Macrophage polarization in adenomyosis: A review.

Adenomyosis (AM) is a common gynecological disorder characterized by the presence of endometrial glands and stroma within the uterine myometrium. It is associated with abnormal uterine bleeding (AUB), dysmenorrhea, and infertility. Although several mechanisms have been proposed to elucidate AM, the exact cause and development of the condition remain unclear. Recent studies have highlighted the significance of macrophage polarization in the microenvironment, which plays a crucial role in AM initiation and progression. However, a comprehensive review regarding the role and regulatory mechanism of macrophage polarization in AM is currently lacking. Therefore, this review aims to summarize the phenotype and function of macrophage polarization and the phenomenon of the polarization of adenomyosis-associated macrophages (AAMs). It also elaborates on the role and regulatory mechanism of AAM polarization in invasion/migration, fibrosis, angiogenesis, dysmenorrhea, and infertility. Furthermore, this review explores the underlying molecular mechanisms of AAM polarization and suggests future research directions. In conclusion, this review provides a new perspective on understanding the pathogenesis of AM and provides a theoretical foundation for developing targeted drugs through the regulation of AAM polarization.

Plant growth-promoting rhizobacteria Pseudomonas aeruginosa HG28-5 improves salt tolerance by regulating Na+/K+ homeostasis and ABA signaling pathway in tomato.

Salinity stress badly restricts the growth, yield and quality of vegetable crops. Plant growth-promoting rhizobacteria (PGPR) is a friendly and effective mean to enhance plant growth and salt tolerance. However, information on the regulatory mechanism of PGPR on vegetable crops in response to salt stress is still incomplete. Here, we screened a novel salt-tolerant PGPR strain Pseudomonas aeruginosa HG28-5 by evaluating the tomatoes growth performance, chlorophyll fluorescence index, and relative electrolyte leakage (REL) under normal and salinity conditions. Results showed that HG28-5 colonization improved seedling growth parameters by increasing the plant height (23.7%), stem diameter (14.6%), fresh and dry weight in the shoot (60.3%, 91.1%) and root (70.1%, 92.5%), compared to salt-stressed plants without colonization. Likewise, HG28-5 increased levels of maximum photochemical efficiency of PSII (Fv/Fm) (99.3%), the antioxidant enzyme activities as superoxide dismutase (SOD, 85.5%), peroxidase (POD, 35.2%), catalase (CAT, 20.6%), and reduced the REL (48.2%), MDA content (41.3%) and ROS accumulation in leaves of WT tomatoes under salt stress in comparison with the plants treated with NaCl alone. Importantly, Na+ content of HG28-5 colonized salt-stressed WT plants were decreased by15.5% in the leaves and 26.6% in the roots in the corresponding non-colonized salt-stressed plants, which may be attributed to the higher K+ concentration and SOS1, SOS2, HKT1;2, NHX1 transcript levels in leaves of colonized plants under saline condition. Interestingly, increased abscisic acid (ABA) content and upregulation of ABA pathway genes (ABA synthesis-related genes NCED1, NCED2, NCED4, NECD6 and signal genes ABF4, ABI5, and AREB) were observed in HG28-5 inoculated salt-stressed WT plants. ABA-deficient mutant (not) with NCED1 deficiency abolishes the effect of HG28-5 on alleviating salt stress in tomato, as exhibited by the substantial rise of REL and ROS accumulation and sharp drop of Fv/Fm in the leaves of not mutant plants. Notably, HG28-5 colonization enhances tomatoes fruit yield by 54.9% and 52.4% under normal and saline water irrigation, respectively. Overall, our study shows that HG28-5 colonization can significantly enhance salt tolerance and improved fruit yield by a variety of plant protection mechanism, including reducing oxidative stress, regulating plant growth, Na+/K+ homeostasis and ABA signaling pathways in tomato. The findings not only deepen our understanding of PGPR regulation plant growth and salt tolerance but also allow us to apply HG28-5 as a microbial fertilizer for agricultural production in high-salinity areas.

Development and validation of a contrast-enhanced CT-based radiomics nomogram for differentiating mass-like thymic hyperplasia and low-risk thymoma.

PURPOSE: To explore the efficiency of a contrast-enhanced CT-based radiomics nomogram integrated with radiomics signature and clinically independent predictors to distinguish mass-like thymic hyperplasia (ml-TH) from low-risk thymoma (LRT) preoperatively. METHODS: 135 Patients with histopathology confirmed ml-TH (n = 65) and LRT (n = 70) were randomly divided into training set (n = 94) and validation set (n = 41) at a ratio of 7:3. The least absolute shrinkage and selection operator (LASSO) algorithm was used to obtain the optimal features. Based on the selected features, four machine learning models, support vector machine (SVM), logistic regression (LR), extreme gradient boosting (XGBOOST), and random forest (RF) were constructed. Multivariate logistic regression was used to establish a radiomics nomogram containing clinically independent predictors and radiomics signature. Receiver operating characteristic (ROC), DeLong test, and calibration curves were used to detect the performance of the radiomics nomogram in training set and validation set. RESULTS: In the validation set, the area under the curve (AUC) value of LR (0.857; 95% CI: 0.741, 0.973) was the highest of the four machine learning models. Radiomics nomogram containing radiomics signature and clinically independent predictors (including age, shape, and net enhancement degree) had better calibration and identification in the training set (AUC: 0.959; 95% CI: 0.922, 0.996) and validation set (AUC: 0.895; 95% CI: 0.795, 0.996). CONCLUSION: We constructed a contrast-enhanced CT-based radiomics nomogram containing clinically independent predictors and radiomics signature as a noninvasive preoperative prediction method to distinguish ml-TH from LRT. The radiomics nomogram we constructed has potential for preoperative clinical decision making.

Elevated serum leptin may be associated with disease activity and secondary osteoporosis in Chinese patients with rheumatoid arthritis.

INTRODUCTION: Rheumatoid arthritis (RA) is a systemic chronic autoimmune disease in adults that is associated with significant joint issues and systemic inflammation. One of the signs of bone damage in RA is osteoporosis (OP). Leptin is an inflammatory protein that has been reported to be related to RA. The potential relationships among leptin, disease activity, and OP in Chinese patients with RA are not well known. METHODS: In total, 245 patients with RA and 120 healthy controls were included in this study. Detailed data on the clinical characteristics and laboratory features were collected. Information about physical activity and functional status was recorded using specific questionnaires. Bone mineral density (BMD) was measured by dual-energy X-ray absorptiometry (DXA). The MECALL castor-50-hf model X-ray scanner was used for the two-hand (including wrist) photographs. RESULTS: Serum leptin levels differed significantly between the RA group and healthy control subjects (1.27/3.29 vs. 0.17/0.24, Z=13.29, P<0.001). The positive rate of leptin protein in RA patients was 86.35%, which was higher than that in controls (19.55%) (χ2=28.51, P<0.001). Pearson's correlation test showed that morning stiffness, disease duration, joint swelling, joint tenderness, swollen joint count (SJC), tender joint count (TJC), health assessment questionnaire (HAQ) score, and Sharp-van der Heijde method (Sharp) score were positively correlated with the level of serum leptin (r=0.212, r=0.312, r=0.322, r=0.501, r=0.291, r=0.334, P<0.05). There was a clear increasing trend in the level of serum leptin according to the different disease activity scores and in the 28 joint activity (DAS28) groups (F=13.936, P<0.001). Elevated leptin was a risk factor for increased disease activity and OP according to logistic regression analysis. The median leptin level differed significantly between the normal bone mass group, osteopenia group, and OP group (P<0.001). An increased serum leptin level was a risk factor for RA-induced osteoporosis according to logistic regression analysis (P<0.001). CONCLUSION: These results suggest that the level of serum leptin is associated with disease activity and secondary OP among Chinese patients with RA. Key Points • Serum leptin levels in RA patients are higher than those in normal control group. • Leptin was associated with disease activity. • Leptin was associated with the occurrence of systemic osteoporosis and affects bone erosion in RA patients.

Application of DCE-MRI radiomics signature analysis in differentiating molecular subtypes of luminal and non-luminal breast cancer.

Background: The goal of this study was to develop and validate a radiomics signature based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) preoperatively differentiating luminal and non-luminal molecular subtypes in patients with invasive breast cancer. Methods: One hundred and thirty-five invasive breast cancer patients with luminal (n = 78) and non-luminal (n = 57) molecular subtypes were divided into training set (n = 95) and testing set (n = 40) in a 7:3 ratio. Demographics and MRI radiological features were used to construct clinical risk factors. Radiomics signature was constructed by extracting radiomics features from the second phase of DCE-MRI images and radiomics score (rad-score) was calculated. Finally, the prediction performance was evaluated in terms of calibration, discrimination, and clinical usefulness. Results: Multivariate logistic regression analysis showed that no clinical risk factors were independent predictors of luminal and non-luminal molecular subtypes in invasive breast cancer patients. Meanwhile, the radiomics signature showed good discrimination in the training set (AUC, 0.86; 95% CI, 0.78-0.93) and the testing set (AUC, 0.80; 95% CI, 0.65-0.95). Conclusion: The DCE-MRI radiomics signature is a promising tool to discrimination luminal and non-luminal molecular subtypes in invasive breast cancer patients preoperatively and noninvasively.

Rema-Net: An efficient multi-attention convolutional neural network for rapid skin lesion segmentation.

For clinical treatment, the accurate segmentation of lesions from dermoscopic images is extremely valuable. Convolutional neural networks (such as U-Net and its numerous variants) have become the main methods for skin lesion segmentation in recent years. However, because these methods frequently have a large number of parameters and complicated algorithm structures, which results in high hardware requirements and long training time, it is difficult to effectively use them for fast training and segmentation tasks. For this reason, we proposed an efficient multi-attention convolutional neural network (Rema-Net) for rapid skin lesion segmentation. The down-sampling module of the network only uses a convolutional layer and a pooling layer, with spatial attention added to improve useful features. We also designed skip-connections between the down-sampling and up-sampling parts of the network, and used reverse attention operation on the skip-connections to strengthen segmentation performance of the network. We conducted extensive experiments on five publicly available datasets to validate the effectiveness of our method, including the ISIC-2016, ISIC-2017, ISIC-2018, PH2, and HAM10000 datasets. The results show that the proposed method reduced the number of parameters by nearly 40% when compared with U-Net. Furthermore, the segmentation metrics are significantly better than some previous methods, and the predictions are closer to the real lesion.

Bidirectional potential effects of DON transformation in vadose zones on groundwater nitrate contamination: Different contributions to nitrification and denitrification.

The main cause of groundwater nitrate contamination is the continual downward migration of dissolved nitrogen (N) in vadose zone with leachate. In recent years it has been found that dissolved organic N (DON) rise to forefront due to its great migration capacity and environmental effects. However, it remains unknown how the transformation behaviors of DONs with different properties in vadose zone profile may impact N forms distribution and groundwater nitrate contamination. To address the issue, we conducted a series of 60-day microcosm incubation experiments to investigate the effects of various DONs transformation behaviors on the distribution of N forms, microbial communities, and functional genes. The results revealed that urea and amino acids mineralized immediately after substrates addition. By contrast, amino sugars and proteins caused less dissolved N throughout entire incubation period. The transformation behaviors could substantially alter the microbial communities. Moreover, we discovered that amino sugars remarkably increased the absolute abundances of denitrification function genes. These results delineated that DONs with unique characteristics (such as amino sugar) promoted different N geochemical processes in distinct ways: different contributions to nitrification and denitrification. This can provide new insights for nitrate non-point source pollution control in groundwater.

DAGM-fusion: A dual-path CT-MRI image fusion model based multi-axial gated MLP.

Medical imaging technology provides a good understanding of human tissue structure. MRI provides high-resolution soft tissue information, and CT provides high-quality bone density information. By creating CT-MRI fusion images of complex diagnostic situations, experts can develop diagnoses and treatment plans more quickly and precisely. We propose a dual-path CT-MRI image fusion model based on multi-axial gated MLP to create high-quality CT-MRI fusion images. The model employs the feature fusion module SFT-block to effectively integrate detailed Local-Path information guided by global Global-Path information. The fusion is completed through triple constraints, namely global constraints, local constraints, and overall constraints. We design a multi-axial gated MLP module (Ag-MLP). The multi-axial structure maintains the computational complexity linear and increases MLP's inductive bias, allowing MLP to work in shallower or pixel-level small dataset tasks. Ag-MLP and CNN are combined in the network so that the model has both globality and locality. In addition, we design a loss calculation method based on image patches that adaptively generates weights for each patch based on image pixel intensity. The details of the image are efficiently increased when patch-loss is used. Numerous studies demonstrate that the results of our model are superior to those of the latest mainstream fusion model, which are more in accordance with actual clinical diagnostic standards. The ablation studies successfully validate the performance of the model's constituent parts. It is worth mentioning that the model can also be excellently generalized to other modal image fusion tasks.

U-Patch GAN: A Medical Image Fusion Method Based on GAN.

Although medical imaging is frequently used to diagnose diseases, in complex diagnostic situations, specialists typically need to look at different modalities of image information. Creating a composite multimodal medical image can aid professionals in making quick and accurate diagnoses of diseases. The fused images of many medical image fusion algorithms, however, are frequently unable to precisely retain the functional and structural information of the source image. This work develops an end-to-end model based on GAN (U-Patch GAN) to implement the self-supervised fusion of multimodal brain images in order to enhance the fusion quality. The model uses the classical network U-net as the generator, and it uses the dual adversarial mechanism based on the Markovian discriminator (PatchGAN) to enhance the generator's attention to high-frequency information. To ensure that the network satisfies the Lipschitz continuity, we apply the spectral norm to each layer of the network. We also propose better adversarial loss and feature loss (feature matching loss and VGG-16 perceptual loss) based on the F-norm, which significantly enhance the quality of fused images. On public data sets, we performed a lot of tests. First, we studied how clinically useful the fused image was. The model's performance in single-slice images and continuous-slice images was then confirmed by comparison with other six most popular mainstream fusion approaches. Finally, we verify the effectiveness of the adversarial loss and feature loss.

Application of a combined radiomics nomogram based on CE-CT in the preoperative prediction of thymomas risk categorization.

Objective: This study aimed to establish a combined radiomics nomogram to preoperatively predict the risk categorization of thymomas by using contrast-enhanced computed tomography (CE-CT) images. Materials and Methods: The clinical, pathological, and CT data of 110 patients with thymoma (50 patients with low-risk thymomas and 60 patients with high-risk thymomas) collected in our Hospital from July 2017 to March 2022 were retrospectively analyzed. The study subjects were randomly divided into the training set (n = 77) and validation set (n = 33) in a 7:3 ratio. Radiomics features were extracted from the CT images, and the least absolute shrinkage and selection operator (LASSO) algorithm was performed to select 13 representative features. Five models, including logistic regression (LR), support vector machine (SVM), random forest (RF), decision tree (DT), and gradient boosting decision tree (GBDT) were constructed to predict thymoma risks based on these features. A combined radiomics nomogram was further established based on the clinical factors and radiomics scores. The performance of the models was evaluated using receiver operating characteristic (ROC) curve, DeLong tests, and decision curve analysis. Results: Maximum tumor diameter and boundary were selected to build the clinical factors model. Thirteen features were acquired by LASSO algorithm screening as the optimal features for machine learning model construction. The LR model exhibited the highest AUC value (0.819) among the five machine learning models in the validation set. Furthermore, the radiomics nomogram combining the selected clinical variables and radiomics signature predicted the categorization of thymomas at different risks more effectively (the training set, AUC = 0.923; the validation set, AUC = 0.870). Finally, the calibration curve and DCA were utilized to confirm the clinical value of this combined radiomics nomogram. Conclusion: We demonstrated the clinical diagnostic value of machine learning models based on CT semantic features and the selected clinical variables, providing a non-invasive, appropriate, and accurate method for preoperative prediction of thymomas risk categorization.

Identification of PDCD1 as a potential biomarker in acute rejection after kidney transplantation via comprehensive bioinformatic analysis.

Background: Acute rejection is a determinant of prognosis following kidney transplantation. It is essential to search for novel noninvasive biomarkers for early diagnosis and prompt treatment. Methods: Gene microarray data was downloaded from the Gene Expression Omnibus (GEO) expression profile database and the intersected differentially expressed genes (DEGs) was calculated. We conducted the DEGs with Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Distribution of immune cell infiltration was calculated by CIBERSORT. A hub gene marker was identified by intersecting the rejection-related genes from WGCNA and a selected KEGG pathway-T cell receptor signaling pathway (hsa04660), and building a protein-protein interaction network using the STRING database and Cytoscape software. We performed flow-cytometry analysis to validate the hub gene. Results: A total of 1450 integrated DEGs were obtained from five datasets (GSE1563, GSE174020, GSE98320, GSE36059, GSE25902). The GO, KEGG and immune infiltration analysis results showed that AR was mainly associated with T cell activation and various T-cell related pathways. Other immune cells, such as B cells, Macrophage and Dendritic cells were also associated with the progress. After utilizing the WGCNA and PPI network, PDCD1 was identified as the hub gene. The flow-cytometry analysis demonstrated that both in CD4+ and CD8+ T cells, PD1+CD57-, an exhausted T cell phenotype, were downregulated in the acute rejection whole blood samples. Conclusions: Our study illustrated that PDCD1 may be a candidate diagnostic biomarker for acute kidney transplant rejection via integrative bioinformatic analysis.

Value of radiomics model based on enhanced computed tomography in risk grade prediction of gastrointestinal stromal tumors.

To explore the application of computed tomography (CT)-enhanced radiomics for the risk-grade prediction of gastrointestinal stromal tumors (GIST). GIST patients (n = 292) confirmed by surgery or endoscopic pathology during June 2013-2019 were reviewed and categorized into low-grade (very low to low risk) and high-grade (medium to high risk) groups. The tumor region of interest (ROI) was depicted layer by layer on each patient's enhanced CT venous phase images using the ITK-SNAP. The texture features were extracted using the Analysis Kit (AK) and then randomly divided into the training (n = 205) and test (n = 87) groups in a ratio of 7:3. After dimension reduction by the least absolute shrinkage and the selection operator algorithm (LASSO), a prediction model was constructed using the logistic regression method. The clinical data of the two groups were statistically analyzed, and the multivariate regression prediction model was constructed by using statistically significant features. The ROC curve was applied to evaluate the prediction performance of the proposed model. A radiomics-prediction model was constructed based on 10 characteristic parameters selected from 396 quantitative feature parameters extracted from the CT images. The proposed radiomics model exhibited effective risk-grade prediction of GIST. For the training group, the area under curve (AUC), sensitivity, specificity, and accuracy rate were 0.793 (95%CI: 0.733-0.854), 83.3%, 64.3%, and 72.7%, respectively; the corresponding values for the test group were 0.791 (95%CI: 0.696-0.886), 84.2%, 69.3%, and 75.9%, respectively. There were significant differences in age (t value: - 3.133, P = 0.008), maximum tumor diameter (Z value: - 12.163, P = 0.000) and tumor morphology (χ2 value:10.409, P = 0.001) between the two groups, which were used to establish a clinical prediction model. The area under the receiver operating characteristic curve of the clinical model was 0.718 (95%CI: 0.659-0.776). The proposed CT-enhanced radiomics model exhibited better accuracy and effective performance than the clinical model, which can be used for the assessment of risk grades of GIST.

LINC00662 modulates cervical cancer cell proliferation, invasion, and apoptosis via sponging miR-103a-3p and upregulating PDK4.

Cervical cancer (CC) is one of the most common cancers among women with high recurrence rates all over the world. Recently, the molecular mechanism of CC has been gradually uncovered in accumulating reports. This study aimed to investigate the function and upstream regulation mechanism of pyruvate dehydrogenase kinase 4 (PDK4) in CC cells, which was verified as an oncogene in several cancers. Through RT-qPCR assay, we discovered that PDK4 was highly expressed in CC cells. Then, it was demonstrated in function assays that PDK4 facilitated CC cell proliferation and invasion, but inhibited CC cell apoptosis. Next, we sought to determine the upstream genes of PDK4, and miR-103a-3p was identified to target PDK4. Then, through bioinformatics tools and a range of mechanism assays, long intergenic non-protein coding RNA 662 (LINC00662) was verified as the sponge of miR-103a-3p. Moreover, LINC00662 positively modulated PDK4 expression via competitively binding to miR-103a-3p in CC cells. Subsequently, rescue assays demonstrated that LINC00662 accelerated CC cell proliferation and inhibited cell apoptosis through upregulating PDK4. Furthermore, forkhead box A1 (FOXA1) was verified to activate transcription of both LINC00662 and PDK4. Taken together, our study revealed a novel ceRNA pattern of LINC00662/miR-103a-3p/PDK4 with FOXA1 as a transcription factor of LINC00662 and PDK4 in CC cells.

Predicting Multi-Interfacial Binding Mechanisms of NLRP3 and ASC Pyrin Domains in Inflammasome Activation.

NLRP3-PYD inflammasome activates an inflammatory pathway in response to a wide variety of cell damage or infections. Dysregulated NLRP3 inflammatory signaling has many chronic inflammatory and autoimmune disorders. NLRP3 and ASC have a PYD, a superfamily member of the Death Domain, which plays a key role in inflammatory assembly. The ASC interacts with NLRP3 through a homotypic PYD and recruits the procaspase-1 through a homotypic caspase recruitment domain interaction. Here, we used several computational approaches to reveal the interactions of the NLRP3 and ASC PYD domains that lead to the activation of the inflammasome complex. We have characterized ASC and NLRP3-PYD intermolecular interactions by protein-protein docking, and further molecular dynamics (MD) simulations were conducted to evaluate the stability of NLRP3/ASC-PYD complex. Subsequently, we have identified several residues that stabilize the NLRP3/ASC-PYD complex in different faces (i.e., Face-1 to Face-4). The research framework offers new insights into the molecular mechanisms of inflammasome and apoptosis signaling as well as the ease of the drug discovery process.

Analysis of thin-section CT in patients with coronavirus disease (COVID-19) after hospital discharge.

PURPOSE: To analyze clinical and thin-section computed tomographic (CT) data from the patients with coronavirus disease (COVID-19) to predict the development of pulmonary fibrosis after hospital discharge. MATERIALS AND METHODS: Fifty-nine patients (31 males and 28 females ranging from 25 to 70 years old) with confirmed COVID-19 infection performed follow-up thin-section thorax CT. After 31.5±7.9 days (range, 24 to 39 days) of hospital admission, the results of CT were analyzed for parenchymal abnormality (ground-glass opacification, interstitial thickening, and consolidation) and evidence of fibrosis (parenchymal band, traction bronchiectasis, and irregular interfaces). Patients were analyzed based on the evidence of fibrosis and divided into two groups namely, groups A and B (with and without CT evidence of fibrosis), respectively. Patient demographics, length of stay (LOS), rate of intensive care unit (ICU) admission, peak C-reactive protein level, and CT score were compared between the two groups. RESULTS: Among the 59 patients, 89.8% (53/59) had a typical transition from early phase to advanced phase and advanced phase to dissipating phase. Also, 39% (23/59) patients developed fibrosis (group A), whereas 61% (36/59) patients did not show definite fibrosis (group B). Patients in group A were older (mean age, 45.4±16.9 vs. 33.8±10.2 years) (P = 0.001), with longer LOS (19.1±5.2 vs. 15.0±2.5 days) (P = 0.001), higher rate of ICU admission (21.7% (5/23) vs. 5.6% (2/36)) (P = 0.061), higher peak C-reactive protein level (30.7±26.4 vs. 18.1±17.9 mg/L) (P = 0.041), and higher maximal CT score (5.2±4.3 vs. 4.0±2.2) (P = 0.06) than those in group B. CONCLUSIONS: Pulmonary fibrosis may develop early in patients with COVID-19 after hospital discharge. Older patients with severe illness during treatment were more prone to develop fibrosis according to thin-section CT results.

Exosomes from EV71-infected oral epithelial cells can transfer miR-30a to promote EV71 infection.

OBJECTIVE: As an extracellular vesicle, exosomes can release from virus-infected cells containing various viral or host cellular elements and could stimulate recipient's cellular response. Enterovirus 71 (EV71), a single-strand positive-sense RNA virus, is known to cause hand, foot, and mouth disease (HFMD) in children and bring about severe clinical diseases. METHODS: Separated the human oral epithelial cells (OE cells) from normal buccal mucosa through enzyme digestion. Performed a comprehensive miRNA profiling in exosomes from EV71-infected OE cells through deep small RNA-seq. Using the Human Antiviral Response RT Profiler PCR Array profiles to explore the interactions of innate immune signaling networks with exosomal miR-30a. Knocked out the MyD88 gene in macrophages using CRISPR/Cas9-mediated genome editing method. RESULTS: Our study demonstrated that the miR-30a was preferentially enriched in exosomes that released from EV71-infected human oral epithelial cells through small RNA-seq. We found that the transfer of exosomal miR-30a to macrophages could suppress type Ⅰ interferon response through targeting myeloid differentiation factor 88 (MyD88) and subsequently facilitate the viral replication. CONCLUSIONS: Exosomes released from EV71-infected OE cells selectively packaged high level of miR-30a that can be functionally transferred to the recipient macrophages resulted in targeting MyD88 and subsequently inhibited type I interferon production in receipt cells, thus promoting the EV71 replication.

Microsatellite primers in red bayberry, Myrica rubra (Myricaceae).

PREMISE OF THE STUDY: Microsatellite markers were developed in Myrica rubra to investigate potential hybridization events within or between M. rubra and its closely related species. METHODS AND RESULTS: Using an ISSR-suppression PCR method, 12 primer pairs were temporarily developed with GSG(GT)(6) as the primer for enriching microsatellite sequences and the genomic DNA of M. rubra cv. 'Heijing' as template. The average allele number per locus was 4.9 within 26 individuals including two species, M. rubra and M. nana. Three pairs of primers produced species-specific alleles, and the other nine showed polymorphisms among 26 accessions. CONCLUSIONS: Results indicated that the ISSR-suppression PCR method is suitable for developing microsatellite markers, especially for this species with little understanding of genomic information. The developed microsatellite markers provide a useful tool for further studies of population structure within or between M. rubra and M. nana or other closely related species.