)% respectively after 24 hours liquid culture (P < 0.01). CONCLUSION: Murine bone marrow endothelial cell conditioned medium could support the growth and proliferation of yolk sac hematopoitic stem cells and progenitors, and this promoting effect was further enhanced by addition of FL and TPO.