HyMM: hybrid method for disease-gene prediction by integrating multiscale module structure.

MOTIVATION: Identifying disease-related genes is an important issue in computational biology. Module structure widely exists in biomolecule networks, and complex diseases are usually thought to be caused by perturbations of local neighborhoods in the networks, which can provide useful insights for the study of disease-related genes. However, the mining and effective utilization of the module structure is still challenging in such issues as a disease gene prediction. RESULTS: We propose a hybrid disease-gene prediction method integrating multiscale module structure (HyMM), which can utilize multiscale information from local to global structure to more effectively predict disease-related genes. HyMM extracts module partitions from local to global scales by multiscale modularity optimization with exponential sampling, and estimates the disease relatedness of genes in partitions by the abundance of disease-related genes within modules. Then, a probabilistic model for integration of gene rankings is designed in order to integrate multiple predictions derived from multiscale module partitions and network propagation, and a parameter estimation strategy based on functional information is proposed to further enhance HyMM's predictive power. By a series of experiments, we reveal the importance of module partitions at different scales, and verify the stable and good performance of HyMM compared with eight other state-of-the-arts and its further performance improvement derived from the parameter estimation. CONCLUSIONS: The results confirm that HyMM is an effective framework for integrating multiscale module structure to enhance the ability to predict disease-related genes, which may provide useful insights for the study of the multiscale module structure and its application in such issues as a disease-gene prediction.

Biomedical data, computational methods and tools for evaluating disease-disease associations.

In recent decades, exploring potential relationships between diseases has been an active research field. With the rapid accumulation of disease-related biomedical data, a lot of computational methods and tools/platforms have been developed to reveal intrinsic relationship between diseases, which can provide useful insights to the study of complex diseases, e.g. understanding molecular mechanisms of diseases and discovering new treatment of diseases. Human complex diseases involve both external phenotypic abnormalities and complex internal molecular mechanisms in organisms. Computational methods with different types of biomedical data from phenotype to genotype can evaluate disease-disease associations at different levels, providing a comprehensive perspective for understanding diseases. In this review, available biomedical data and databases for evaluating disease-disease associations are first summarized. Then, existing computational methods for disease-disease associations are reviewed and classified into five groups in terms of the usages of biomedical data, including disease semantic-based, phenotype-based, function-based, representation learning-based and text mining-based methods. Further, we summarize software tools/platforms for computation and analysis of disease-disease associations. Finally, we give a discussion and summary on the research of disease-disease associations. This review provides a systematic overview for current disease association research, which could promote the development and applications of computational methods and tools/platforms for disease-disease associations.

DGHNE: network enhancement-based method in identifying disease-causing genes through a heterogeneous biomedical network.

The identification of disease-causing genes is critical for mechanistic understanding of disease etiology and clinical manipulation in disease prevention and treatment. Yet the existing approaches in tackling this question are inadequate in accuracy and efficiency, demanding computational methods with higher identification power. Here, we proposed a new method called DGHNE to identify disease-causing genes through a heterogeneous biomedical network empowered by network enhancement. First, a disease-disease association network was constructed by the cosine similarity scores between phenotype annotation vectors of diseases, and a new heterogeneous biomedical network was constructed by using disease-gene associations to connect the disease-disease network and gene-gene network. Then, the heterogeneous biomedical network was further enhanced by using network embedding based on the Gaussian random projection. Finally, network propagation was used to identify candidate genes in the enhanced network. We applied DGHNE together with five other methods into the most updated disease-gene association database termed DisGeNet. Compared with all other methods, DGHNE displayed the highest area under the receiver operating characteristic curve and the precision-recall curve, as well as the highest precision and recall, in both the global 5-fold cross-validation and predicting new disease-gene associations. We further performed DGHNE in identifying the candidate causal genes of Parkinson's disease and diabetes mellitus, and the genes connecting hyperglycemia and diabetes mellitus. In all cases, the predicted causing genes were enriched in disease-associated gene ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways, and the gene-disease associations were highly evidenced by independent experimental studies.

Application of Nanomaterials in Stem Cell-Based Therapeutics for Cardiac Repair and Regeneration.

Cardiovascular disease is a leading cause of disability and death worldwide. Although the survival rate of patients with heart diseases can be improved with contemporary pharmacological treatments and surgical procedures, none of these therapies provide a significant improvement in cardiac repair and regeneration. Stem cell-based therapies are a promising approach for functional recovery of damaged myocardium. However, the available stem cells are difficult to differentiate into cardiomyocytes, which result in the extremely low transplantation efficiency. Nanomaterials are widely used to regulate the myocardial differentiation of stem cells, and play a very important role in cardiac tissue engineering. This study discusses the current status and limitations of stem cells and cell-derived exosomes/micro RNAs based cardiac therapy, describes the cardiac repair mechanism of nanomaterials, summarizes the recent advances in nanomaterials used in cardiac repair and regeneration, and evaluates the advantages and disadvantages of the relevant nanomaterials. Besides discussing the potential clinical applications of nanomaterials in cardiac therapy, the perspectives and challenges of nanomaterials used in stem cell-based cardiac repair and regeneration are also considered. Finally, new research directions in this field are proposed, and future research trends are highlighted.

NIDM: network impulsive dynamics on multiplex biological network for disease-gene prediction.

The prediction of genes related to diseases is important to the study of the diseases due to high cost and time consumption of biological experiments. Network propagation is a popular strategy for disease-gene prediction. However, existing methods focus on the stable solution of dynamics while ignoring the useful information hidden in the dynamical process, and it is still a challenge to make use of multiple types of physical/functional relationships between proteins/genes to effectively predict disease-related genes. Therefore, we proposed a framework of network impulsive dynamics on multiplex biological network (NIDM) to predict disease-related genes, along with four variants of NIDM models and four kinds of impulsive dynamical signatures (IDSs). NIDM is to identify disease-related genes by mining the dynamical responses of nodes to impulsive signals being exerted at specific nodes. By a series of experimental evaluations in various types of biological networks, we confirmed the advantage of multiplex network and the important roles of functional associations in disease-gene prediction, demonstrated superior performance of NIDM compared with four types of network-based algorithms and then gave the effective recommendations of NIDM models and IDS signatures. To facilitate the prioritization and analysis of (candidate) genes associated to specific diseases, we developed a user-friendly web server, which provides three kinds of filtering patterns for genes, network visualization, enrichment analysis and a wealth of external links (http://bioinformatics.csu.edu.cn/DGP/NID.jsp). NIDM is a protocol for disease-gene prediction integrating different types of biological networks, which may become a very useful computational tool for the study of disease-related genes.

SEPA: signaling entropy-based algorithm to evaluate personalized pathway activation for survival analysis on pan-cancer data.

MOTIVATION: Biomarkers with prognostic ability and biological interpretability can be used to support decision-making in the survival analysis. Genes usually form functional modules to play synergistic roles, such as pathways. Predicting significant features from the functional level can effectively reduce the adverse effects of heterogeneity and obtain more reproducible and interpretable biomarkers. Personalized pathway activation inference can quantify the dysregulation of essential pathways involved in the initiation and progression of cancers, and can contribute to the development of personalized medical treatments. RESULTS: In this study, we propose a novel method to evaluate personalized pathway activation based on signaling entropy for survival analysis (SEPA), which is a new attempt to introduce the information-theoretic entropy in generating pathway representation for each patient. SEPA effectively integrates pathway-level information into gene expression data, converting the high-dimensional gene expression data into the low-dimensional biological pathway activation scores. SEPA shows its classification power on the prognostic pan-cancer genomic data, and the potential pathway markers identified based on SEPA have statistical significance in the discrimination of high- and low-risk cohorts and are likely to be associated with the initiation and progress of cancers. The results show that SEPA scores can be used as an indicator to precisely distinguish cancer patients with different clinical outcomes, and identify important pathway features with strong discriminative power and biological interpretability. AVAILABILITY AND IMPLEMENTATION: The MATLAB-package for SEPA is freely available from https://github.com/xingyili/SEPA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

NetAUC: A network-based multi-biomarker identification method by AUC optimization.

Complex diseases are caused by a variety of factors, and their diagnosis, treatment and prognosis are usually difficult. Proteins play an indispensable role in living organisms and perform specific biological functions by interacting with other proteins or biomolecules, their dysfunction may lead to diseases, it is a natural way to mine disease-related biomarkers from protein-protein interaction network. AUC, the area under the receiver operating characteristics (ROC) curve, is regarded as a gold standard to evaluate the effectiveness of a binary classifier, which measures the classification ability of an algorithm under arbitrary distribution or any misclassification cost. In this study, we have proposed a network-based multi-biomarker identification method by AUC optimization (NetAUC), which integrates gene expression and the network information to identify biomarkers for the complex disease analysis. The main purpose is to optimize two objectives simultaneously: maximizing AUC and minimizing the number of selected features. We have applied NetAUC to two types of disease analysis: 1) prognosis of breast cancer, 2) classification of similar diseases. The results show that NetAUC can identify a small panel of disease-related biomarkers which have the powerful classification ability and the functional interpretability.

PrGeFNE: Predicting disease-related genes by fast network embedding.

Identifying disease-related genes is of importance for understanding of molecule mechanisms of diseases, as well as diagnosis and treatment of diseases. Many computational methods have been proposed to predict disease-related genes, but how to make full use of multi-source biological data to enhance the ability of disease-gene prediction is still challenging. In this paper, we proposed a novel method for predicting disease-related genes by using fast network embedding (PrGeFNE), which can integrate multiple types of associations related to diseases and genes. Specifically, we first constructed a heterogeneous network by using phenotype-disease, disease-gene, protein-protein and gene-GO associations; and low-dimensional representation of nodes is extracted from the network by using a fast network embedding algorithm. Then, a dual-layer heterogeneous network was reconstructed by using the low-dimensional representation, and a network propagation was applied to the dual-layer heterogeneous network to predict disease-related genes. Through cross-validation and newly added-association validation, we displayed the important roles of different types of association data in enhancing the ability of disease-gene prediction, and confirmed the excellent performance of PrGeFNE by comparing to state-of-the-art algorithms. Furthermore, we developed a web tool that can facilitate researchers to search for candidate genes of different diseases predicted by PrGeFNE, along with the enrichment analysis of GO and pathway on candidate gene set. This may be useful for investigation of diseases' molecular mechanisms as well as their experimental validations. The web tool is available at http://bioinformatics.csu.edu.cn/prgefne/.

Occurrence, source tracking and removal of antibiotics in recirculating aquaculture systems (RAS) in southern China.

The recirculating aquaculture system (RAS) has attracted much attention in China as a way to rapidly transform and upgrade aquaculture ponds to realize zero-emissions of pollutants in aquaculture tail water. Tail water purification ponds (TWPPs) play an important role in the treatment of aquaculture wastewater. However, until now, there have been few reports on the occurrence of antibiotics in RAS and the removal of antibiotics from the TWPPs of RAS. Therefore, this study focused on the occurrence of antibiotics in a typical ecological RAS. For comparison, the same measurements were simultaneously carried out in nearby open aquaculture ponds and rivers. The pollution level and spatial distribution of antibiotics in the RAS and the removal of antibiotics in the TWPPs were explored. The results showed that (1) eleven and twelve antibiotics were detected in water and sediment samples in the RAS, respectively, but no antibiotics were found in fish muscles and feed. Erythromycin (ERY), lincomycin (LIN), and ciprofloxacin (CFX) were the three main types of antibiotics found in water and sediment samples. (2) The TWPPs of the RAS can effectively remove antibiotics in aquaculture water. The antibiotic concentration in recirculating aquaculture ponds of the RAS was as high as 180 ng/L. After treatments in the TWPPs, the antibiotic concentration of aquaculture water decreased to 81.6 ng/L (3) The antibiotic concentrations in recirculating aquaculture ponds (25.2-180 ng/L) were lower than those in the nearby open aquaculture ponds (126-267.3 ng/L), and the concentration of antibiotics in the sediments of recirculating aquaculture ponds was up to 22.9 ng/g, while that in TWPPs was as high as 56.1 ng/g. In conclusion, the antibiotic residues in the RAS were low after antibiotics were banned in feed in China, and the removal of antibiotics in the TWPPs was more pronounced. Furthermore, cross-contamination was found between the RAS, surrounding open aquaculture ponds and the river, and the water supply of the RAS was likely to be the main contributor of antibiotics in the aquaculture environments. This study can help the government formulate discharge standards for antibiotics in aquaculture and also provide a reference for the transformation and upgrading of aquaculture ponds to achieve a zero-emission aquaculture mode.

Clinical value of contrast-enhanced computed tomography (CECT) combined with contrast-enhanced ultrasound (CEUS) for characterization and diagnosis of small nodular lesions in liver.

OBJECTIVES: To explore the clinical value of contrast-enhanced computed tomography (CECT) combined with contrast-enhanced ultrasound (CEUS) for characterization and diagnosis of small nodular lesions in the liver and investigate the association between such small nodular lesions and the degree of tumor differentiation. METHODS: Combined imaging modalities were performed on 120 patients who were admitted by Linyi Maternal and Child Health hospital from December 2018 to December 2020 and diagnosed with hepatic nodular lesions. The CT scans were interpreted by two senior imageologists while the ultrasound scans were analyzed by two senior sonographers. A comparative analysis was carried out on different scan modes and the postoperative or post-puncture pathological results using the t-test, the χ2 test, and the Pearson's correlation analysis. RESULTS: Compared to the pathological results, definite diagnoses of 55 malignant cases were made using CECT alone, with the coincidence rate of 78.6%; CECT combined with CEUS formed correct diagnoses in 64 cases, and the coincidence rate was up to 91.4%. The difference between the two scan modes was statistically significant (p= 0.03). Based on pathological diagnosis, seventy out of the 120 cases of small nodular lesions were identified as malignant, while the other 50 cases were benign. The single imaging modality diagnosed 63 malignant and 57 benign nodules, whereas the combined modalities identified 68 malignancies and 52 benign conditions. Compared to CECT as a single imaging modality, the combined modalities showed a higher degree of sensitivity and accuracy, and the difference was statistically significant (sensitivity: p= 0.03; accuracy: p= 0.02); in the malignant cases, the magnitudes of contrast enhancement of CT and ultrasound imaging decreased with an increase in the degree of differentiation, indicating a negative correlation between these factors. CONCLUSIONS: CECT combined with CEUS has a higher coincidence rate, greater sensitivity, and better diagnostic accuracy when being used for characterization and diagnosis of small nodular lesions in the liver. A higher degree of tumor differentiation means a decreased magnitude of contrast enhancement and a blurrier boundary, which indicates that CECT and CEUS are complementary to each other in classifying malignant liver nodules. The use of the combined imaging modalities shows clinical value for characterizing small liver nodules and predicting the degree of malignancy.

NEDD: a network embedding based method for predicting drug-disease associations.

BACKGROUND: Drug discovery is known for the large amount of money and time it consumes and the high risk it takes. Drug repositioning has, therefore, become a popular approach to save time and cost by finding novel indications for approved drugs. In order to distinguish these novel indications accurately in a great many of latent associations between drugs and diseases, it is necessary to exploit abundant heterogeneous information about drugs and diseases. RESULTS: In this article, we propose a meta-path-based computational method called NEDD to predict novel associations between drugs and diseases using heterogeneous information. First, we construct a heterogeneous network as an undirected graph by integrating drug-drug similarity, disease-disease similarity, and known drug-disease associations. NEDD uses meta paths of different lengths to explicitly capture the indirect relationships, or high order proximity, within drugs and diseases, by which the low dimensional representation vectors of drugs and diseases are obtained. NEDD then uses a random forest classifier to predict novel associations between drugs and diseases. CONCLUSIONS: The experiments on a gold standard dataset which contains 1933 validated drug-disease associations show that NEDD produces superior prediction results compared with the state-of-the-art approaches.

The association between IL18, FOXP3 and IL13 genes polymorphisms and risk of allergic rhinitis: a meta-analysis.

OBJECTIVES: Allergic rhinitis (AR) is a chronic inflammatory disease of nasal mucosa. Loss of function of Th17 cells and regulatory T (Treg) cells plays a role in the pathogenesis of AR. IL18, FOXP3, and IL13 are key genes in the development of AR. However, the genetic associations between IL18, FOXP3 and IL13 genes polymorphisms and AR risk were inconclusive yet. METHODS: A meta-analysis was performed by searching through Pubmed, EMBASE, web of science and CNKI databases. The ORs and 95%CIs were used to assess the genetic association between the allelic, dominant and recessive models of IL18, FOXP3 and IL13 genes polymorphisms and AR risk. RESULTS: A total of 15 articles (6 for FOXP3, 5 for IL18, and 5 for IL13) were enrolled in the present study. No association was detected between the IL18 rs187238, rs1946518, rs360721, FOXP3 rs2232365, rs3761548 and IL13 rs1800925 polymorphisms and AR risk (p > 0.05). Significant associations were observed between the allelic (p = 0.001, OR 1.32, 95% CI 1.12-1.56), dominant (p = 0.005, OR 1.43, 95% CI 1.11-1.83) and recessive models (p = 0.01, OR 1.64, 95% CI 1.13, 2.40) of IL13 rs20541 and AR risk. Subgroup analysis based on ethnicity revealed that the IL13 rs20541 was significantly associated with AR risk in Asian population (allelic model: p = 0.009, OR 1.36, 95% CI 1.13-1.63, dominant model: p = 0.005, OR 1.43, 95% CI 1.11-1.83; recessive model: p = 0.01, OR 1.64, 95% CI 1.13-2.40). CONCLUSIONS: IL13 rs20541 may contribute to the risk of AR in Asian population. To confirm these results, larger number of case-control study with more subjects is necessary in the future.

A Stand-Alone Smart Camera System for Online Post-Earthquake Building Safety Assessment.

Computer vision-based approaches are very useful for dynamic displacement measurement, damage detection, and structural health monitoring. However, for the application using a large number of existing cameras in buildings, the computational cost of videos from dozens of cameras using a centralized computer becomes a huge burden. Moreover, when a manual process is required for processing the videos, prompt safety assessment of tens of thousands of buildings after a catastrophic earthquake striking a megacity becomes very challenging. Therefore, a decentralized and fully automatic computer vision-based approach for prompt building safety assessment and decision-making is desired for practical applications. In this study, a prototype of a novel stand-alone smart camera system for measuring interstory drifts was developed. The proposed system is composed of a single camera, a single-board computer, and two accelerometers with a microcontroller unit. The system is capable of compensating for rotational effects of the camera during earthquake excitations. Furthermore, by fusing the camera-based interstory drifts with the accelerometer-based ones, the interstory drifts can be measured accurately even when residual interstory drifts exist. Algorithms used to compensate for the camera's rotational effects, algorithms used to track the movement of three targets within three regions of interest, artificial neural networks used to convert the interstory drifts to engineering units, and some necessary signal processing algorithms, including interpolation, cross-correlation, and filtering algorithms, were embedded in the smart camera system. As a result, online processing of the video data and acceleration data using decentralized computational resources is achieved in each individual smart camera system to obtain interstory drifts. Using the maximum interstory drifts measured during an earthquake, the safety of a building can be assessed right after the earthquake excitation. We validated the feasibility of the prototype of the proposed smart camera system through the use of large-scale shaking table tests of a steel building. The results show that the proposed smart camera system had very promising results in terms of assessing the safety of steel building specimens after earthquake excitations.

Revealing Drug-Target Interactions with Computational Models and Algorithms.

BACKGROUND: Identifying possible drug-target interactions (DTIs) has become an important task in drug research and development. Although high-throughput screening is becoming available, experimental methods narrow down the validation space because of extremely high cost, low success rate, and time consumption. Therefore, various computational models have been exploited to infer DTI candidates. METHODS: We introduced relevant databases and packages, mainly provided a comprehensive review of computational models for DTI identification, including network-based algorithms and machine learning-based methods. Specially, machine learning-based methods mainly include bipartite local model, matrix factorization, regularized least squares, and deep learning. RESULTS: Although computational methods have obtained significant improvement in the process of DTI prediction, these models have their limitations. We discussed potential avenues for boosting DTI prediction accuracy as well as further directions.

Rs4265085 in GPER1 gene increases the risk for unexplained recurrent spontaneous abortion in Dai and Bai ethnic groups in China.

Oestrogen receptors are implicated in the pathogenesis of recurrent spontaneous abortion (RSA). Non-genomic oestrogen responses can be mediated by GPER. The prevalence of polymorphisms in GPER1 gene in RSA was assessed in 747 Chinese women from Yunnan province (171 Bai, 258 Chinese Han, 234 Dai, 33 Achang and 51 Jingpo patients). Snapshot technology was used for genotyping the polymorphisms of the GPER1 gene. The rs4265085G was significantly increased in the Dai and Bai groups versus controls (Dai: P < 0.0001, Padj < 0.0001, OR 95% CI 2.34 [1.79 to 3.05]; Bai: P = 0.0004, Padj = 0.0012, OR 95% CI 1.71 [1.27 to 2.31]); recessive model of rs4265085 in the Dai (P = 0.003, Padj = 0.009, OR 95% CI 2.71 [1.38 to 5.30]); Bai (P < 0.0001, Padj < 0.0001, OR 95% CI 3.37 [1.93 to 5.91]). Haplotype frequencies containing rs10269151G-rs4265085G-rs11544331C were separately significantly different in Dai and Bai ethnic groups (Dai: P = 0.0002, Padj = 0.001, OR 95% CI = 2.12 [1.43 to 3.17]; Bai: P = 0.005, Padj = 0.025, OR 95% CI = 1.82 [1.18 to 2.78]) compared with controls. The intron variant rs4265085 may confer risk for RSA in Dai and Bai ethnic groups.

Imbalance of Th17/Treg in Different Subtypes of Autoimmune Thyroid Diseases.

AIMS: To clarify the imbalance of Th17/Treg in different subtypes of autoimmune thyroid diseases (AITDs) including Graves' disease(GD), Hashimoto's thyroiditis(HT) and Graves' ophthalmopathy (GO). METHODS: 47 patients with AITD (including 16 GD, 15 HT, and 16 GO) and 12 healthy controls were enrolled in this study. The percentages of Th17 and Treg cells, the ratio of Th17/Treg, as well as their related transcription factors RORγt and Foxp3 mRNA in peripheral blood mononuclear cells (PBMCs) were measured by flow cytometry and real-time quantitative PCR Results: Compared with those in control group, the percentage of CD4+IL-17+T cell(Th17) and the mRNA expression of its transcription factor RORγt were higher in PBMCs of AITDs (P<0.05), particularly in HT subgroup (P<0.01). The percentage of CD4+Foxp3+T (Treg) cells and its transcription factor Foxp3 mRNA were significantly decreased in PBMCs of GD (P<0.05). In addition, the ratio of Th17/Treg was elevated in AITD group and GO subgroup (P<0.01). In GO subgroup, the patients with clinical activity score (CAS) above 4.5 had higher percentages of Th17 than those with CAS ranging from 3 to 4.5 (P<0.05). CONCLUSION: Increased Th17 lymphocytes may play a more important role in the pathogenesis of HT and GO while decreased Treg may be greatly involved in GD.

MDDOmics: multi-omics resource of major depressive disorder.

Major depressive disorder (MDD) is a pressing global health issue. Its pathogenesis remains elusive, but numerous studies have revealed its intricate associations with various biological factors. Consequently, there is an urgent need for a comprehensive multi-omics resource to help researchers in conducting multi-omics data analysis for MDD. To address this issue, we constructed the MDDOmics database (Major Depressive Disorder Omics, (https://www.csuligroup.com/MDDOmics/), which integrates an extensive collection of published multi-omics data related to MDD. The database contains 41 222 entries of MDD research results and several original datasets, including Single Nucleotide Polymorphisms, genes, non-coding RNAs, DNA methylations, metabolites and proteins, and offers various interfaces for searching and visualization. We also provide extensive downstream analyses of the collected MDD data, including differential analysis, enrichment analysis and disease-gene prediction. Moreover, the database also incorporates multi-omics data for bipolar disorder, schizophrenia and anxiety disorder, due to the challenge in differentiating MDD from similar psychiatric disorders. In conclusion, by leveraging the rich content and online interfaces from MDDOmics, researchers can conduct more comprehensive analyses of MDD and its similar disorders from various perspectives, thereby gaining a deeper understanding of potential MDD biomarkers and intricate disease pathogenesis. Database URL: https://www.csuligroup.com/MDDOmics/.

Effect of ouabain on myocardial ultrastructure and cytoskeleton during the development of ventricular hypertrophy.

The aim of this work is to study cytoskeletal impairment during the development of ouabain-induced ventricular hypertrophy. Male Sprague-Dawley rats were treated with either ouabain or saline. Systolic blood pressure (SBP) was recorded weekly. At the end of the 3rd and 6th week, the rats were killed and cardiac mass index were measured. Hematoxylin-eosin and Sirius red staining were carried out and cardiac ultrastructure were studied using transmission electron microscopy. The mRNA level of Profilin-1, Desmin, PCNA, TGF-ß(1) and ET-1 in the left ventricle were measured using real-time quantitative PCR while their protein levels were examined by Western blot or immunohistochemistry. After 3 weeks, there was no significant difference in the mean SBP, cardiac mass index, mRNA and protein expression of PCNA, TGF-ß(1) and ET-1 between the two groups. However, ouabain-treated rats showed disorganized cardiac cytoskeleton with abnormal expression of Profilin-1 and Desmin. After 6 weeks, the cardiac mass index remained the same in the two groups while PCNA, TGF-ß(1), and ET-1 have been upregulated in ouabain-treated rats. The cardiac cytoskeletal impairment was more severe in ouabain-treated rats with further changes of Profilin-1 and Desmin. Cytoskeletal abnormality is an ultra-early change during ouabain-induced ventricular hypertrophy, before the release of hypertrophic factors. Therapy for prevention of ouabain-induced hypertrophy should start at the early stage by preventing the cytoskeleton from disorganization.

[Comparison study of endobronchial ultrasound-guided transbronchial needle aspiration and transbronchial needle aspiration for the diagnosis of hilar and mediastinal lesions].

OBJECTIVE: To compare the values of endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) and TBNA for the diagnosis of hilar and mediastinal lesions. METHODS: The clinical data of 100 patients who underwent EBUS-TBNA (n=50) and TBNA (n=50) between January 2010 and May 2011 were retrospectively reviewed, and the results and complications were recorded. RESULTS: A total of 121 lesions in the 100 patients were evaluated, the sample yeilds of EBUS-TBNA and TBNA were 90.6% and 78.9% and the diagnostic accuracy rates in the two groups were 90.0% and 72.0%(P=0.022), respectively. No major complications happened. The sensitivity, specificity and accuracy of EBUS-TBNA were higher and the complication rate was not increased as compared with TBNA. CONCLUSION: EBUS-TBNA has a higher diagnostic yield for the evaluation of hilar and mediastinal lesions.

[Elective microscopic resection of dorsal penile nerves for primary premature ejaculation: a clinical observation].

OBJECTIVE: To evaluate the effect of elective microscopic resection of dorsal penile nerves in the treatment of primary premature ejaculation (PPE). METHODS: Seventy-eight PPE patients received elective microscopic resection of dorsal penile nerves, 5 branches in 9 cases, 6 in 17, 7 in 15, 8 in 14, 9 in 8, 10 in 6, 11 in 6, and 12 in 3. The patients were followed up for 12 months, and their intravaginal ejaculation latency time (IELT) and sexual intercourse satisfaction scores were recorded before and after treatment. RESULTS: Compared with the baseline, the IELT was significantly prolonged after surgery ([0.86 +/- 0.32] vs [6.65 +/- 3.9] min, P < 0.01), and the sexual intercourse satisfaction scores of the patients were dramatically increased (7.32 +/- 2.52 vs 12.32 +/- 3.76, P < 0.01), so were those of their sexual partners (4.46 +/- 1.36 vs 12.73 +/- 1.45, P < 0.01). CONCLUSION: Elective microscopic resection of dorsal penile nerves is safe and effective for the treatment of PPE.