184 research outputs found

    Comparing journal and paper level classifications of science

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    The classification of science into disciplines is at the heart of bibliometric analyses. While most classifications systems are implemented at the journal level, their accuracy has been questioned, and paper-level classifications have been considered by many to be more precise. However, few studies investigated the difference between journal and the paper classification systems. This study addresses this gap by comparing the journal- and paper-level classifications for the same set of papers and journals. This isolates the effects of classification precision (i.e., journal- or paper-level) to reveal the extent of paper misclassification. Results show almost half of papers could be misclassified in journal classification systems. Given their importance in the construction and analysis of bibliometric indicators, more attention should be given to the robustness and accuracy of these disciplinary classifications schemes

    Oxidative stress: The nexus of obesity and cognitive dysfunction in diabetes

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    Obesity has been associated with oxidative stress. Obese patients are at increased risk for diabetic cognitive dysfunction, indicating a pathological link between obesity, oxidative stress, and diabetic cognitive dysfunction. Obesity can induce the biological process of oxidative stress by disrupting the adipose microenvironment (adipocytes, macrophages), mediating low-grade chronic inflammation, and mitochondrial dysfunction (mitochondrial division, fusion). Furthermore, oxidative stress can be implicated in insulin resistance, inflammation in neural tissues, and lipid metabolism disorders, affecting cognitive dysfunction in diabetics

    The use of comparative genomic hybridization to characterize genome dynamics and diversity among the serotypes of Shigella

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    <p>Abstract</p> <p>Background</p> <p>Compelling evidence indicates that <it>Shigella </it>species, the etiologic agents of bacillary dysentery, as well as <it>enteroinvasive Escherichia coli</it>, are derived from multiple origins of <it>Escherichia coli </it>and form a single pathovar. To further understand the genome diversity and virulence evolution of <it>Shigella</it>, comparative genomic hybridization microarray analysis was employed to compare the gene content of <it>E. coli </it>K-12 with those of 43 <it>Shigella </it>strains from all lineages.</p> <p>Results</p> <p>For the 43 strains subjected to CGH microarray analyses, the common backbone of the <it>Shigella </it>genome was estimated to contain more than 1,900 open reading frames (ORFs), with a mean number of 726 undetectable ORFs. The mosaic distribution of absent regions indicated that insertions and/or deletions have led to the highly diversified genomes of pathogenic strains.</p> <p>Conclusion</p> <p>These results support the hypothesis that by gain and loss of functions, <it>Shigella </it>species became successful human pathogens through convergent evolution from diverse genomic backgrounds. Moreover, we also found many specific differences between different lineages, providing a window into understanding bacterial speciation and taxonomic relationships.</p

    Complete genome sequence of Shigella flexneri 5b and comparison with Shigella flexneri 2a

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    BACKGROUND: Shigella bacteria cause dysentery, which remains a significant threat to public health. Shigella flexneri is the most common species in both developing and developed countries. Five Shigella genomes have been sequenced, revealing dynamic and diverse features. To investigate the intra-species diversity of S. flexneri genomes further, we have sequenced the complete genome of S. flexneri 5b strain 8401 (abbreviated Sf8401) and compared it with S. flexneri 2a (Sf301). RESULTS: The Sf8401 chromosome is 4.5-Mb in size, a little smaller than that of Sf301, mainly because the former lacks the SHI-1 pathogenicity island (PAI). Compared with Sf301, there are 6 inversions and one translocation in Sf8401, which are probably mediated by insertion sequences (IS). There are clear differences in the known PAIs between these two genomes. The bacteriophage SfV segment remaining in SHI-O of Sf8401 is clearly larger than the remnants of bacteriophage SfII in Sf301. SHI-1 is absent from Sf8401 but a specific related protein is found next to the pheV locus. SHI-2 is involved in one intra-replichore inversion near the origin of replication, which may change the expression of iut/iuc genes. Moreover, genes related to the glycine-betaine biosynthesis pathway are present only in Sf8401 among the known Shigella genomes. CONCLUSION: Our data show that the two S. flexneri genomes are very similar, which suggests a high level of structural and functional conservation between the two serotypes. The differences reflect different selection pressures during evolution. The ancestor of S. flexneri probably acquired SHI-1 and SHI-2 before SHI-O was integrated and the serotypes diverged. SHI-1 was subsequently deleted from the S. flexneri 5b genome by recombination, but stabilized in the S. flexneri 2a genome. These events may have contributed to the differences in pathogenicity and epidemicity between the two serotypes of S. flexneri

    Low Mannose Binding Lectin, but Not L-Ficolin, Is Associated With Spontaneous Clearance of Hepatitis C Virus After Infection

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    Some individuals can spontaneously clear the hepatitis C virus (HCV) after infection, whereas others develop a chronic infection. The exact mechanism of this phenomenon is unknown. We aimed to evaluate the association of plasma levels of MBL, L-ficolin, and cytokines with outcome of HCV infections in two groups of patients who cleared HCV spontaneously (CHS), and who developed chronic HCV infections (CHC). Altogether, 86 patients and 183 healthy controls were included. Of 86 patients, 36 had CHS and 50 had CHC. Concentrations of plasma MBL and L-ficolin were measured in patients and controls. Twenty plasma cytokines and adhesion molecules, including GM-CSF, ICAM-1, IFN-gamma, IFN-alpha, IL-1 alpha, IL-1 beta, IL-10, IL-12p70, IL-13, IL-17A, IL-4, IL-8, IP-10, MCP-1, IL-6, MIP-1 alpha, MIP-1 beta, sE-Selectin, sP-Selectin, and TNF-alpha, were determined in all patients and randomly selected 45 controls. The level of MBL was significantly lower in subjects with CHS than in healthy controls (median: 293.10 vs. 482.64 ng/ml, p = 0.008), whereas the level of MBL was significantly higher in patients with CHC than in controls (median: 681.32 vs. 482.64 ng/ml, p = 0.001). No such differences in plasma L-ficolin were observed. Plasma levels of all cytokines and adhesion molecules, except ICAM-1, were significantly higher in patients than in controls. Moreover, patients with CHC had significantly higher levels of IFN-gamma, IFN-alpha, IL-1 alpha, IL-10, IL-13, IL-4, IL-6, and TNF-alpha than those with CHS. These findings implicate that lower levels of plasma MBL, together with lower levels of above mentioned cytokines may play a part in virus clearance of HCV infection

    Fast Green FCF Attenuates Lipopolysaccharide-Induced Depressive-Like Behavior and Downregulates TLR4/Myd88/NF-κB Signal Pathway in the Mouse Hippocampus

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    Depression is a common neuropsychiatric disorder and new anti-depressive treatments are still in urgent demand. Fast Green FCF, a safe biocompatible color additive, has been suggested to mitigate chronic pain. However, Fast green FCF’s effect on depression is unknown. We aimed to investigate Fast green FCF’s effect on lipopolysaccharide (LPS)-induced depressive-like behavior and the underlying mechanisms. Pretreatment of Fast green FCF (100 mg/kg, i.p. daily for 7 days) alleviated depressive-like behavior in LPS-treated mice. Fast green FCF suppressed the LPS-induced microglial and astrocyte activation in the hippocampus. Fast green FCF decreased the mRNA and protein levels of Toll-like receptor 4 (TLR4) and Myeloid differentiation primary response 88 (Myd88) and suppressed the phosphorylation of nuclear factor-κB (NF-κB) in the hippocampus of LPS-treated mice. Fast green FCF also downregulated hippocampal tumor necrosis factor (TNF)-α, interleukin (IL)-1β, and IL-6, but did not alter the level of the brain-derived neurotrophic factor (BDNF) in the hippocampus of LPS-treated mice. The molecular docking simulation predicts that Fast green FCF may interact with TLR4 and interrupt the formation of the TLR4-MD2 complex. In conclusion, the anti-depressive action of Fast green FCF in LPS-treated mice may involve the suppression of neuroinflammation and the downregulation of TLR4/Myd88/NF-κB signal pathway in mouse hippocampus. Our findings indicate the potential of Fast green FCF for controlling depressive symptoms

    Genome dynamics and diversity of Shigella species, the etiologic agents of bacillary dysentery

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    The Shigella bacteria cause bacillary dysentery, which remains a significant threat to public health. The genus status and species classification appear no longer valid, as compelling evidence indicates that Shigella, as well as enteroinvasive Escherichia coli, are derived from multiple origins of E.coli and form a single pathovar. Nevertheless, Shigella dysenteriae serotype 1 causes deadly epidemics but Shigella boydii is restricted to the Indian subcontinent, while Shigella flexneri and Shigella sonnei are prevalent in developing and developed countries respectively. To begin to explain these distinctive epidemiological and pathological features at the genome level, we have carried out comparative genomics on four representative strains. Each of the Shigella genomes includes a virulence plasmid that encodes conserved primary virulence determinants. The Shigella chromosomes share most of their genes with that of E.coli K12 strain MG1655, but each has over 200 pseudogenes, 300∼700 copies of insertion sequence (IS) elements, and numerous deletions, insertions, translocations and inversions. There is extensive diversity of putative virulence genes, mostly acquired via bacteriophage-mediated lateral gene transfer. Hence, via convergent evolution involving gain and loss of functions, through bacteriophage-mediated gene acquisition, IS-mediated DNA rearrangements and formation of pseudogenes, the Shigella spp. became highly specific human pathogens with variable epidemiological and pathological features

    High Chromosome Number in hematological cancer cell lines is a Negative Predictor of Response to the inhibition of Aurora B and C by GSK1070916

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    <p>Abstract</p> <p>Background</p> <p>Aurora kinases play critical roles in mitosis and are being evaluated as therapeutic targets in cancer. GSK1070916 is a potent, selective, ATP competitive inhibitor of Aurora kinase B and C. Translation of predictive biomarkers to the clinic can benefit patients by identifying the tumors that are more likely to respond to therapies, especially novel inhibitors such as GSK1070916.</p> <p>Methods</p> <p>59 Hematological cancer-derived cell lines were used as models for response where <it>in vitro </it>sensitivity to GSK1070916 was based on both time and degree of cell death. The response data was analyzed along with karyotype, transcriptomics and somatic mutation profiles to determine predictors of response.</p> <p>Results</p> <p>20 cell lines were sensitive and 39 were resistant to treatment with GSK1070916. High chromosome number was more prevalent in resistant cell lines (p-value = 0.0098, Fisher Exact Test). Greater resistance was also found in cell lines harboring polyploid subpopulations (p-value = 0.00014, Unpaired t-test). A review of NOTCH1 mutations in T-ALL cell lines showed an association between NOTCH1 mutation status and chromosome number (p-value = 0.0066, Fisher Exact Test).</p> <p>Conclusions</p> <p>High chromosome number associated with resistance to the inhibition of Aurora B and C suggests cells with a mechanism to bypass the high ploidy checkpoint are resistant to GSK1070916. High chromosome number, a hallmark trait of many late stage hematological malignancies, varies in prevalence among hematological malignancy subtypes. The high frequency and relative ease of measurement make high chromosome number a viable negative predictive marker for GSK1070916.</p
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