Prediction of Potential Small Molecule-Associated MicroRNAs Using Graphlet Interaction.

MicroRNAs (miRNAs) have been proved to be targeted by the small molecules recently, which made using small molecules to target miRNAs become a possible therapy for human diseases. Therefore, it is very meaningful to investigate the relationships between small molecules and miRNAs, which is still yet in the newly-developing stage. In this paper, we presented a prediction model of Graphlet Interaction based inference for Small Molecule-MiRNA Association prediction (GISMMA) by combining small molecule similarity network, miRNA similarity network and known small molecule-miRNA association network.

Nanomedicines guided nanoimaging probes and nanotherapeutics for early detection of lung cancer and abolishing pulmonary metastasis: Critical appraisal of newer developments and challenges to clinical transition.

Lung cancer (LC) is the second most prevalent type of cancer and primary cause of mortality among both men and women, worldwide. The most commonly employed diagnostic modalities for LC include chest X-ray (CXR), magnetic-resonance-imaging (MRI), computed tomography (CT-scan), and fused-positron-emitting-tomography-CT (PET-CT). Owing to several limitations associated with the use of conventional diagnostic tools such as radiation burden to the patient, misleading diagnosis ("missed lung cancer"), false staging and low sensitivity and resolution, contemporary diagnostic regimen needed to be employed for screening of LC.

Context Impacts in Accelerometer-Based Walk Detection and Step Counting.

Walk detection (WD) and step counting (SC) have become popular applications in the recent emergence of wearable devices. These devices monitor user states and process data from MEMS-based accelerometers and optional gyroscope sensors. Various algorithms have been proposed for WD and SC, which are generally sensitive to the contexts of applications, i.

Efficient Mining Template of Predictive Temporal Clinical Event Patterns From Patient Electronic Medical Records.

Exploring the temporal relationship among events in patient electronic medical records (EMR) is an important problem in biomedical informatics and the results can reveal patients' impending disease conditions. In this paper, we investigate the problem of mining patterns from a sequence of point events, i.e.

MicroRNA-small molecule association identification: from experimental results to computational models.

Small molecule is a kind of low molecular weight organic compound with variety of biological functions. Studies have indicated that small molecules can inhibit a specific function of a multifunctional protein or disrupt protein-protein interactions and may have beneficial or detrimental effect against diseases. MicroRNAs (miRNAs) play crucial roles in cellular biology, which makes it possible to develop miRNA as diagnostics and therapeutic targets.

Extramedullary relapse of acute lymphoblastic leukemia after allogeneic hematopoietic stem cell transplantation treated by CAR T-cell therapy: a case report.

Philadelphia chromosome-positive (Ph-positive) acute leukemia (ALL) accounts for around one quarter of adult cases of ALL and is usually associated with poor prognosis. The patients still encounter a high rate of relapse even after they receive hematopoietic stem cell transplantation (HSCT). HSCT is considered the established therapy and best option for many malignant ALL cases, however, disease relapse remains the main reason of failure.

A Hybrid Interpolation Weighted Collaborative Filtering Method for Anti-cancer Drug Response Prediction.

Individualized therapies ask for the most effective regimen for each patient, while the patients' response may differ from each other. However, it is impossible to clinically evaluate each patient's response due to the large population. Human cell lines have harbored most of the same genetic changes found in patients' tumors, thus are widely used to help understand initial responses of drugs.

Hybrid Catalyst of a Metal-Organic Framework, Metal Nanoparticles, and Oxide That Enables Strong Steric Constraint and Metal-Support Interaction for the Highly Effective and Selective Hydrogenation of Cinnamaldehyde.

In this work, we designed a hybrid catalyst composed of a metal-organic framework (MOF), Pt nanoparticles (NPs), and ferric oxide, namely, Co-MOF-74@(Pt@FeO), which enables not only high turnover frequencies of up to 245.7 h but also ultrahigh 100% selectivity toward cinnamyl alcohol in the hydrogenation of cinnamaldehyde under mild conditions. This excellent performance is attributed to the fact that such a hybrid catalyst enables not only strong steric constraint to provide the favored C═O adsorption of cinnamaldehyde but also strong metal-support interaction to lower the electron density of Pt NPs.

Computational models for lncRNA function prediction and functional similarity calculation.

From transcriptional noise to dark matter of biology, the rapidly changing view of long non-coding RNA (lncRNA) leads to deep understanding of human complex diseases induced by abnormal expression of lncRNAs. There is urgent need to discern potential functional roles of lncRNAs for further study of pathology, diagnosis, therapy, prognosis, prevention of human complex disease and disease biomarker detection at lncRNA level. Computational models are anticipated to be an effective way to combine current related databases for predicting most potential lncRNA functions and calculating lncRNA functional similarity on the large scale.

Dicationic Ionic Liquids of Herbicide 2,4-Dichlorophenoxyacetic Acid with Reduced Negative Effects on Environment.

Due to high volatility and water solubility, 2,4-dichlorophenoxyacetic acid (2,4-D) can easily enter into the atmosphere and water bodies by volatilization, drift, leaching, or runoff, which results in potential threats to the environment and human health. The physicochemical properties of pesticides can be regulated by preparing their ionic liquids. In this work, a series of dicationic ionic liquids (DILs) of 2,4-D were prepared to reduce its environmental risk and enhance herbicidal activity.

Height at three months can indicate overweight at two years in catch-up growth of small for gestational age infants.

This study aimed to find an indicator at three months to predict overweight and short stature at two years in small for gestational age (SGA) infants. A total of 468 SGA infants and 4642 appropriate for gestational age (AGA) infants were included. Weight and height were measured at birth, three months and two years.

Reliable Link Inference for Network Data With Community Structures.

Complex systems are often characterized by complex networks with links and entities. However, in many complex systems such as protein-protein interaction networks, recommender systems, and online communities, their links are hard to reveal directly, but they can be inaccurately observed by multiple data collection platforms or by a data collection platform at different times. Then, the links of the systems are inferred by the integration of the collected observations.

Both maternal and paternal risk factors for term singleton low birthweight infants in rural Chinese population: a population-based, retrospective cohort study.

No large population-based study has focused on both maternal paternal risk factors for low birthweight (LBW) in China. We aimed to identify parental risk factors associated with LBW.A population-based, retrospective cohort study was conducted on 202,725 singleton infants at 37-42 weeks.

Exploiting the semantic graph for the representation and retrieval of medical documents.

Objective: The objective of this study was to propose a graph-based semantic search approach by addressing the inherent complexity and ambiguity of medical terminology in queries and clinical text for enhanced medical information retrieval.

Methods: The supportive use of a medical domain ontology exploits the light-weight semantics discovered from queries and documents for enhanced document ranking. First, the implicit information regarding concepts and the relations between them is discovered in the documents and queries and is used to evaluate the relevance of the query-document; then, the semantic linkages between concepts distributed in target documents and reference documents are built and used to score the document's popularity; finally, the above two evaluations are integrated to produce the final ranking list for document ranking.

Knockdown of lncRNA BDNF-AS suppresses neuronal cell apoptosis via downregulating miR-130b-5p target gene PRDM5 in acute spinal cord injury.

Objective: The present study was designed to investigate the molecular mechanism and biological roles of lncRNA brain-derived neurotrophic factor antisense (lncRNA BDNF-AS) in acute spinal cord injury (ASCI).

Methods: The rat model of ASCI and hypoxic cellular model were established to detect the expression of BDNF-AS, miR-130b-5p, PR (PRDI-BF1 and RIZ) domain protein 5 (PRDM5) and cleaved caspase 3 (c-caspase 3) using qRT-PCR and western blot. Basso, Beattie and Bresnahan (BBB) score was carried out to assess neurological function.

Low ecological representation in the protected area network of China.

Protected areas are considered as an essential strategy to halt the decline of biodiversity. Ecological representation in protected areas is crucial for assessment on the progress toward conservation targets. Although China has established a large number of protected areas since the 1950s, ecological representation of protected areas is poorly understood.

Inferring potential small molecule-miRNA association based on triple layer heterogeneous network.

Recently, many biological experiments have indicated that microRNAs (miRNAs) are a newly discovered small molecule (SM) drug targets that play an important role in the development and progression of human complex diseases. More and more computational models have been developed to identify potential associations between SMs and target miRNAs, which would be a great help for disease therapy and clinical applications for known drugs in the field of medical research. In this study, we proposed a computational model of triple layer heterogeneous network based small molecule-MiRNA association prediction (TLHNSMMA) to uncover potential SM-miRNA associations by integrating integrated SM similarity, integrated miRNA similarity, integrated disease similarity, experimentally verified SM-miRNA associations and miRNA-disease associations into a heterogeneous graph.

Predicting miRNA-disease association based on inductive matrix completion.

Motivation: It has been shown that microRNAs (miRNAs) play key roles in variety of biological processes associated with human diseases. In Consideration of the cost and complexity of biological experiments, computational methods for predicting potential associations between miRNAs and diseases would be an effective complement.

Results: This paper presents a novel model of Inductive Matrix Completion for MiRNA-Disease Association prediction (IMCMDA).

Development of a computer-aided tool for the pattern recognition of facial features in diagnosing Turner syndrome: comparison of diagnostic accuracy with clinical workers.

Technologies applied for the recognition of facial features in diagnosing certain disorders seem to be promising in reducing the medical burden and improve the efficiency. This pilot study aimed to develop a computer-assisted tool for the pattern recognition of facial features for diagnosing Turner syndrome (TS). Photographs of 54 patients with TS and 158 female controls were collected from July 2016 to May 2017.

Upregulation of microRNA-335-5p reduces inflammatory responses by inhibiting FASN through the activation of AMPK/ULK1 signaling pathway in a septic mouse model.

Sepsis, as a systemic inflammatory response syndrome (SIRS) subtype, is generally characterized by infection. Emerging evidence has highlighted dysregulated microRNAs (miRNAs) are involved in the progression of sepsis. The aim of the study was to investigate the effects of miR-335-5p on inflammatory responses in a septic mouse model.

Guanidinium ionic liquid-controlled synthesis of zeolitic imidazolate framework for improving its adsorption property.

The massive release of rhodamine B (RhB) to water system is an emerging problem, which dramatically threaten environment and human health. The development of an adsorbent with enhanced removal efficiency for RhB is urgently needed. Herein, we report an environment-friendly synthesis of high quality zeolitic imidazolate framework-8 (ZIF-8) and functional ionic liquid@ZIF-8 in water-based system without heat treatment for improving its adsorption property.

Detection of Clavibacter michiganensis subsp. michiganensis in viable but nonculturable state from tomato seed using improved qPCR.

Clavibacter michiganensis subsp. michiganensis (Cmm) is a seed-borne pathogen that causes bacterial canker disease of tomato. Cmm is typically detected in tomato seeds using quantitative real-time polymerase chain reaction (qPCR) combined with culture-based isolation.

Nonlinear equalization based on pruned artificial neural networks for 112-Gb/s SSB-PAM4 transmission over 80-km SSMF.

A pruning method of artificial neural network based nonlinear equalizer (ANN-NLE) is proposed and validated for single-sideband 4-ary pulse amplitude modulation (SSB-PAM4) in IM/DD system. As a classifier, ANN is capable to form a complex nonlinear boundary among different classifications, which is considered as an appropriate way to mitigate the nonlinear impairments in optical communication system. In this paper, first, we introduce the operation principle of the traditional linear equalizer (LE) and NLE such as volterra equalizer (VE).

HNMDA: heterogeneous network-based miRNA-disease association prediction.

Recently, accumulating evidences have shown that microRNAs (miRNAs) could play key roles in the development and progression of multiple important human diseases. Nonetheless, due to the shortcoming of being expensive and time-consuming existing in experimental approaches, computational methods are needed for the prediction of potential miRNA-disease associations. In our study, we proposed a computational model named Heterogeneous Network-based MiRNA-Disease Association prediction (HNMDA) for the latent miRNA-disease association prediction by integrating known miRNA-disease associations, miRNA functional similarity, disease semantic similarity and Gaussian interaction profile kernel similarity.

Cryomilled zinc sulfide: A prophylactic for Staphylococcus aureus-infected wounds.

Bacterial pathogens that colonize wounds form biofilms, which protect the bacteria from the effect of host immune response and antibiotics. This study examined the effectiveness of newly synthesized zinc sulfide in inhibiting biofilm development by Staphylococcus aureus ( S. aureus) strains.