Characterizing microbiota and metabolomics analysis to identify candidate biomarkers in lung cancer.

Background: Lung cancer is the leading malignant disease and cause of cancer-related death worldwide. Most patients with lung cancer had insignificant early symptoms so that most of them were diagnosed at an advanced stage. In addition to factors such as smoking, pollution, lung microbiome and its metabolites play vital roles in the development of lung cancer.

In-silico analysis of ligand-receptor binding patterns of α-MMC, TCS and MAP30 protein to LRP1 receptor.

Alpha-momorcharin (α-MMC), trichosanthin (TCS), and momordica anti-HIV protein of 30 kD (MAP30) are potential anti-tumor drug candidates but have cytotoxicity to normal cells. The binding of these proteins to LRP1 receptor and the subsequent endocytosis are essential to their cytotoxicity, but this binding process remains largely unknown. This study, in-silico analysis of the binding patterns, was conducted via the protein-protein docking software, ZDOCK 3.

Study the effect of trypsin enzyme activity on the screening of applying frontal affinity chromatography.

Frontal affinity chromatography (FAC) combined with enzyme has been widely used for drug screening. In this paper, the effect of target enzyme activity on screening of bioactive compounds was studied through applying FAC. Trypsin with different degree of inactivation were prepared as target enzyme by thermal denaturation.

Comparison and analysis of the efficacy and safety of minimally invasive surgery and craniotomy in the treatment of hypertensive intracerebral hemorrhage.

Objective: This study was aimed to compare and analyze the effects and safety of minimally invasive and craniotomy in the treatment of hypertensive intracerebral hemorrhage.

Methods: A total of 130 patients with hypertensive intracerebral hemorrhage were recruited. The patients were randomly divided into two groups (research and control group).

Bioinformatics analysis of aggressive behavior of breast cancer via an integrated gene regulatory network.

Background: Breast cancer is one of the most frequently diagnosed cancers in women. Though death from this disease is mainly caused by the metastases of the aggressive cancer cells, few studies have expounded the aggressive behavior of breast cancer.

Materials And Methods: We downloaded the gene expression profiles of GSE40057, including four aggressive and six less-aggressive breast cancer cell lines, from Gene Expression Omnibus and identified the differentially expressed genes (DEGs) between the aggressive and less-aggressive samples.

Dose-volume histogram parameters for predicting radiation pneumonitis using receiver operating characteristic curve.

Objective: To assess the predictability of dose-volume histogram (DVH) parameters for radiation pneumonitis (RP) using receiver operating characteristic (ROC) curve.

Methods: One hundred and thirty-five cases of locally advanced non-small cell lung cancer patients treated with three-dimensional radiotherapy and chemotherapy were analyzed retrospectively. The end point of follow-up was ≥2 grade RP defined according to the National Cancer Institute Common Terminology Criteria for Adverse Events, version 3.

High-molecular-weight polyethyleneimine conjuncted pluronic for gene transfer agents.

In order to enhance the gene delivery efficiency and decrease cytotoxicity of polyplexes, copolymers consisting of branched polyethyleneimine (PEI) 25 kDa grafted with Pluronic (F127, F68, P105) were successfully synthesized using a simple two-step procedure. The copolymers were tested for cytotoxicity and DNA condensation and complexation properties. Their polyplexes with plasmid DNA were characterized in terms of DNA size and surface charge and transfection efficiency.

Primary massage using one-finger twining manipulation for treatment of infantile muscular torticollis.

Objectives: The study objectives were to observe the clinical efficacy of primary massage of twining manipulation with one finger (PMTMOF) versus conventional tuina manipulation for treating muscular torticollis.

Methods: A randomized, controlled, single-blind method was employed. Patients received either PMTMOF (experimental group, n = 265) or conventional tuina manipulation (control group, n = 235), once a day, 20 minutes each, for 15 days of treatment.

Coclustering for cross-subject fiber tract analysis through diffusion tensor imaging.

One of the fundamental goals of computational neuroscience is the study of anatomical features that reflect the functional organization of the brain. The study of physical associations between neuronal structures and the examination of brain activity in vivo have given rise to the concept of anatomical and functional connectivity, which has been invaluable for our understanding of brain mechanisms and their plasticity during development. However, at present, there is no robust and accurate computational framework for the quantitative assessment of cortical connectivity patterns.

Simulation of genomic recombination and the influence of DNA sequence diversity on cluster formation and detectability of recombination: a scientific workflow method.

The detection of recombination from DNA sequences is relevant to the understanding of evolutionary and molecular genetics. We developed a Recombination Simulation Scientific Workflow System (RSSWS) for simulating recombination and using GENECONV to test the effect of pairwise differences in a diverse population on the detectability of recombination. Decreases in recombination rate owing to pairwise differences resulted in population clusters analogous to sympatric speciation under specific conditions and decreases in detectability of recombination, a phenomenon that we call 'cryptic recombination'.

Cocluster analysis of thalamo-cortical fibre tracts extracted from diffusion tensor MRI.

As the central relay station of the human brain, the thalamus modulates sensory signals to and from the cerebral cortex. The reciprocal connectivity between the cerebral cortex and the thalamus is believed to play an essential role in consciousness and various neurological disorders. Thus, in-vivo analysis of thalamo-cortical connectivity is important for our understanding of normal and pathological brain processes.

RECOMBFLOW: a scientific workflow environment for Intragenomic Gene Conversion analysis in bacterial genomes, including the pathogen Streptococcus pyogenes.

Intragenomic Gene Conversion (IGC) is important in the evolution of bacteria but has only been analysed computationally in a few strains of Escherichia coli. This paper describes a scientific workflow system, called RECOMBFLOW, that automates this complex procedure for the analysis of more than 400 bacterial genomes, with a median analysis time per genome of less than 5 minutes. Results show that IGC varies greatly, both between different species and among multiple genomes of the same species.

Computational identification of transcription frameworks of early committed spermatogenic cells.

It is known that transcription factors (TFs) work in cooperation with each other to govern gene expression and thus single TF studies may not always reflect the underlying biology. Using microarray data obtained from two independent studies of the first wave of spermatogenesis, we tested the hypothesis that co-expressed spermatogenic genes in cells committed to differentiation are regulated by a set of distinct combinations of TF modules. A computational approach was designed to identify over-represented module combinations in the promoter regions of genes associated with transcripts that either increase or decrease in abundance between the first two major spermatogenic cell types: spermatogonia and spermatocytes.

Finding females: pheromone-guided reproductive tracking behavior by male Nereis succinea in the marine environment.

Pheromones trigger reproductive responses of many marine organisms, but little is known about how pheromones mediate mate-finding behavior in the marine environment. This paper investigates whether the tetrapeptide nereithione (cysteine-glutathione disulfide), known to be released by females of the polychaete Nereis succinea to trigger spawning in male N. succinea, can also be used at lower concentrations to guide males to the females.

Multimodality data integration in epilepsy.

An important goal of software development in the medical field is the design of methods which are able to integrate information obtained from various imaging and nonimaging modalities into a cohesive framework in order to understand the results of qualitatively different measurements in a larger context. Moreover, it is essential to assess the various features of the data quantitatively so that relationships in anatomical and functional domains between complementing modalities can be expressed mathematically. This paper presents a clinically feasible software environment for the quantitative assessment of the relationship among biochemical functions as assessed by PET imaging and electrophysiological parameters derived from intracranial EEG.

FM-test: a fuzzy-set-theory-based approach to differential gene expression data analysis.

Background: Microarray techniques have revolutionized genomic research by making it possible to monitor the expression of thousands of genes in parallel. As the amount of microarray data being produced is increasing at an exponential rate, there is a great demand for efficient and effective expression data analysis tools. Comparison of gene expression profiles of patients against those of normal counterpart people will enhance our understanding of a disease and identify leads for therapeutic intervention.

Compressed pattern matching in DNA sequences.

We propose derivative Boyer-Moore (d-BM), a new compressed pattern matching algorithm in DNA sequences. This algorithm is based on the Boyer-Moore method, which is one of the most popular string matching algorithms. In this approach, we compress both DNA sequences and patterns by using two bits to represent each A, T, C, G character.

Incremental genetic K-means algorithm and its application in gene expression data analysis.

Background: In recent years, clustering algorithms have been effectively applied in molecular biology for gene expression data analysis. With the help of clustering algorithms such as K-means, hierarchical clustering, SOM, etc, genes are partitioned into groups based on the similarity between their expression profiles. In this way, functionally related genes are identified.