NTBiRW: A Novel Neighbor Model based on Two-tier Bi-Random Walk for Predicting Potential Disease-related Microbes.

Studies have revealed that microbes have an important effect on numerous physiological processes, and further research on the links between diseases and microbes is significant. Given that laboratory methods are expensive and not optimized, computational models are increasingly used for discovering disease-related microbes. Here, a new neighbor approach based on two-tier Bi-Random Walk is proposed for potential disease-related microbes, known as NTBiRW. In this method, the first step is to construct multiple microbe similarities and disease similarities. Then, three kinds of microbe/disease similarity are integrated through two-tier Bi-Random Walk to obtain the final integrated microbe/disease similarity network with different weights. Finally, Weighted K Nearest Known Neighbors (WKNKN) is used for prediction based on the final similarity network. In addition, leave-one-out cross-validation (LOOCV) and 5-fold cross-validation (5-fold CV) are applied for evaluating the performance of NTBiRW. Multiple evaluating indicators are taken to show the performance from multiple perspectives. And most of the evaluation index values of NTBiRW are better than those of the compared methods. Moreover, in case studies on atopic dermatitis and psoriasis, most of the first 10 candidates in the final result can be proven. This also demonstrates the capability of NTBiRW for discovering new associations. Therefore, this method can contribute to the discovery of disease-related microbes and thus offer new thoughts for further understanding the pathogenesis of diseases.

Predicting miRNA-Disease Associations Through Deep Autoencoder With Multiple Kernel Learning.

Determining microRNA (miRNA)-disease associations (MDAs) is an integral part in the prevention, diagnosis, and treatment of complex diseases. However, wet experiments to discern MDAs are inefficient and expensive. Hence, the development of reliable and efficient data integrative models for predicting MDAs is of significant meaning. In the present work, a novel deep learning method for predicting MDAs through deep autoencoder with multiple kernel learning (DAEMKL) is presented. Above all, DAEMKL applies multiple kernel learning (MKL) in miRNA space and disease space to construct miRNA similarity network and disease similarity network, respectively. Then, for each disease or miRNA, its feature representation is learned from the miRNA similarity network and disease similarity network via the regression model. After that, the integrated miRNA feature representation and disease feature representation are input into deep autoencoder (DAE). Furthermore, the novel MDAs are predicted through reconstruction error. Ultimately, the AUC results show that DAEMKL achieves outstanding performance. In addition, case studies of three complex diseases further prove that DAEMKL has excellent predictive performance and can discover a large number of underlying MDAs. On the whole, our method DAEMKL is an effective method to identify MDAs.

A Method Based On Dual-Network Information Fusion to Predict MiRNA-Disease Associations.

MicroRNAs (miRNAs) are single-stranded small RNAs. An increasing number of studies have shown that miRNAs play a vital role in many important biological processes. However, some experimental methods to predict unknown miRNA-disease associations (MDAs) are time-consuming and costly. Only a small percentage of MDAs are verified by researchers. Therefore, there is a great need for high-speed and efficient methods to predict novel MDAs. In this paper, a new computational method based on Dual-Network Information Fusion (DNIF) is developed to predict potential MDAs. Specifically, on the one hand, two enhanced sub-models are integrated to reconstruct an effective prediction framework; on the other hand, the prediction performance of the algorithm is improved by fully fusing multiple omics data information, including validated miRNA-disease associations network, miRNA functional similarity, disease semantic similarity and Gaussian interaction profile (GIP) kernel network associations. As a result, DNIF achieves the excellent performance under situation of 5-fold cross validation (average AUC of 0.9571). In the cases study of three important human diseases, our model has achieved satisfactory performance in predicting potential miRNAs for certain diseases. The reliable experimental results demonstrate that DNIF could serve as an effective calculation method to accelerate the identification of MDAs.

MSF-LRR: Multi-Similarity Information Fusion Through Low-Rank Representation to Predict Disease-Associated Microbes.

An Increase in microbial activity is shown to be intimately connected with the pathogenesis of diseases. Considering the expense of traditional verification methods, researchers are working to develop high-efficiency methods for detecting potential disease-related microbes. In this article, a new prediction method, MSF-LRR, is established, which uses Low-Rank Representation (LRR) to perform multi-similarity information fusion to predict disease-related microbes. Considering that most existing methods only use one class of similarity, three classes of microbe and disease similarity are added. Then, LRR is used to obtain low-rank structural similarity information. Additionally, the method adaptively extracts the local low-rank structure of the data from a global perspective, to make the information used for the prediction more effective. Finally, a neighbor-based prediction method that utilizes the concept of collaborative filtering is applied to predict unknown microbe-disease pairs. As a result, the AUC value of MSF-LRR is superior to other existing algorithms under 5-fold cross-validation. Furthermore, in case studies, excluding originally known associations, 16 and 19 of the top 20 microbes associated with Bacterial Vaginosis and Irritable Bowel Syndrome, respectively, have been confirmed by the recent literature. In summary, MSF-LRR is a good predictor of potential microbe-disease associations and can contribute to drug discovery and biological research.

BMPMDA: Prediction of MiRNA-Disease Associations Using a Space Projection Model Based on Block Matrix.

With the high-quality development of bioinformatics technology, miRNA-disease associations (MDAs) are gradually being uncovered. At present, convenient and efficient prediction methods, which solve the problem of resource-consuming in traditional wet experiments, need to be further put forward. In this study, a space projection model based on block matrix is presented for predicting MDAs (BMPMDA). Specifically, two block matrices are first composed of the known association matrix and similarity to increase comprehensiveness. For the integrity of information in the heterogeneous network, matrix completion (MC) is utilized to mine potential MDAs. Considering the neighborhood information of data points, linear neighborhood similarity (LNS) is regarded as a measure of similarity. Next, LNS is projected onto the corresponding completed association matrix to derive the projection score. Finally, the AUC and AUPR values for BMPMDA reach 0.9691 and 0.6231, respectively. Additionally, the majority of novel MDAs in three disease cases are identified in existing databases and literature. It suggests that BMPMDA can serve as a reliable prediction model for biological research.

Cytogenetic Characteristics of Childhood Acute Lymphoblastic Leukemia: A Study of 1541 Chinese Patients Newly Diagnosed between 2001 and 2014.

OBJECTIVE: Cytogenetic abnormalities have been proven to be the most valuable parameter for risk stratification of childhood acute lymphoblastic leukemia (ALL). However, studies on the prevalence of cytogenetic abnormalities and their correlation to clinical features in Chinese pediatric patients are limited, especially large-scale studies. METHODS: We collected the cytogenetics and clinical data of 1541 children newly diagnosed with ALL between 2001 and 2014 in four Chinese hospitals, and retrospectively analyzed their clinical features, prognosis and risk factors associated with pediatric ALL. RESULTS: All of these patients had karyotyping results, and some of them were tested for fusion genes by fluorescence in situ hybridization or reverse-transcription polymerase chain reaction. Overall, 930 cases (60.4%) had abnormal cytogenetics in this study, mainly including high hyperdiploidy (HHD, n=276, 17.9%), hypodiploidy (n=74, 4.8%), t(12;21)/TEL-AML1 (n=260, 16.9%), t(1;19)/E2A-PBX1 (n=72, 4.7%), t(9;22)/BCR-ABL (n=64, 4.2%), and t(v;11q23)/MLL rearrangements (n=40, 2.6%). The distribution of each cytogenetic abnormality was correlated with gender, age, white blood cell count at diagnosis, and immunophenotype. In addition, multivariate analysis suggested that t(v;11q23)/MLL rearrangements (OR: 2.317, 95%CI: 1.219-3.748, P=0.008) and t(9;22)/BCR-ABL (OR: 2.519, 95%CI: 1.59-3.992, P<0.001) were independent risk factors for a lower event-free survival (EFS) rate in children with ALL, while HHD (OR: 0.638, 95%CI: 0.455-0.894, P=0.009) and t(12;21)/TEL-AML1 (OR: 0.486, 95%CI: 0.333-0.707, P<0.001) were independent factors of a favorable EFS. CONCLUSION: The cytogenetic characteristics presented in our study resembled other research groups, emphasizing the important role of cytogenetic and molecular genetic classification in ALL, especially in B-ALL.

NCPLP: A Novel Approach for Predicting Microbe-Associated Diseases With Network Consistency Projection and Label Propagation.

A growing number of clinical studies have provided substantial evidence of a close relationship between the microbe and the disease. Thus, it is necessary to infer potential microbe-disease associations. But traditional approaches use experiments to validate these associations that often spend a lot of materials and time. Hence, more reliable computational methods are expected to be applied to predict disease-associated microbes. In this article, an innovative mean for predicting microbe-disease associations is proposed, which is based on network consistency projection and label propagation (NCPLP). Given that most existing algorithms use the Gaussian interaction profile (GIP) kernel similarity as the similarity criterion between microbe pairs and disease pairs, in this model, Medical Subject Headings descriptors are considered to calculate disease semantic similarity. In addition, 16S rRNA gene sequences are borrowed for the calculation of microbe functional similarity. In view of the gene-based sequence information, we use two conventional methods (BLAST+ and MEGA7) to assess the similarity between each pair of microbes from different perspectives. Especially, network consistency projection is added to obtain network projection scores from the microbe space and the disease space. Ultimately, label propagation is utilized to reliably predict microbes related to diseases. NCPLP achieves better performance in various evaluation indicators and discovers a greater number of potential associations between microbes and diseases. Also, case studies further confirm the reliable prediction performance of NCPLP. To conclude, our algorithm NCPLP has the ability to discover these underlying microbe-disease associations and can provide help for biological study.

Bipartite graph-based collaborative matrix factorization method for predicting miRNA-disease associations.

BACKGROUND: With the rapid development of various advanced biotechnologies, researchers in related fields have realized that microRNAs (miRNAs) play critical roles in many serious human diseases. However, experimental identification of new miRNA-disease associations (MDAs) is expensive and time-consuming. Practitioners have shown growing interest in methods for predicting potential MDAs. In recent years, an increasing number of computational methods for predicting novel MDAs have been developed, making a huge contribution to the research of human diseases and saving considerable time. In this paper, we proposed an efficient computational method, named bipartite graph-based collaborative matrix factorization (BGCMF), which is highly advantageous for predicting novel MDAs. RESULTS: By combining two improved recommendation methods, a new model for predicting MDAs is generated. Based on the idea that some new miRNAs and diseases do not have any associations, we adopt the bipartite graph based on the collaborative matrix factorization method to complete the prediction. The BGCMF achieves a desirable result, with AUC of up to 0.9514 ± (0.0007) in the five-fold cross-validation experiments. CONCLUSIONS: Five-fold cross-validation is used to evaluate the capabilities of our method. Simulation experiments are implemented to predict new MDAs. More importantly, the AUC value of our method is higher than those of some state-of-the-art methods. Finally, many associations between new miRNAs and new diseases are successfully predicted by performing simulation experiments, indicating that BGCMF is a useful method to predict more potential miRNAs with roles in various diseases.

Epidemiology of Takayasu arteritis in Shanghai: A hospital-based study and systematic review.

BACKGROUND: Takayasu arteritis (TAK) is a rare large vessel vasculitis, and epidemiological data on TAK are lacking in China. Thus, we designed this study to estimate the TAK prevalence and incidence in residential Shanghai, China. METHODS: Data on diagnosed TAK cases aged over 16 years were retrieved from 22 tertiary hospitals in Shanghai through hospital electronic medical record systems between January 1, 2015 and December 31, 2017 to estimate the prevalence and incidence. A systematic literature review based on searches in PubMed, Ovid-Medline, Excerpta Medica Database (EMBASE), Web of Science, and China National Knowledge Infrastructure (CNKI) was performed to summarize TAK distribution across the world. RESULTS: In total 102 TAK patients, with 64% female, were identified. The point prevalence (2015-2017) was 7.01 (95% CI 5.65-8.37) cases per million, and the mean annual incidence was 2.33 (1.97-3.21) cases per million. The average age of TAK patients was 44 ± 16 years, with the highest prevalence (11.59 [9.23-19.50] cases per million) and incidence (3.55 [0.72 3.74] cases per million) in the 16 to 34 years population. Seventeen reports were included in the system review, showing that the epidemiology of TAK varied greatly across the world. The incidence and prevalence were both relatively higher in Asian countries, with the prevalence ranging 3.3-40 cases per million and annual incidence ranging 0.34-2.4 cases per million. CONCLUSIONS: The prevalence and incidence of TAK in Shanghai was at moderate to high levels among the previous reports. The disease burden varied globally among racial populations.

LWPCMF: Logistic Weighted Profile-Based Collaborative Matrix Factorization for Predicting MiRNA-Disease Associations.

As is known to all, constructing experiments to predict unknown miRNA-disease association is time-consuming, laborious and costly. Accordingly, new prediction model should be conducted to predict novel miRNA-disease associations. What's more, the performance of this method should be high and reliable. In this paper, a new computation model Logistic Weighted Profile-based Collaborative Matrix Factorization (LWPCMF) is put forward. In this method, weighted profile (WP) is combined with collaborative matrix factorization (CMF) to increase the performance of this model. And, the neighbor information is considered. In addition, logistic function is applied to miRNA functional similarity matrix and disease semantic similarity matrix to extract valuable information. At the same time, by adding WP and logistic function, the known correlation can be protected. And, Gaussian Interaction Profile (GIP) kernels of miRNAs and diseases are added to miRNA functional similarity network and disease semantic similarity network to augment kernel similarities. Then, a five-fold cross validation is implemented to evaluate the predictive ability of this method. Besides, case studies are conducted to view the experimental results. The final result contains not only known associations but also newly predicted ones. And, the result proves that our method is better than other existing methods. This model is able to predict potential miRNA-disease associations.

MCCMF: collaborative matrix factorization based on matrix completion for predicting miRNA-disease associations.

BACKGROUND: MicroRNAs (miRNAs) are non-coding RNAs with regulatory functions. Many studies have shown that miRNAs are closely associated with human diseases. Among the methods to explore the relationship between the miRNA and the disease, traditional methods are time-consuming and the accuracy needs to be improved. In view of the shortcoming of previous models, a method, collaborative matrix factorization based on matrix completion (MCCMF) is proposed to predict the unknown miRNA-disease associations. RESULTS: The complete matrix of the miRNA and the disease is obtained by matrix completion. Moreover, Gaussian Interaction Profile kernel is added to the miRNA functional similarity matrix and the disease semantic similarity matrix. Then the Weight K Nearest Known Neighbors method is used to pretreat the association matrix, so the model is close to the reality. Finally, collaborative matrix factorization method is applied to obtain the prediction results. Therefore, the MCCMF obtains a satisfactory result in the fivefold cross-validation, with an AUC of 0.9569 (0.0005). CONCLUSIONS: The AUC value of MCCMF is higher than other advanced methods in the fivefold cross validation experiment. In order to comprehensively evaluate the performance of MCCMF, accuracy, precision, recall and f-measure are also added. The final experimental results demonstrate that MCCMF outperforms other methods in predicting miRNA-disease associations. In the end, the effectiveness and practicability of MCCMF are further verified by researching three specific diseases.

Advancements in medical and surgical treatments of Takayasu arteritis-induced renal arteritis: a systematic review.

BACKGROUND: Takayasu arteritis-induced renal arteritis (TARA), commonly seen in Takayasu arteritis (TA), has become one of the main causes of poor prognosis and early mortality in patients with TA. TARA progressing into Takayasu arteritis-induced renal artery stenosis (TARAS), could lead to severe complications including malignant hypertension, cardiac-cerebral vascular disease, and ischemic nephropathy. Since there existed no guidelines on treatments, this study aimed to review the comprehensive treatments for TARA. METHODS: We searched systematically in databases including PubMed, Ovid-Medline, EMBASE, Web of Science, China National Knowledge Infrastructure, Wanfang, and SinoMed, from inception to May 2018. Literature selection, data extraction, and statistical analysis were performed. RESULTS: Eighty-two literatures were recruited focusing on medical treatments (n = 34) and surgical treatments (n = 48). We found that combined medical treatments of glucocorticoids and conventional synthetic disease-modifying anti-rheumatic drugs could reach high rates of remission in patients with TARA, and biological disease-modifying anti-rheumatic drugs were preferred for refractory patients. After remission induction, surgical treatment could help reconstruct renal artery and recover renal function partly. Percutaneous transluminal angioplasty was the first choice for patients with TARAS, while open surgery showed a good long-term survival. CONCLUSIONS: Patients with TARA should benefit both from medical treatments and from surgical treatments comprehensively and sequentially. Multidisciplinary team coordination is recommended especially in patients with severe complications.

Dietary factors and risk of pancreatic cancer: a multi-centre case-control study in China.

BACKGROUND: Pancreatic cancer is the sixth leading cause of cancer death with an increasing trend in China. Dietary intake is believed to play an important role in pancreatic cancer carcinogenesis. The aim of this paper was to evaluate associations between some dietary factors and risk of pancreatic cancer in a multi-centre case-control study conducted in China. MATERIALS AND METHODS: Cases (n=323) were ascertained from four provincial cancer hospitals. Controls (n=323) were randomly selected from the family members of patients without pancreatic cancer in the same hospitals, 1:1 matched to cases by gender, age and study center. Data were collected with a questionnaire by personal interview. Odds ratios (OR) and 95% confidence intervals (95%CI) were estimated using conditional logistic regression. RESULTS: Tea intake (OR =0.49; 95%CI: 0.30-0.80) was associated with a half reduction in risk of pancreatic cancer. Reduced vegetable consumption (P trend: 0.04) was significant related to pancreatic cancer. Although no significant association was found for meat and fruit, ORs were all above or below the reference group. A protective effect was found for fruit (OR=1.73 for consumption of 1-2 times/week vs more than 3 times/week; 95%CI: 1.05-2.86). A high intake of meat was associated to a higher risk of pancreatic cancer (OR=0.59 for consumption of 1-2 times /week vs. more than 3 times /week; 95%CI: 0.35-0.97). CONCLUSIONS: The present study supports fruit consumption to reduce pancreatic cancer risk and indicates that high consumption of meat is related to an elevated risk. Direct inverse relations with tea and vegetable intake were also confirmed.

[Progress on PI3K/Akt signaling pathway regulating self-renewal and pluripotency of embryonic stem cells].

The phosphatidylinositol 3-kinase (PI3K) and its downstream target protein kinase B (Akt/PKB) can be activated by a variety of extracellular and intracellular signals. They are important signaling molecules and key survival factors involved in cell proliferation, differentiation, apoptosis and other cellular processes. Recently, many reports demonstrate that type I PI3K/Akt signaling pathway plays an important role in maintenance of self-renewal and pluripotency of embryonic stem (ES) cells. Further studies with regard to the self-renewal and pluripotency of ES cells and underlying molecular mechanisms are crucial to its application in cell replacement therapy, regenerative medicine and tissue engineering. The present review focuses on the recent progress on the mediation of PI3K/Akt signaling pathway on the maintenance of self-renewal and pluripotency of ES cells.

[Prokaryotic soluble expression of protein D of Haemophilus influenzae type b].

OBJECTIVE: To express the recombinant D protein in prokaryotic expression system solubly and make preparation for producing D-carrier conjugate vaccine next step. METHODS: The hpd gene fragment removed of signal peptide from genomic DNA of Hib CMCC was inserted into pET43. 1a. The recombinant plasmid was transformed to competent E. coli BL21 (DE3) for expression under induction of IPTG. The expressed recombination protein was precipitated with ammonium sulfate, purified by DEAE anion exchange column chromatography and identified for reactogenicity by Western Blot. RESULTS: The expressed recombination protein, in a soluble form, constained about 50% of total somatic protein and showed specific reaction with the HIB antisera after preliminary purification. CONCLUSION: The D protein recombined expression plasmid was constructed successfully and expressed D protein in prokaryotic cells in a solube form.

[Flocculation characteristics of the return sludge in chemical-biological flocculation process].

The chemical-biological flocculation (CBF) process was used to treat municipal wastewater in Shanghai, and the effect of its return sludge on pollutant removal was studied through stopping chemical addition, simulating different return sludge ratio, and analyzing extracellular polymeric substances (EPS). The results show that the return sludge in CBF exhibits strong pollutant removal capability, and chemical in its return sludge can further TP removal and enhance CBF's adaptability to the influent TP impulse. After stopping PAFC (Al2 O3 : 10.8%, Fe2 O3: 1.8%) addition, COD removal efficiency is achieved at about 50%, and removal rates of PO4(3-) and TP gradually decline. It is also found that chemical addition plays an important role in promote sludge sedimentation in CBF. EPS extracted from CBF is 145.89 mg/g, while EPS from CEPT is only 17.24 mg/g, which indicates that there is strong biological flocculation behavior in CBF. With the increase of return sludge ratio, TP and COD removal efficiencies decrease in CEPT, which reveals that chemical in waste sludge has poor flocculation capability, while CBF return sludge exhibits good flocculation behavior due to its biological flocculation. Chemical flocculation and biological flocculation work collaboratively in CBF process.