Search

Whooping cough (pertussis) is a contagious respiratory illness caused by Bordetella pertussis. The introduction of the whole-cell vaccine (WCV) successfully decreased the incidence of pertussis. Although vaccination has been successful, [...]

ObjectivesLevofloxacin is widely used because of its broad-spectrum antimicrobial activity and convenient dosing schedule. However, the relevance of its use in children remains to be investigated. The purpose of this study is to investigate the efficacy and safety of levofloxacin use in children with severe infections.MethodsWe conducted a retrospective observational study of patients

As one of the most induced genes in activated macrophages, immune-responsive gene 1 (IRG1) encodes a mitochondrial metabolic enzyme catalysing the production of itaconic acid (ITA). Although ITA has an anti-inflammatory property, the underlying mechanisms are not fully understood. Here we show that ITA is a potent inhibitor of the TET-family DNA dioxygenases. ITA binds to the same site on TET2 as the co-substrate α-ketoglutarate, inhibiting TET2 catalytic activity. Lipopolysaccharide treatment, which induces Irg1 expression and ITA accumulation, inhibits Tet activity in macrophages. Transcriptome analysis reveals that TET2 is a major target of ITA in suppressing lipopolysaccharide-induced genes, including those regulated by the NF-κB and STAT signalling pathways. In vivo, ITA decreases the levels of 5-hydroxymethylcytosine, reduces lipopolysaccharide-induced acute pulmonary oedema as well as lung and liver injury, and protects mice against lethal endotoxaemia, depending on the catalytic activity of Tet2. Our study thus identifies ITA as an immune modulatory metabolite that selectively inhibits TET enzymes to dampen the inflammatory responses.

Two independent experiments were performed with three groups each (sepsis control, sepsis, and sepsis with apoE23 treatment) to investigate the anti-inflammatory effect of apolipoprotein 23 (apoE23) in a mouse model of sepsis induced by S. typhimurium. Survival rates; plasma level variations in tumor necrosis factor (TNF)-α, interleukin (IL)-6, and lipopolysaccharide (LPS); S. typhimurium colony-forming units in the spleen tissue; and mRNA and protein expression levels of low-density lipoprotein receptor (LDLR), LDLR-related protein (LRP), syndecan-1, and scavenger receptor B1 were evaluated in the livers of mice from the three groups. Results found that the survival rate of septic mice treated with apoE23 was 100% within 48 h, while it was only 40% in septic mice without apoE23 treatment (P < 0.001). The plasma LPS, TNF-α, and IL-6 levels and the S. typhimurium load in mice in the apoE23-treated group were significantly lower than those in septic mice (P < 0.05). Moreover, apoE23 restored the downregulated expression of LDLR and LRP in the liver tissue of septic mice. So apoE23 exhibits an anti-inflammatory effect in the mouse model of S. typhimurium-induced sepsis. Further studies are required to understand the mechanisms underlying the anti-inflammatory effects of apoE23.

Aiming at a kind of dual-rate Hammerstein system, based on the auxiliary model identification idea, using the maximum likelihood principle and recursive identification technology, this paper proposes a maximum likelihood recursive least squares algorithm. The main method is to construct an auxiliary model for the unknown output in the model, and use the output of the auxiliary model to predict the unknown output. This method can directly identify parameters based on the dual-rate input and output data. The simulation experiments show that the proposed algorithm can effectively identify the dual-rate Hammerstein system, and the final error tends to be about 1%.

Aims: Status epilepticus (SE) is the most common neurological emergency in pediatric patients. This study aimed to screen for prognostic biomarkers of SE in the cerebrospinal fluid (CSF) using metabolomics. Methods: Ultra‐performance liquid chromatography quadrupole time‐of‐flight tandem mass spectrometry (UPLC‐QTOF‐MS) was conducted to identify prognostic biomarkers in CSF metabolomics by comparing the poor outcome group (N = 13) with the good outcome group (N = 15) of children with SE. Differentially expressed metabolites were identified using Mann–Whitney U test corrected by Benjamini‐Hochberg and partial least squares discriminant analysis (PLS‐DA). Results: The PLS‐DA model identified and validated significant metabolic differences between the poor and good outcome groups of children with SE (PLS‐DA with R2Y = 0.992 and Q2 = 0.798). A total of 49 prognosis‐related metabolites were identified. Of these metabolites, 20 including glutamyl‐glutamine, 3‐iodothyronamine, and L‐fucose had an area under the curve (AUC) ≥ 80% in prognostic prediction of SE. The logistic regression model combining glutamyl‐glutamine and 3‐iodothyronamine produced an AUC value of 0.976, with a sensitivity of 0.863 and specificity of 0.956. Pathway analysis revealed that dysregulation of the citrate cycle (TCA) and arginine biosynthesis may contribute to poor SE prognosis. Conclusions: This study highlighted the prognosis‐related metabolomic disturbances in the CSF of children with SE and identified potential prognostic biomarkers. A prognostic prediction model combining glutamyl‐glutamine and 3‐iodothyronamine with high predictive value was established. [ABSTRACT FROM AUTHOR]