Nielsenlassen6976

Hepatocellular carcinoma (HCC) is a common fatal malignant tumor worldwide. STAT4 is HCC susceptibility gene identified by genome-wide association study. The purpose of this study was to determine the association between four candidate single nucleotide polymorphisms (SNPs) in STAT4 genes and HCC risk in Chinese Han population.

A case-control study was conducted to assess the association between STAT4 SNPs and HCC risk in 1011 Chinese Han population. Agena MassARRAY was used to genotype SNPs. The association between SNPs and HCC susceptibility under different genetic models was evaluated by logistic regression analysis. Multifactorial dimension reduction (MDR) analyzed the interaction of 'SNP-SNP' in HCC risk. The difference of clinical characteristics between different genotypes was completed by ANOVA.

The results showed that STAT4 rs11889341 was significantly associated with HCC risk under multiple genetic models (homozygote OR = 0.60, p = 0.033; recessive OR = 0.63, p = 0.028; log-additive OR = 0.83, p = 0.032). The results of subgroup analysis showed that STAT4 rs11889341 is significantly associated with HCC risk with participants who were > 55 years, male or smoking. Both STAT4 rs7574865 and rs10174238 were significantly associated with HCC risk among participants who were > 55 years old, smoking or drinking. STAT4 haplotype (Trs11889341Trs7574865) could reduce the risk of HCC. In addition, rs11889341 and rs7574865 were significantly associated with the level of serum ferritin.

STAT4 rs11889341, rs7574865 or rs10174238 is potentially associated with HCC risk in Chinese Han population. In particular, rs11889341 showed outstanding association with HCC risk.

STAT4 rs11889341, rs7574865 or rs10174238 is potentially associated with HCC risk in Chinese Han population. In particular, rs11889341 showed outstanding association with HCC risk.

Pulmonary exacerbations (PEx) in people with cystic fibrosis (PwCF) are associated with significant morbidity. While standard PEx treatment for PwCF with Pseudomonas aeruginosa infection includes two IV antipseudomonal antibiotics, little evidence exists to recommend this approach. This study aimed to compare clinical outcomes of single versus double antipseudomonal antibiotic use for PEx treatment.

Retrospective cohort study using the linked CF Foundation Patient Registry-Pediatric Health Information System dataset. PwCF were included if hospitalized between 2007-2018 and 6-21 years of age. Regression modeling accounting for repeated measures was used to compare lung function outcomes between single versus double IV antipseudomonal antibiotic regimens using propensity-score weighting to adjust for relevant confounding factors.

Among 10,660 PwCF in the dataset, we analyzed 2,578 PEx from 1,080 PwCF, of which 455 and 2,123 PEx were treated with 1 versus 2 IV antipseudomonal antibiotics, respectively. We identified no significant differences between PEx treated with 1 versus 2 IV antipseudomonal antibiotics either in change between pre- and post-PEx percent predicted forced expiratory volume in one second (ppFEV1) (-0.84%, [95% CI -2.25, 0.56]; p=0.24), odds of returning to ≥90% of baseline ppFEV1 within 3 months following PEx (Odds Ratio 0.83, [95% CI 0.61, 1.13]; p=0.24) or time to next PEx requiring IV antibiotics (Hazard Ratio 1.04, [95% CI 0.87, 1.24]; p=0.69).

Use of 2 IV antipseudomonal antibiotics for PEx treatment in young PwCF was not associated with greater improvements in measured respiratory and clinical outcomes compared to treatment with 1 IV antipseudomonal antibiotic.

Use of 2 IV antipseudomonal antibiotics for PEx treatment in young PwCF was not associated with greater improvements in measured respiratory and clinical outcomes compared to treatment with 1 IV antipseudomonal antibiotic.

Genomic sequences are widely used to infer the evolutionary history of a given group of individuals. Many methods have been developed for sequence clustering and tree building. In the early days of genome sequencing, these were often limited to hundreds of sequences, but due to the surge of high throughput sequencing, it is now common to have millions of sampled sequences at hand. We introduce MNHN-Tree-Tools, a high performance set of algorithms that builds multi-scale, nested clusters of sequences found in a FASTA file. MNHN-Tree-Tools does not rely on sequence alignment and can thus be used on large datasets to infer a sequence tree. DNA Damage inhibitor Herein we outline two applications A human alpha-satellite repeats classification and a tree of life derivation from 16S/18S rDNA sequences.

Open source with a Zlib License via the Git protocol https//gitlab.in2p3.fr/mnhn-tools/mnhn-tree-tools.

An in depth discussion about the algorithm with numerical simulations https//gitlab.in2p3.fr/mnhn-tools/tree-tools-algorithms-document/-/raw/master/article.pdf.

A detailed users guide and tutorial https//gitlab.in2p3.fr/mnhn-tools/mnhn-tree-tools-manual/-/raw/master/manual.pdf.

http//treetools.haschka.net.

http//treetools.haschka.net.We present the complete genome sequences of 3 Erwinia rhapontici strains, MAFF 311153, 311154, and 311155. These chromosome sequences contained variety types of luxI/luxR gene pair involved in acylhomoserine lactone biosynthesis and reception. Large-scale insertion sequence was observed in the indigenous plasmid of MAFF 311154 and contained eraI3/eraR3 gene pair that make possible to produce acylhomoserine lactone.Metabolic engineering technologies have been employed with increasing success over the last three decades for the engineering and optimization of industrial host strains to competitively produce high-value chemical targets. To this end, continued reductions in the time taken from concept, to development, to scale-up are essential. Design-Build-Test-Learn pipelines that are able to rapidly deliver diverse chemical targets through iterative optimization of microbial production strains have been established. Biofoundries are employing in silico tools for the design of genetic parts, alongside combinatorial design of experiments approaches to optimize selection from within the potential design space of biological circuits based on multi-criteria objectives. These genetic constructs can then be built and tested through automated laboratory workflows, with performance data analysed in the learn phase to inform further design. Successful examples of rapid prototyping processes for microbially produced compounds reveal the potential role of biofoundries in leading the sustainable production of next-generation bio-based chemicals.