Visible-Light-Mediated [2+2] Photocycloadditions of Alkynes.

Visible-light-mediated [2+2] photocycloaddition reaction can be considered an ideal solution due to its green and sustainable properties, and is one of the most efficient methods to synthesize four-membered ring motifs. Although research on the [2+2] photocycloaddition of alkynes is challenging because of the diminished reactivity of alkynes, and the more significant ring strain of the products, remarkable achievements have been made in this field. In this article, we highlight the recent advances in visible-light-mediated [2+2] photocycloaddition reactions of alkynes, with focus on the reaction mechanism and the late-stage synthetic applications. Advances in obtaining cyclobutenes, azetines, and oxetene active intermediates continue to be breakthroughs in this fascinating field of research.

Rapid and Visual Detection of Volatile Amines Based on Their Gas-Solid Reaction with Tetrachloro-p-Benzoquinone.

The detection of volatile amines is necessary due to the serious toxicity hazards they pose to human skin, respiratory systems, and nervous systems. However, traditional amines detection methods require bulky equipment, high costs, and complex measurements. Herein, we report a new simple, rapid, convenient, and visual method for the detection of volatile amines based on the gas-solid reactions of tetrachloro-p-benzoquinone (TCBQ) and volatile amines. The gas-solid reactions of TCBQ with a variety of volatile amines showed a visually distinct color in a time-dependent manner. Moreover, TCBQ can be easily fabricated into simple and flexible rapid test strips for detecting and distinguishing n-propylamine from other volatile amines, including ethylamine, n-butyamine, n-pentamine, n-butyamine and dimethylamine, in less than 3 s without any equipment assistance.

[Effects of Platelet-Rich Plasma-Derived Exosomes on Proliferation and Migration of Tendon Stem/Progenitor Cell].

Objective To investigate the effects of platelet-rich plasma-derived exosomes (PRP-Exos) on the proliferation and migration of tendon stem/progenitor cell (TSPC).Methods PRP-Exos were extracted through the combination of polymer-based precipitation and ultracentrifugation.The morphology,concentration,and particle size of PRP-Exos were identified by transmission electron microscopy and nanoparticle tracking analysis.The expression levels of surface marker proteins on PRP-Exos and platelet membrane glycoproteins were determined by Western blot analysis.Rat TSPC was extracted and cultured,and the expression of surface marker molecules on TSPC was detected using flow cytometry and immunofluorescence staining.The proliferation of TSPC influenced by PRP-Exos was evaluated using CCK-8 assay and EdU assay.The effect of PRP-Exos on the migration of TSPC was evaluated by cell scratch assay and Transwell assay.Results The extracted PRP-Exos exhibit typical saucer-like structures,with a concentration of 4.9×1011 particles/mL,an average particle size of (132.2±56.8) nm,and surface expression of CD9,CD63 and CD41.The extracted TSPC expressed the CD44 protein.PRP-Exos can be taken up by TSPC,and after co-cultured for 48 h,concentrations of 50 and 100 µg/mL of PRP-Exos significantly promoted the proliferation of TSPC (both P<0.001),with no statistical difference between the two concentrations (P=0.283).Additionally,after co-cultured for 24 h,50 µg/mL of PRP-Exos significantly promoted the migration of TSPC (P<0.001).Conclusion Under in vitro culture conditions,PRP-Exos significantly promote the proliferation and migration of rat TSPC.

Aberrant degree centrality profiles during rumination in major depressive disorder.

Rumination is closely linked to the onset and maintenance of major depressive disorder (MDD). Prior neuroimaging studies have identified the association between self-reported rumination trait and the functional coupling among a network of brain regions using resting-state functional magnetic resonance imaging (MRI). However, little is known about the underlying neural circuitry mechanism during active rumination in MDD. Degree centrality (DC) is a simple metric to denote network integration, which is critical for higher-order psychological processes such as rumination. During an MRI scan, individuals with MDD (N = 45) and healthy controls (HC, N = 46) completed a rumination state task. We examined the interaction effect between the group (MDD vs. HC) and condition (rumination vs. distraction) on vertex-wise DC. We further characterized the identified brain region's functional involvement with Neurosynth and BrainMap. Network-wise seed-based functional connectivity (FC) analysis was also conducted for the identified region of interest. Finally, exploratory correlation analysis was conducted between the identified region of interest's network FCs and self-reported in-scanner affect levels. We found that a left superior frontal gyrus (SFG) region, generally overlapped with the frontal eye field, showed a significant interaction effect. Further analysis revealed its involvement with executive functions. FCs between this region, the frontoparietal, and the dorsal attention network (DAN) also showed significant interaction effects. Furthermore, its FC to DAN during distraction showed a marginally significant negative association with in-scanner affect level at the baseline. Our results implicated an essential role of the left SFG in the rumination's underlying neural circuitry mechanism in MDD and provided novel evidence for the conceptualization of rumination in terms of impaired executive control.

Tensor decomposition with relational constraints for predicting multiple types of microRNA-disease associations.

MicroRNAs (miRNAs) play crucial roles in multifarious biological processes associated with human diseases. Identifying potential miRNA-disease associations contributes to understanding the molecular mechanisms of miRNA-related diseases. Most of the existing computational methods mainly focus on predicting whether a miRNA-disease association exists or not. However, the roles of miRNAs in diseases are prominently diverged, for instance, Genetic variants of miRNA (mir-15) may affect the expression level of miRNAs leading to B cell chronic lymphocytic leukemia, while circulating miRNAs (including mir-1246, mir-1307-3p, etc.) have potentials to detecting breast cancer in the early stage. In this paper, we aim to predict multi-type miRNA-disease associations instead of taking them as binary. To this end, we innovatively represent miRNA-disease-type triples as a tensor and introduce tensor decomposition methods to solve the prediction task. Experimental results on two widely-adopted miRNA-disease datasets: HMDD v2.0 and HMDD v3.2 show that tensor decomposition methods improve a recent baseline in a large scale (up to $38\%$ in Top-1F1). We then propose a novel method, Tensor Decomposition with Relational Constraints (TDRC), which incorporates biological features as relational constraints to further the existing tensor decomposition methods. Compared with two existing tensor decomposition methods, TDRC can produce better performance while being more efficient.

AGR2 and FOXA1 as prognostic markers in ER-positive breast cancer.

BACKGROUND: The prognostic role of either forkhead box A1 (FOXA1) or anterior gradient 2 (AGR2) in breast cancer has been found separately. Considering that there were interplays between them depending on ER status, we aimed to assess the statistical interaction between AGR2 and FOXA1 on breast cancer prognosis and examine the prognostic role of the combination of them by ER status. METHODS: AGR2 and FOXA1 expression in tumor tissues were evaluated with tissue microarrays by immunohistochemistry in 915 breast cancer patients with follow up data. The expression levels of these two markers were treated as binary variables, and many different cutoff values were tried for each marker. Survival and Cox proportional hazard analyses were used to evaluate the relationship between AGR2, FOXA1 and prognosis, and the statistical interaction between them on the prognosis was assessed on multiplicative scale. RESULTS: Statistical interaction between AGR2 and FOXA1 on the PFS was significant with all the cutoff points in ER-positive breast cancer patients but not ER-negative ones. Among ER-positive patients, the poor prognostic role of the high level of FOXA1 was significant only in patients with the low level of AGR2, and vice versa. When AGR2 and FOXA1 were considered together, patients with low levels of both markers had significantly longer PFS compared with all other groups. CONCLUSIONS: There was a statistical interaction between AGR2 and FOXA1 on the prognosis of ER-positive breast cancer. The combination of AGR2 and FOXA1 was a more useful marker for the prognosis of ER-positive breast cancer patients.

Combined low levels of H4K16ac and H4K20me3 predicts poor prognosis in breast cancer.

BACKGROUND: Results of previous studies about the prognostic roles of histone H4 lysine 16 acetylation (H4K16ac) and histone H4 lysine 20 trimethylation (H4K20me3) in breast cancer were inconsistent. Cellular experiments revealed the interplays between H4K16ac and H4K20me3, but no population study explored the interaction between them on the prognosis. METHODS: H4K16ac and H4K20me3 levels in tumors were evaluated by immunohistochemistry for 958 breast cancer patients. Hazard ratios for overall survival (OS) and progression-free survival (PFS) were estimated using Cox regression models. Interaction was assessed on multiplicative scale. Concordance index (C-index) was calculated to verify the predictive performance. RESULTS: The prognostic roles of the low level of H4K16ac or H4K20me3 were significant only in patients with the low level of another marker and their interactions were significant. Moreover, compared with joint high levels of both them, only the combined low levels of both them was associated with a poor prognosis but not the low level of single one. The C-index of the clinicopathological model combined the joint expression of H4K16ac and H4K20me3 [0.739 for OS; 0.672 for PFS] was significantly larger than that of the single clinicopathological model [0.699 for OS, P < 0.001; 0.642 for PFS, P = 0.003] or the model combined with the single H4K16ac [0.712 for OS, P < 0.001; 0.646 for PFS, P < 0.001] or H4K20me3 [0.724 for OS, P = 0.031; 0.662 for PFS, P = 0.006]. CONCLUSIONS: There was an interaction between H4K16ac and H4K20me3 on the prognosis of breast cancer and the combination of them was a superior prognostic marker compared to the single one.

[Human Platelet-Rich Plasma-Derived Exosomes Promote the Proliferation of Schwann Cells Cultured in Vitro].

Objective To investigate the effect of human platelet-rich plasma-derived exosomes(PRP-exos)on the proliferation of Schwann cell(SC)cultured in vitro. Methods PRP-exos were extracted by polymerization-precipitation combined with ultracentrifugation.The morphology of PRP-exos was observed by transmission electron microscopy,and the concentration and particle size distribution of PRP-exos were determined by nanoparticle tracking analysis.Western blotting was employed to determine the expression of the marker proteins CD63,CD81,and CD9 on exosome surface and the platelet membrane glycoprotein CD41.The SCs of rats were isolated and cultured,and the expression of the SC marker S100ß was detected by immunofluorescence staining.The fluorescently labeled PRP-exos were co-cultured with SCs in vitro for observation of their interaction.EdU assay was employed to detect the effect of PRP-exos on SC proliferation,and CCK-8 assay to detect the effects of PRP-exos at different concentrations(0,10,20,40,80,and 160 µg/ml)on SC proliferation. Results The extracted PRP-exos appeared as uniform saucer-shaped vesicles with the average particle size of(122.8±38.7)nm and the concentration of 3.5×1012 particles/ml.CD63,CD81,CD9,and CD41 were highly expressed on PRP-exos surface(P<0.001,P=0.025,P=0.004,and P=0.032).The isolated SCs expressed S100ß,and PRP-exos could be taken up by SCs.PRP-exos of 40,80,and 160 µg/ml promoted the proliferation of SCs,and that of 40 µg/ml showed the best performance(all P<0.01). Conclusions High concentrations of PRP-exos can be extracted from PRP.PRP-exos can be taken up by SCs and promote the proliferation of SCs cultured in vitro.

Plant-Derived Molecule 4-Methylumbelliferone Suppresses FcεRI-Mediated Mast Cell Activation and Allergic Inflammation.

Mast cells (MCs) are an important treatment target for high-affinity IgE Fc receptor (FcεRI)-mediated allergic diseases. The plant-derived molecule 4-methylumbelliferone (4-MU) has beneficial effects in animal models of inflammation and autoimmunity diseases. The aim of this study was to examine 4-MU effects on MC activation and probe the underlying molecular mechanism(s). We sensitized rat basophilic leukemia cells (RBLs) and mouse bone marrow-derived mast cells (BMMCs) with anti-dinitrophenol (DNP) immunoglobulin (Ig)E antibodies, stimulated them with exposure to DNP-human serum albumin (HSA), and then treated stimulated cells with 4-MU. Signaling-protein expression was determined by immunoblotting. In vivo allergic responses were examined in IgE-mediated passive cutaneous anaphylaxis (PCA) and ovalbumin (OVA)-induced active systemic anaphylaxis (ASA) mouse models. 4-MU inhibited ß-hexosaminidase activity and histamine release dose-dependently in FcεRI-activated RBLs and BMMCs. Additionally, 4-MU reduced cytomorphological elongation and F-actin reorganization while down-regulating IgE/Ag-induced phosphorylation of SYK, NF-κB p65, ERK1/2, p38, and JNK. Moreover, 4-MU attenuated the PCA allergic reaction (i.e., less ear thickening and dye extravasation). Similarly, we found that 4-MU decreased body temperature, serum histamine, and IL4 secretion in OVA-challenged ASA model mice. In conclusion, 4-MU had a suppressing effect on MC activation both in vitro and in vivo and thus may represent a new strategy for treating IgE-mediated allergic conditions.

Actinomycin D: a novel Pseudomonas aeruginosa quorum sensing inhibitor from the endophyte Streptomyces cyaneochromogenes RC1.

The infections caused by Pseudomonas aeruginosa are difficult to treat due to its multidrug resistance. A promising strategy for controlling P. aeruginosa infection is targeting the quorum sensing (QS) system. Actinomycin D isolated from the metabolite of endophyte Streptomyces cyaneochromogenes RC1 exhibited good anti-QS activity against P. aeruginosa PAO1. Actinomycin D (50, 100, and 200 µg/mL) significantly inhibited the motility as well as reduced the production of multiple virulence factors including pyocyanin, protease, rhamnolipid, and siderophores. The images of confocal laser scanning microscopy and scanning electron microscopy revealed that the treatment of actinomycin D resulted in a looser and flatter biofilm structure. Real-time quantitative PCR analysis showed that the expression of QS-related genes lasI, rhlI, rhlR, pqsR, pslA, and pilA were downregulated dramatically. The production of QS signaling molecules N-(3-oxododecanoyl)-L-homoserine lactone and N-butanoyl-L-homoserine lactone were also decreased by actinomycin D. These findings suggest that actinomycin D, a potent in vitro anti-virulence agent, is a promising candidate to treat P. aeruginosa infection by interfering with the QS systems.

Graph embedding on biomedical networks: methods, applications and evaluations.

MOTIVATION: Graph embedding learning that aims to automatically learn low-dimensional node representations, has drawn increasing attention in recent years. To date, most recent graph embedding methods are evaluated on social and information networks and are not comprehensively studied on biomedical networks under systematic experiments and analyses. On the other hand, for a variety of biomedical network analysis tasks, traditional techniques such as matrix factorization (which can be seen as a type of graph embedding methods) have shown promising results, and hence there is a need to systematically evaluate the more recent graph embedding methods (e.g. random walk-based and neural network-based) in terms of their usability and potential to further the state-of-the-art. RESULTS: We select 11 representative graph embedding methods and conduct a systematic comparison on 3 important biomedical link prediction tasks: drug-disease association (DDA) prediction, drug-drug interaction (DDI) prediction, protein-protein interaction (PPI) prediction; and 2 node classification tasks: medical term semantic type classification, protein function prediction. Our experimental results demonstrate that the recent graph embedding methods achieve promising results and deserve more attention in the future biomedical graph analysis. Compared with three state-of-the-art methods for DDAs, DDIs and protein function predictions, the recent graph embedding methods achieve competitive performance without using any biological features and the learned embeddings can be treated as complementary representations for the biological features. By summarizing the experimental results, we provide general guidelines for properly selecting graph embedding methods and setting their hyper-parameters for different biomedical tasks. AVAILABILITY AND IMPLEMENTATION: As part of our contributions in the paper, we develop an easy-to-use Python package with detailed instructions, BioNEV, available at: https://github.com/xiangyue9607/BioNEV, including all source code and datasets, to facilitate studying various graph embedding methods on biomedical tasks. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The circular RNA circ-ERBIN promotes growth and metastasis of colorectal cancer by miR-125a-5p and miR-138-5p/4EBP-1 mediated cap-independent HIF-1α translation.

BACKGROUND: Circular RNA (circRNAs) and hypoxia have been found to play the key roles in the pathogenesis and progression of cancer including colorectal cancer (CRC). However, the expressions and functions of the specific circRNAs in regulating hypoxia-involved CRC metastasis, and the circRNAs that are relevant to regulate HIF-1α levels in CRC remain elusive. METHODS: qRT-PCR was used to detect the expression of circRNAs and mRNA in CRC cells and tissues. Fluorescence in situ hybridization (FISH) was used to analyze the location of circ-ERBIN. Function-based experiments were performed using circ-ERBIN overexpression and knockdown cell lines in vitro and in vivo, including CCK8, colony formation, EdU assay, transwell, tumor growth and metastasis models. Mechanistically, luciferase reporter assay, western blots and immunohistochemical stainings were performed. RESULTS: Circ-Erbin was highly expressed in the CRC cells and Circ-Erbin overexpression facilitated the proliferation, migration and metastasis of CRC in vitro and in vivo. Notably, circ-Erbin overexpression significantly promoted angiogenesis by increasing the expression of hypoxia induced factor (HIF-1α) in CRC. Mechanistically, circ-Erbin accelerated a cap-independent protein translation of HIF-1α in CRC cells as the sponges of miR-125a-5p and miR-138-5p, which synergistically targeted eukaryotic translation initiation factor 4E binding protein 1(4EBP-1). CONCLUSIONS: Our findings uncover a key mechanism for circ-Erbin mediated HIF-1α activation by miR-125a-5p-5p/miR-138-5p/4EBP-1 axis and circ-ERBIN is a potential target for CRC treatment.

[Values of Apparent Diffusion Coefficient and Fractional Anisotropy in the Diagnosis of Brucella Spondylitis].

Objective To compare the differences in apparent diffusion coefficient (ADC) and fractional anisotropy (FA) between brucella spondylitis (BS) groups at different stages before treatment and the normal control group and to evaluate the change trend of ADC value and FA value at different time points before and after treatment. Methods Totally 53 patients suspected of BS by conventional magnetic resonance imaging (MRI) and later confirmed as BS patients by serological tests were enrolled in this study. These patients underwent conventional MRI and diffusion tensor imaging scans,and the ADC value and FA value were measured. Independent sample t test was used to compare the ADC value and FA value between the BS group and the control group,the ADC value and FA value between the BS group at each stage. Repeated measurement ANOV was used to compare the ADC values and FA values at different time points before and after treatment. Results FA imaging showed that the color code of BS was different from that of the normal control group,and the color code of FA imaging showed increased singal. The ADC values of BS in the acute,subacute,and chronic stages [(1.45±0.02)×10 -3 mm 2/s,(1.35±0.03)×10 -3 mm 2/s,(1.26±0.05)×10 -3 mm 2/s,respectively] were significantly higher than those in the control group [(1.06±0.09) ×10 -3 mm 2/s](t=2.538,P=0.009;t=1.998,P=0.032;t=1.575,P=0.004),and the FA value (0.55±0.02,0.65±0.03,0.71±0.04,respectively) were significantly lower than those of the control group (0.78±0.02) (t=2.440,P=0.012; t=1.847,P=0.041;t=2.102,P=0.003). Repeated measurement analysis showed that there were statistically significant differences in ADC values and FA values at different time points before and after treatment in the acute,subacute,and chronic stages (ADC:F=12.100,P<0.001;F=8.439,P=0.005;F=9.704,P=0.004,respectively;FA:F=7.080,P=0.002;F=6.607;P=0.003;F=8.868,P=0.001,respectively). The ADC values at different time points after treatment were significantly lower than those before treatment or at a previous time point after treatment (F=332.14,P<0.001),and the FA values were significantly higher than those before treatment or at a previous time point after treatment (F=134.26,P<0.001). Conclusions FA color code can intuitively display differences in BS and normal vertebral bodies and show change of color code before and after treatment. Also,the ADC values and FA values can quantitatively reveal differences between BS and normal vertebral body in different time points and quantify BS vertebral lesion changes before and after treatment. In particular,in BS patients who are recovering from treatment,it can quantify microscopic edema. Therefore,diffusion tensor imaging may be useful objective indicator in evaluating the effectiveness of a specific treatment for BS.

[Quantitative Evaluation of Sciatic Nerve Crush Injury with Conventional Ultrasound Combined with Shear-wave Elastography in Rabbit Models].

Objective To explore the value of conventional ultrasound combined with shear-wave elastography in the quantitative evaluation of sciatic nerve crush injury in rabbit models. Methods Forty healthy male New Zealand white rabbits were randomly divided into four groups (n=10 in each group):three crush injury (CI) groups (2,4,and 8 weeks after crush) and control group (without injury). The thickness and stiffness of the crushed sciatic nerves and denervated triceps surae muscles were measured at different time points and compared with histopathologic parameters. Inter-reader variability was assessed with intraclass correlation coefficients. Results Compared with the control group,the inner diameters of the sciatic nerves significantly increased in the 2-week CI group [(1.65±0.34) mm vs. (0.97±0.15) mm,P=0.00] but recovered to the nearly normal level in the 8-week CI group [(1.12±0.18) mm vs. (0.97±0.15) mm,P=0.06];however,compared with control group [(8.75±1.02)kPa],the elastic modulus of the nerves increased significantly in all the CI groups [2-week:(14.77±2.53) kPa;4-week:(19.12±3.46) kPa;and 8-week:(28.39±5.26) kPa;all P=0.00];pathologically,massive hyperplasia of collagen fibers were found in the nerve tissues. The thickness of denervated triceps surae muscle decreased gradually,and the elastic modulus decreased 2 weeks after injury but increased gradually in the following 6 weeks;pathologically,massive hyperplasia of collagen fibers and adipocytes infiltration were visible,along with decreased muscle wet-weight ratio and muscle fiber cross-sectional area. The inter-reader agreements were good. Conclusion Conventional ultrasound combined with shear-wave elastography is feasible for the quantitative evaluation of the morphological and mechanical properties of crushed nerves and denervated muscles.

Prediction of drug-disease associations based on ensemble meta paths and singular value decomposition.

BACKGROUND: In the field of drug repositioning, it is assumed that similar drugs may treat similar diseases, therefore many existing computational methods need to compute the similarities of drugs and diseases. However, the calculation of similarity depends on the adopted measure and the available features, which may lead that the similarity scores vary dramatically from one to another, and it will not work when facing the incomplete data. Besides, supervised learning based methods usually need both positive and negative samples to train the prediction models, whereas in drug-disease pairs data there are only some verified interactions (positive samples) and a lot of unlabeled pairs. To train the models, many methods simply treat the unlabeled samples as negative ones, which may introduce artificial noises. Herein, we propose a method to predict drug-disease associations without the need of similarity information, and select more likely negative samples. RESULTS: In the proposed EMP-SVD (Ensemble Meta Paths and Singular Value Decomposition), we introduce five meta paths corresponding to different kinds of interaction data, and for each meta path we generate a commuting matrix. Every matrix is factorized into two low rank matrices by SVD which are used for the latent features of drugs and diseases respectively. The features are combined to represent drug-disease pairs. We build a base classifier via Random Forest for each meta path and five base classifiers are combined as the final ensemble classifier. In order to train out a more reliable prediction model, we select more likely negative ones from unlabeled samples under the assumption that non-associated drug and disease pair have no common interacted proteins. The experiments have shown that the proposed EMP-SVD method outperforms several state-of-the-art approaches. Case studies by literature investigation have found that the proposed EMP-SVD can mine out many drug-disease associations, which implies the practicality of EMP-SVD. CONCLUSIONS: The proposed EMP-SVD can integrate the interaction data among drugs, proteins and diseases, and predict the drug-disease associations without the need of similarity information. At the same time, the strategy of selecting more reliable negative samples will benefit the prediction.

SFPEL-LPI: Sequence-based feature projection ensemble learning for predicting LncRNA-protein interactions.

LncRNA-protein interactions play important roles in post-transcriptional gene regulation, poly-adenylation, splicing and translation. Identification of lncRNA-protein interactions helps to understand lncRNA-related activities. Existing computational methods utilize multiple lncRNA features or multiple protein features to predict lncRNA-protein interactions, but features are not available for all lncRNAs or proteins; most of existing methods are not capable of predicting interacting proteins (or lncRNAs) for new lncRNAs (or proteins), which don't have known interactions. In this paper, we propose the sequence-based feature projection ensemble learning method, "SFPEL-LPI", to predict lncRNA-protein interactions. First, SFPEL-LPI extracts lncRNA sequence-based features and protein sequence-based features. Second, SFPEL-LPI calculates multiple lncRNA-lncRNA similarities and protein-protein similarities by using lncRNA sequences, protein sequences and known lncRNA-protein interactions. Then, SFPEL-LPI combines multiple similarities and multiple features with a feature projection ensemble learning frame. In computational experiments, SFPEL-LPI accurately predicts lncRNA-protein associations and outperforms other state-of-the-art methods. More importantly, SFPEL-LPI can be applied to new lncRNAs (or proteins). The case studies demonstrate that our method can find out novel lncRNA-protein interactions, which are confirmed by literature. Finally, we construct a user-friendly web server, available at http://www.bioinfotech.cn/SFPEL-LPI/.

Predicting drug-disease associations and their therapeutic function based on the drug-disease association bipartite network.

Drug-disease associations provide important information for drug discovery and drug repositioning. Drug-disease associations can induce different effects, and the therapeutic effect attracts wide spread interest. Therefore, developing drug-disease association prediction methods is an important task, and differentiating therapeutic associations from other associations is also very important. In this paper, we formulate the known drug-disease associations as a bipartite network, and then present a novel representation for drugs and diseases based on the bipartite network and linear neighborhood similarity. Thus, we propose the network topological similarity-based inference method (NTSIM) to predict unobserved drug-disease associations. Further, we extend the work to the association classification, and propose the network topological similarity-based classification method (NTSIM-C) to differentiate therapeutic associations from others. Compared with existing drug-disease association prediction methods, NTSIM can produce superior performances in predicting drug-disease associations, and NTSIM-C can accurately classify drug-disease associations. Further, we analyze the capability of proposed methods by using several case studies. The studies show the usefulness of NTSIM and NTSIM-C in the real applications. In conclusion, NTSIM and NTSIM-C are promising for predicting drug-disease associations and their therapeutic functions.

A Multicenter Large-Sample Shear Wave Ultrasound Elastographic Study of the Achilles Tendon in Chinese Adults.

OBJECTIVES: The changes in the viscoelasticity of the Achilles tendon are related to tendinopathy. Therefore, constructing a data model in the healthy population is essential to understanding the key factors affecting the viscoelasticity of the Achilles tendon. The purpose of our research was to obtain large sample data, construct a data model, and determine parameters that affect the elastic modulus of the Achilles tendon in healthy Chinese adults. METHODS: We designed a prospective multicenter clinical trial to evaluate the viscoelasticity of the Achilles tendon by using shear wave elastography. A total of 1165 healthy adult participants from 17 Chinese hospitals were recruited for the assessment. The necessary parameters (age, height, weight, and body mass index) were recorded. The elastic modulus (Young modulus) was obtained from the middle of the Achilles tendon and calculated with feet in naturally relaxed, dorsal, and plantar positions. The thickness and perimeter of the Achilles tendon were measured via cross section on the same site. A multiple linear regression was performed to find the key factors affecting the Young modulus of the Achilles tendon. RESULTS: The Young modulus of the left Achilles tendon in the natural relaxed position followed a normal distribution (P > .05) with a mean ± SD of 374.24 ± 106.12 kPa. The regression equations showed a positive correlation between the Young modulus and weight and a negative correlation between the Young modulus and the circumference or thickness of the left Achilles tendon (P < .05). CONCLUSIONS: The Young modulus of the Achilles tendon as measured by shear wave elastography is related to body weight as well as the perimeter or thickness of the tendon.

Sequence-based bacterial small RNAs prediction using ensemble learning strategies.

BACKGROUND: Bacterial small non-coding RNAs (sRNAs) have emerged as important elements in diverse physiological processes, including growth, development, cell proliferation, differentiation, metabolic reactions and carbon metabolism, and attract great attention. Accurate prediction of sRNAs is important and challenging, and helps to explore functions and mechanism of sRNAs. RESULTS: In this paper, we utilize a variety of sRNA sequence-derived features to develop ensemble learning methods for the sRNA prediction. First, we compile a balanced dataset and four imbalanced datasets. Then, we investigate various sRNA sequence-derived features, such as spectrum profile, mismatch profile, reverse compliment k-mer and pseudo nucleotide composition. Finally, we consider two ensemble learning strategies to integrate all features for building ensemble learning models for the sRNA prediction. One is the weighted average ensemble method (WAEM), which uses the linear weighted sum of outputs from the individual feature-based predictors to predict sRNAs. The other is the neural network ensemble method (NNEM), which trains a deep neural network by combining diverse features. In the computational experiments, we evaluate our methods on these five datasets by using 5-fold cross validation. WAEM and NNEM can produce better results than existing state-of-the-art sRNA prediction methods. CONCLUSIONS: WAEM and NNEM have great potential for the sRNA prediction, and are helpful for understanding the biological mechanism of bacteria.

Predicting drug-disease associations by using similarity constrained matrix factorization.

BACKGROUND: Drug-disease associations provide important information for the drug discovery. Wet experiments that identify drug-disease associations are time-consuming and expensive. However, many drug-disease associations are still unobserved or unknown. The development of computational methods for predicting unobserved drug-disease associations is an important and urgent task. RESULTS: In this paper, we proposed a similarity constrained matrix factorization method for the drug-disease association prediction (SCMFDD), which makes use of known drug-disease associations, drug features and disease semantic information. SCMFDD projects the drug-disease association relationship into two low-rank spaces, which uncover latent features for drugs and diseases, and then introduces drug feature-based similarities and disease semantic similarity as constraints for drugs and diseases in low-rank spaces. Different from the classic matrix factorization technique, SCMFDD takes the biological context of the problem into account. In computational experiments, the proposed method can produce high-accuracy performances on benchmark datasets, and outperform existing state-of-the-art prediction methods when evaluated by five-fold cross validation and independent testing. CONCLUSION: We developed a user-friendly web server by using known associations collected from the CTD database, available at http://www.bioinfotech.cn/SCMFDD/ . The case studies show that the server can find out novel associations, which are not included in the CTD database.