Inhibition of the TLR4/NF-κB pathway promotes the polarization of LPS-induced BV2 microglia toward the M2 phenotype.

This study aimed to investigate whether the inhibition of the TLR4/NF-κB pathway can promote lipopolysaccharide (LPS)-induced microglial polarization from the M1 to M2 phenotype, and thus exert neuroprotection. LPS-induced microglia were used as a model for inflammation in vitro. TLR4-specific inhibitor resatorvid (TAK-242) and NF-κB inhibitor pyrrolidine dithiocarbamate (PDTC) were used to verify the effect of the TLR4/NF-κB pathway on microglia activation and polarization. Cell proliferation was measured by cell counting, and nitric oxide (NO) and reactive oxygen species (ROS) release was measured using the Griess reagent and ROS kit, respectively. Immunofluorescence and RT-qPCR analyses were used to detect the expression of microglial activation markers, phenotypic markers, related pathway molecules, and inflammatory factors. TLR4 specific inhibitor TAK-242 and NF-κB inhibitor PDTC alleviated LPS-induced microglia over-activation by inhibiting the TLR4/NF-κB pathway, and reduced LPS-stimulated cell proliferation and the release of NO, ROS, TNF-a, and IL-6 and IL-1ß. Meanwhile, TAK-242 and PDTC promoted LPS-induced polarization of microglia from M1 to M2 phenotype, decreased the expression of microglial activation marker Iba1 and M1 phenotypic markers (TNF-a and CD86), and increased the expression of M2 phenotypic markers (Arg-1 and CD206). The mechanism may be related to inhibiting the TLR4/NF-κB pathway. The inhibition of the TLR4/NF-κB pathway can promote LPS-induced polarization of BV2 microglia from M1 phenotype to M2 phenotype.

Shenfu Injection Protects Brain Injury in Rats with Cardiac Arrest through Nogo/NgR Pathway.

The effect of Shenfu injection on brain injury after cardiac arrest (CA) and cardiopulmonary resuscitation (CPR) along with the underlying mechanism of axonal regeneration was explored. CA/CPR model in rats was established for subsequent experiments. A total of 160 rats were randomly divided into sham group, model group, conventional western medicine (CWM) group, Shenfu group, and antagonist group (n = 32 per group). After 3 hours, 24 hours, 3 days, and 7 days of drug administration, the modified Neurological Severity Score tests were performed. The ultrastructure of the brain and hippocampus was observed by electron microscopy. Real-time quantitative polymerase chain reaction (PCR), western blotting, and immunohistochemistry were used to detect Nogo receptor (NgR) expression in the hippocampus and cerebral cortex, and Nogo-NgR expression in CA/CPR model. Neurological deficits in the model group were severe at 3 hours, 24 hours, 3 days, and 7 days after the recovery of natural circulation, whereas the neurological deficits in CWM, antagonist, and Shenfu group were relatively mild. The ultrastructure of neuronal cells in Shenfu group had relatively complete cell membranes and more vesicles than those in the model group. The results of PCR and western blotting showed lower messenger ribonucleic acid and protein expression of NgR in Shenfu group than the model group and CWM group. Immunohistochemical examination indicated a reduction of Nogo-NgR expression in Shenfu group and antagonist group. Our results suggested that Shenfu injection reduced brain injury by attenuating Nogo-NgR signaling pathway and promoting axonal regeneration.

Classifier assessment and feature selection for recognizing short coding sequences of human genes.

With the ever-increasing pace of genome sequencing, there is a great need for fast and accurate computational tools to automatically identify genes in these genomes. Although great progress has been made in the development of gene-finding algorithms during the past decades, there is still room for further improvement. In particular, the issue of recognizing short exons in eukaryotes is still not solved satisfactorily. This article is devoted to assessing various linear and kernel-based classification algorithms and selecting the best combination of Z-curve features for further improvement of the issue. Eight state-of-the-art linear and kernel-based supervised pattern recognition techniques were used to identify the short (21-192 bp) coding sequences of human genes. By measuring the prediction accuracy, the tradeoff between sensitivity and specificity and the time consumption, partial least squares (PLS) and kernel partial least squares (KPLS) algorithms were verified to be the most optimal linear and kernel-based classifiers, respectively. A surprising result was that, by making good use of the interpretability of the PLS and the Z-curve methods, 93 Z-curve features were proved to be the best selective combination. Using them, the average recognition accuracy was improved as high as 7.7% by means of KPLS when compared with what was obtained by the Fisher discriminant analysis using 189 Z-curve variables (Gao and Zhang, 2004 ). The used codes are freely available from the following approaches (implemented in MATLAB and supported on Linux and MS Windows): (1) SVM: http://www.support-vector-machines.org/SVM_soft.html. (2) GP: http://www.gaussianprocess.org. (3) KPLS and KFDA: Taylor, J.S., and Cristianini, N. 2004. Kernel Methods for Pattern Analysis. Cambridge University Press, Cambridge, UK. (4) PLS: Wise, B.M., and Gallagher, N.B. 2011. PLS-Toolbox for use with MATLAB: ver 1.5.2. Eigenvector Technologies, Manson, WA. Supplementary Material for this article is available at www.liebertonline.com/cmb.