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.

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.

A randomised dose-response study of prophylactic Methoxamine infusion for preventing spinal-induced hypotension during Cesarean delivery.

BACKGROUND: α-receptor agonists have been reported to be safe and effective for treating or preventing spinal-induced hypotension during cesarean delivery. As a pure α1 adrenergic agonist, methoxamine has potential advantages of reducing myocardial oxygen consumption and protecting the heart in obstetric patients compared to phenylephrine. The aim of this study was to determine the optimal prophylactic methoxamine infusion dose that would be effective for preventing spinal-induced hypotension in 50% (ED50) and 95% (ED95) of parturients. METHODS: Eighty parturients with a singleton pregnancy scheduled for elective cesarean delivery were randomly allocated to receive prophylactic methoxamine infusion at one of four different fixed-rates: 1 µg/kg/min (group M1), 2 µg/kg/min (group M2), 3 µg/kg/min (group M3), or 4 µg/kg/min (group M4). An adequate response was defined as absence of hypotension (maternal SBP < 80% of baseline or SBP < 90 mmHg). The values for ED50 and ED95 of prophylactic methoxamine infusion were determined by probit regression model. The outcomes of maternal hemodynamics and fetal status were compared among the groups. RESULTS: The calculated ED50 and ED95 (95% confidence interval) of prophylactic methoxamine infusion dose were 2.178 (95% CI 1.564 to 2.680) µg/kg/min and 4.821 (95% CI 3.951 to 7.017) µg/kg/min, respectively. The incidence of hypotension decreased with increasing methoxamine infusion dose (15/20, 11/20, 7/20 and 2/20 in group M1, M2, M3 and M4 respectively, P <  0.001). 1-min Apgar scores and umbilical arterial PaO2 were lower but umbilical arterial PaCO2 was higher in Group M1. No difference was found in the other incidence of adverse effects and neonatal outcomes among groups. CONCLUSIONS: Under the conditions of this study, when prophylactic methoxamine infusion was given at a fixed-rate based on body weight for preventing spinal-induced hypotension in obstetric patients, the values for ED50 and ED95 were 2.178 µg/kg/min and 4.821 µg/kg/min respectively. CLINICAL TRIAL REGISTRATION: Chinese Clinical Trial Registry (ChiCTR), registry number of clinical trial: ChiCTR-1,800,018,988 , date of registration: October 20, 2018.

Nerve injury elevates functional Cav3.2 channels in superficial spinal dorsal horn.

Cav3 channels play an important role in modulating chronic pain. However, less is known about the functional changes of Cav3 channels in superficial spinal dorsal horn in neuropathic pain states. Here, we examined the effect of partial sciatic nerve ligation (PSNL) on either expression or electrophysiological properties of Cav3 channels in superficial spinal dorsal horn. Our in vivo studies showed that the blockers of Cav3 channels robustly alleviated PSNL-induced mechanical allodynia and thermal hyperalgesia, which lasted at least 14 days following PSNL. Meanwhile, PSNL triggered an increase in both mRNA and protein levels of Cav3.2 but not Cav3.1 or Cav3.3 in rats. However, in Cav3.2 knockout mice, PSNL predominantly attenuated mechanical allodynia but not thermal hyperalgesia. In addition, the results of whole-cell patch-clamp recordings showed that both the overall proportion of Cav3 current-expressing neurons and the Cav3 current density in individual neurons were elevated in spinal lamina II neurons from PSNL rats, which could not be recapitulated in Cav3.2 knockout mice. Altogether, our findings reveal that the elevated functional Cav3.2 channels in superficial spinal dorsal horn may contribute to the mechanical allodynia in PSNL-induced neuropathic pain model.

Multi-Kernel Graph Attention Deep Autoencoder for MiRNA-Disease Association Prediction.

Accumulating evidence indicates that microRNAs (miRNAs) can control and coordinate various biological processes. Consequently, abnormal expressions of miRNAs have been linked to various complex diseases. Recognizable proof of miRNA-disease associations (MDAs) will contribute to the diagnosis and treatment of human diseases. Nevertheless, traditional experimental verification of MDAs is laborious and limited to small-scale. Therefore, it is necessary to develop reliable and effective computational methods to predict novel MDAs. In this work, a multi-kernel graph attention deep autoencoder (MGADAE) method is proposed to predict potential MDAs. In detail, MGADAE first employs the multiple kernel learning (MKL) algorithm to construct an integrated miRNA similarity and disease similarity, providing more biological information for further feature learning. Second, MGADAE combines the known MDAs, disease similarity, and miRNA similarity into a heterogeneous network, then learns the representations of miRNAs and diseases through graph convolution operation. After that, an attention mechanism is introduced into MGADAE to integrate the representations from multiple graph convolutional network (GCN) layers. Lastly, the integrated representations of miRNAs and diseases are input into the bilinear decoder to obtain the final predicted association scores. Corresponding experiments prove that the proposed method outperforms existing advanced approaches in MDA prediction. Furthermore, case studies related to two human cancers provide further confirmation of the reliability of MGADAE in practice.

FSCME: A Feature Selection Method Combining Copula Correlation and Maximal Information Coefficient by Entropy Weights.

Feature selection is a critical component of data mining and has garnered significant attention in recent years. However, feature selection methods based on information entropy often introduce complex mutual information forms to measure features, leading to increased redundancy and potential errors. To address this issue, we propose FSCME, a feature selection method combining Copula correlation (Ccor) and the maximum information coefficient (MIC) by entropy weights. The FSCME takes into consideration the relevance between features and labels, as well as the redundancy among candidate features and selected features. Therefore, the FSCME utilizes Ccor to measure the redundancy between features, while also estimating the relevance between features and labels. Meanwhile, the FSCME employs MIC to enhance the credibility of the correlation between features and labels. Moreover, this study employs the Entropy Weight Method (EWM) to evaluate and assign weights to the Ccor and MIC. The experimental results demonstrate that FSCME yields a more effective feature subset for subsequent clustering processes, significantly improving the classification performance compared to the other six feature selection methods. The source codes of the FSCME are available online at https://github.com/CDMBlab/FSCME.

Deep Self-Reconstruction Fusion Similarity Hashing for the Diagnosis of Alzheimer's Disease on Multi-Modal Data.

The pathogenesis of Alzheimer's disease (AD) is extremely intricate, which makes AD patients almost incurable. Recent studies have demonstrated that analyzing multi-modal data can offer a comprehensive perspective on the different stages of AD progression, which is beneficial for early diagnosis of AD. In this paper, we propose a deep self-reconstruction fusion similarity hashing (DS-FSH) method to effectively capture the AD-related biomarkers from the multi-modal data and leverage them to diagnose AD. Given that most existing methods ignore the topological structure of the data, a deep self-reconstruction model based on random walk graph regularization is designed to reconstruct the multi-modal data, thereby learning the nonlinear relationship between samples. Additionally, a fused similarity hash based on anchor graph is proposed to generate discriminative binary hash codes for multi-modal reconstructed data. This allows sample fused similarity to be effectively modeled by a fusion similarity matrix based on anchor graph while modal correlation can be approximated by Hamming distance. Especially, extracted features from the multi-modal data are classified using deep sparse autoencoders classifier. Finally, experiments conduct on the AD Neuroimaging Initiative database show that DS-FSH outperforms comparable methods of AD classification. To conclude, DS-FSH identifies multi-modal features closely associated with AD, which are expected to contribute significantly to understanding of the pathogenesis of AD.

Diagnosis-Guided Deep Subspace Clustering Association Study for Pathogenetic Markers Identification of Alzheimer's Disease Based on Comparative Atlases.

The roles of brain region activities and genotypic functions in the pathogenesis of Alzheimer's disease (AD) remain unclear. Meanwhile, current imaging genetics methods are difficult to identify potential pathogenetic markers by correlation analysis between brain network and genetic variation. To discover disease-related brain connectome from the specific brain structure and the fine-grained level, based on the Automated Anatomical Labeling (AAL) and human Brainnetome atlases, the functional brain network is first constructed for each subject. Specifically, the upper triangle elements of the functional connectivity matrix are extracted as connectivity features. The clustering coefficient and the average weighted node degree are developed to assess the significance of every brain area. Since the constructed brain network and genetic data are characterized by non-linearity, high-dimensionality, and few subjects, the deep subspace clustering algorithm is proposed to reconstruct the original data. Our multilayer neural network helps capture the non-linear manifolds, and subspace clustering learns pairwise affinities between samples. Moreover, most approaches in neuroimaging genetics are unsupervised learning, neglecting the diagnostic information related to diseases. We presented a label constraint with diagnostic status to instruct the imaging genetics correlation analysis. To this end, a diagnosis-guided deep subspace clustering association (DDSCA) method is developed to discover brain connectome and risk genetic factors by integrating genotypes with functional network phenotypes. Extensive experiments prove that DDSCA achieves superior performance to most association methods and effectively selects disease-relevant genetic markers and brain connectome at the coarse-grained and fine-grained levels.

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.

Visualization and Analysis of Single Cell RNA-Seq Data by Maximizing Correntropy Based Non-Negative Low Rank Representation.

The exploration of single cell RNA-sequencing (scRNA-seq) technology generates a new perspective to analyze biological problems. One of the major applications of scRNA-seq data is to discover subtypes of cells by cell clustering. Nevertheless, it is challengeable for traditional methods to handle scRNA-seq data with high level of technical noise and notorious dropouts. To better analyze single cell data, a novel scRNA-seq data analysis model called Maximum correntropy criterion based Non-negative and Low Rank Representation (MccNLRR) is introduced. Specifically, the maximum correntropy criterion, as an effective loss function, is more robust to the high noise and large outliers existed in the data. Moreover, the low rank representation is proven to be a powerful tool for capturing the global and local structures of data. Therefore, some important information, such as the similarity of cells in the subspace, is also extracted by it. Then, an iterative algorithm on the basis of the half-quadratic optimization and alternating direction method is developed to settle the complex optimization problem. Before the experiment, we also analyze the convergence and robustness of MccNLRR. At last, the results of cell clustering, visualization analysis, and gene markers selection on scRNA-seq data reveal that MccNLRR method can distinguish cell subtypes accurately and robustly.

HTRPCA: Hypergraph Regularized Tensor Robust Principal Component Analysis for Sample Clustering in Tumor Omics Data.

In recent years, clustering analysis of cancer genomics data has gained widespread attention. However, limited by the dimensions of the matrix, the traditional methods cannot fully mine the underlying geometric structure information in the data. Besides, noise and outliers inevitably exist in the data. To solve the above two problems, we come up with a new method which uses tensor to represent cancer omics data and applies hypergraph to save the geometric structure information in original data. This model is called hypergraph regularized tensor robust principal component analysis (HTRPCA). The data processed by HTRPCA becomes two parts, one of which is a low-rank component that contains pure underlying structure information between samples, and the other is some sparse interference points. So we can use the low-rank component for clustering. This model can retain complex geometric information between more sample points due to the addition of the hypergraph regularization. Through clustering, we can demonstrate the effectiveness of HTRPCA, and the experimental results on TCGA datasets demonstrate that HTRPCA precedes other advanced methods. This paper proposes a new method of using tensors to represent cancer omics data and introduces hypergraph items to save the geometric structure information of the original data. At the same time, the model decomposes the original tensor into low-order tensors and sparse tensors. The low-rank tensor was used to cluster cancer samples to verify the effectiveness of the method.

Genetic Deficiency of p53 Leads to Structural, Functional, and Synaptic Deficits in Primary Somatosensory Cortical Neurons of Adult Mice.

The tumor suppressor p53 plays a crucial role in embryonic neuron development and neurite growth, and its involvement in neuronal homeostasis has been proposed. To better understand how the lack of the p53 gene function affects neuronal activity, spine development, and plasticity, we examined the electrophysiological and morphological properties of layer 5 (L5) pyramidal neurons in the primary somatosensory cortex barrel field (S1BF) by using in vitro whole-cell patch clamp and in vivo two-photon imaging techniques in p53 knockout (KO) mice. We found that the spiking frequency, excitatory inputs, and sag ratio were decreased in L5 pyramidal neurons of p53KO mice. In addition, both in vitro and in vivo morphological analyses demonstrated that dendritic spine density in the apical tuft is decreased in L5 pyramidal neurons of p53KO mice. Furthermore, chronic imaging showed that p53 deletion decreased dendritic spine turnover in steady-state conditions, and prevented the increase in spine turnover associated with whisker stimulation seen in wildtype mice. In addition, the sensitivity of whisker-dependent texture discrimination was impaired in p53KO mice compared with wildtype controls. Together, these results suggest that p53 plays an important role in regulating synaptic plasticity by reducing neuronal excitability and the number of excitatory synapses in S1BF.

1,4-Bis[(1H-pyrazol-1-yl)meth-yl]benzene.

In the title compound, C(14)H(14)N(4), the center of the phenyl-ene group is a crystallographic center of inversion. The compound is composed of three aromatic rings displaying a Z-like conformation. The dihedral angle between the pyrazole rings and the central phenyl ring is 83.84 (9)°.

1,4-Bis(1H-benzimidazol-1-yl)but-2-ene.

In the pseudo-centrosymmetric mol-ecule of the title compound, C(18)H(16)N(4), two benzimidazole fragments form the dihedral angles of 83.49 (7) and 79.37 (7)°, with the mean plane of the linking butene chain. No classical inter-molecular inter-actions are observed. The porous crystal packing exhibits voids of 85 Å(3).

NLRC5: A Potential Target for Central Nervous System Disorders.

Nucleotide oligomerization domain-like receptors (NLRs), a class of pattern recognition receptors, participate in the host's first line of defense against invading pathogenic microorganisms. NLR family caspase recruitment domain containing 5 (NLRC5) is the largest member of the NLR family and has been shown to play an important role in inflammatory processes, angiogenesis, immunity, and apoptosis by regulating the nuclear factor-κB, type I interferon, and inflammasome signaling pathways, as well as the expression of major histocompatibility complex I genes. Recent studies have found that NLRC5 is also associated with neuronal development and central nervous system (CNS) diseases, such as CNS infection, cerebral ischemia/reperfusion injury, glioma, multiple sclerosis, and epilepsy. This review summarizes the research progress in the structure, expression, and biological characteristics of NLRC5 and its relationship with the CNS.

Hyper-Graph Regularized Constrained NMF for Selecting Differentially Expressed Genes and Tumor Classification.

Non-negative Matrix Factorization (NMF) is a dimensionality reduction approach for learning a parts-based and linear representation of non-negative data. It has attracted more attention because of that. In practice, NMF not only neglects the manifold structure of data samples, but also overlooks the priori label information of different classes. In this paper, a novel matrix decomposition method called Hyper-graph regularized Constrained Non-negative Matrix Factorization (HCNMF) is proposed for selecting differentially expressed genes and tumor sample classification. The advantage of hyper-graph learning is to capture local spatial information in high dimensional data. This method incorporates a hyper-graph regularization constraint to consider the higher order data sample relationships. The application of hyper-graph theory can effectively find pathogenic genes in cancer datasets. Besides, the label information is further incorporated in the objective function to improve the discriminative ability of the decomposition matrix. Supervised learning with label information greatly improves the classification effect. We also provide the iterative update rules and convergence proofs for the optimization problems of HCNMF. Experiments under The Cancer Genome Atlas (TCGA) datasets confirm the superiority of HCNMF algorithm compared with other representative algorithms through a set of evaluations.

Estrogen modulation of visceral pain.

It is commonly accepted that females and males differ in their experience of pain. Gender differences have been found in the prevalence and severity of pain in both clinical and animal studies. Sex-related hormones are found to be involved in pain transmission and have critical effects on visceral pain sensitivity. Studies have pointed out the idea that serum estrogen is closely related to visceral nociceptive sensitivity. This review aims to summarize the literature relating to the role of estrogen in modulating visceral pain with emphasis on deciphering the potential central and peripheral mechanisms.

Identification and Antibiotic Resistance Assessment of Ensifer adhaerens YX1, a Vitamin B12 -Producing Strain Used as a Food and Feed Additive.

This study provides phenotypic and molecular analyses of the antibiotic resistance of Ensifer adhaerens strain YX1 (CICC 11008s), a strain that was identified using a polyphasic taxonomy approach. The antibiotic resistance profile of E. adhaerens YX1 was assessed using the Clinical & Laboratory Standards Inst. (CLSI) method. The strain was susceptible to ciprofloxacin, levofloxacin, norfloxacin, ofloxacin, gentamicin, tobramycin, chloramphenicol, tetracycline, imipenem, and ceftazidime, and resistant to kanamycin, streptomycin, fosfomycin, and nitrofurantoin. The antibiotic resistance genes nsfA, nsfB, fosA, aph, and aadA1 were not detected in E. adhaerens YX1 via PCR using gene-specific primers. Subsequently, the genome sequence of E. adhaerens was screened for antibiotic genes. Although no antibiotic resistance genes were identified using the ResFinder database, five genes copies of one resistance gene, adeF, were detected using the Comprehensive Antibiotic Resistance Database (CARD). The results of this study will be useful for understanding the phenotypic and genotypic aspects of E. adhaerens antibiotic resistance. No safety issues were identified for E. adhaerens YX1 in terms of antibiotic resistance. Performing similar studies will be conducive to the safety assessment and control of the use of E. adhaerens in the food and feed industry. PRACTICAL APPLICATION: Few relevant reports are currently available regarding antibiotic resistance assessments or other safety evaluations for Ensifer adhaerens. Because of a lack of relevant information on the safety of this bacterium, including the genetic basis of antibiotic resistance in the production strain, it has not been recommended for use in the "qualified presumption of safety" (QPS) list and subsequent updated lists. The current study shows no safety issue of E. adhaerens YX1 in terms of its antibiotic resistance. These results are important as they provide an initial basis for an understanding of the antibiotic resistance/susceptibility of E. adhaerens YX1 (CICC 11008s), which produces vitamin B12 and is widely used in the food and feed industry.

Chemical Components and Pharmacological Action for Angelica dahurica Sinensis and Predictive Analysis on its Q-marker / 世界科学技术-中医药现代化

Angelica dahurica,as a medicinal and edible traditional Chinese medicine,is widely used in clinical practice.It is mainly cultivated in Henan,Hebei,Sichuang and Zhejiang provinces.At present,the study found that the main chemical components of this plant contained coumarins,volatile oil,polysaccharides,alkaloids,and amino acids and other components.It has anti-inflammatory,analgesic,anti-oxidant,anti-tumor,whitening effect and other pharmacological activities.In this paper,the chemical composition,pharmacological action of Angelica dahurica were summarized.Based on the core concepts of quality markers,the quality markers of Angelica dahurica were predicted and analyzed from the aspects of plant relativity and chemical component specificity,traditional medicinal properties,traditional efficacy,measurability of chemical components,Sulfur Fumigated,so as to provide reference for establishment and development of quality evaluation system of Angelica dahurica.

Latent profile analysis and influencing factors of health emergency literacy among emergency department nurses / 中国实用护理杂志

Objective:To investigate the current situation of health emergency response literacy among emergency nurses, analyze the potential profile of emergency nurses′ health emergency response literacy and explore the influencing factors of different categories, so as to provide reference for formulating targeted and specific training intervention programs.Methods:From September to October 2022, 386 emergency nurses from 5 tertiary A-level hospitals in Shandong Province were selected as the research objects by convenient sampling method. A cross-sectional survey was conducted by using the general information questionnaire, the Literacy Self-assessment Scale of Public Health Emergency Response In Nurses, the Chinese version of Modified Self-leadership Questionnaire, and Nurse Perceived Professional Value Scale. The latent profile analysis was carried out with the results of 3 explicit indexes of the Literacy Self-assessment Scale of Public Health Emergency Response In Nurses. At the same time, the influencing factors of different latent profiles of health emergency literacy were explored by univariate analysis and ordered multi-classification Logistic regression.Results:The health emergency literacy of emergency department nurses was divided into three latent profiles: high health emergency literacy type accounting for 45.4% (175/386), medium health emergency literacy type accounting for 41.5% (160/386), and low health emergency literacy type accounting for 13.1% (51/386). Ordinal Logistic regression showed that the main emergency work post was pre hospital first aid, participated in public health emergency rescue operations, self leadership, and professional value were protective factors of health emergency literacy (all P<0.05). Age≤25 years old and junior college degree were risk factors of health emergency literacy (both P<0.05). Conclusions:Nursing managers can implement personalized training and intervention strategies according to different demographic characteristics of the health emergency literacy of emergency nurses, develop self leadership improvement plans from individual and organizational factors, and actively cultivate their sense of professional value, in order to comprehensively improve the health emergency literacy of emergency nurses.