No abstract

Background: Inflammation and apoptosis are considered to be two main factors affecting ischemic brain injury and the subsequent reperfusion damage. MiR-19a-3p has been reported to be a possible novel biomarker in ischemic stroke. However, the function and molecular mechanisms of miR-19a-3p remain unclear in cerebral ischemia/reperfusion (I/R) injury. Methods: The I/R injury model was established in vivo by middle cerebral artery occlusion/reperfusion (MCAO/R) in rats and in vitro by oxygen-glucose deprivation and reperfusion (OGD/R) induced SH-SY5Y cells. The expression of miR-19a-3p was determined by reverse transcription quantitative PCR. The infarction volumes, Neurological deficit scores, apoptosis, cell viability, pro-inflammatory cytokines and apoptosis were evaluated using Longa score, Bederson score, TTC, TUNEL staining, CCK-8, ELISA, flow cytometry assays. Luciferase reporter assay was utilized to validate the target gene of miR-19a-3p. Results: We first found miR-19a-3p was significantly up-regulated in rat I/R brain tissues and OGD/R induced SH-SY5Y cells. Using the in vivo and in vitro I/R injury model, we further demonstrated that miR-19a-3p inhibitor exerted protective role against injury to cerebral I/R, which was reflected by reduced infarct volume, improved neurological outcomes, increased cell viability, inhibited inflammation and apoptosis. Mechanistically, miR-19a-3p binds to 3′UTR region of IGFBP3 mRNA. Inhibition of miR-19a-3p caused the increased expression of IGFBP3 in OGD/R induced SH-SY5Y cells. Furthermore, we showed that IGFBP3 overexpression imitated, while knockdown reversed the protective effects of miR-19a-3p inhibitor against OGD/R-induced injury. Conclusions: In summary, our findings showed miR-19a-3p regulated I/R-induced inflammation and apoptosis through targeting IGFBP3, which might provide a potential therapeutic target for cerebral I/R injury.

Objective: To investigate the mechanisms of action of the tumoricidal effects of temozolomide against the human glioma cell line U251 in vitro, and to provide preclinical proof-of-concept studies of the effects of temozolomide-containing regimens. Methods: U251 cells were exposed to 100 mmol/l temozolomide. Morphological alterations were monitored by light microscopy. Cell viability was measured using the 3 -(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. Cell cycle analysis and the rate of apoptosis were determined using flow cytometry and the number of acidic vesicular organelles stained with acridine orange were analysed by fluorescence microscopy. The scratch recovery test was used to measure cell migration. Results: U251 cells that were treated with temozolomide displayed morphological alterations indicative of a rounder shape and impaired cellular adhesion to the cell culture plate compared with control U251 cells. Temozolomide reduced cell viability as measured by the MTT assay, caused cell cycle arrest in the gap 2/mitosis phase, inhibited cell migration and promoted autophagy in U251 cells. Conclusion: Temozolomide induced autophagic, but not apoptotic processes, in U251 cells and thus reduced their viability and migration.

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Biologically-inspired models and algorithms are considered as promising sensor array signal processing methods for electronic noses. Feature selection is one of the most important issues for developing robust pattern recognition models in machine learning. This paper describes an investigation into the classification performance of a bionic olfactory model with the increase of the dimensions of input feature vector (outer factor) as well as its parallel channels (inner factor). The principal component analysis technique was applied for feature selection and dimension reduction. Two data sets of three classes of wine derived from different cultivars and five classes of green tea derived from five different provinces of China were used for experiments. In the former case the results showed that the average correct classification rate increased as more principal components were put in to feature vector. In the latter case the results showed that sufficient parallel channels should be reserved in the model to avoid pattern space crowding. We concluded that 6∼8 channels of the model with principal component feature vector values of at least 90% cumulative variance is adequate for a classification task of 3∼5 pattern classes considering the trade-off between time consumption and classification rate.