Effects of Aptamer-siRNA Nucleic Acid Compound on Growth and Apoptosis in Myeloid Leukemia Cell Line K562 / 中国实验血液学杂志

<p><b>OBJECTIVE</b>To explore the effects of aptamer-siRNA nucleic acid compound on growth and apoptosis in myeloid leukemia cell line K562.</p><p><b>METHODS</b>the changes of cellular morphology and structure were observed by using fluorescence microscope, laser confocal microscope, JEM-4000EX transmission electron microscopy; MTT assay were performed to evaluate the sensibility of K562 cells to aptamer-siRNA compound, the apoptosis was detected by DNA gel electro-phoresis.</p><p><b>RESULTS</b>The remarkably changes of morphology and structure of K562 cells treated with 200 µmol/L aptamer-siRNA were observed under fluorescence microscopy and electromicroscopy. As compared with control, the aptamer-siRNA compound showed more inhibitory effect on K562 cells and there was significant difference (P<0.05). The MTT assay showed that the IC50 value of aptamer-siRNA compound for K562 cells was 150 µmol/L. According to agarose gel electrophoresis observation, when the aptamer-siRNA compound showed effect on K562 cells, the typical DNA lader could be observed.</p><p><b>CONCLUSION</b>The aptamer-siRNA compound can significantly induce K562 cell apoptosis, and provide reference for gene therapy of patients with chronic myelocytic lenkemia.</p>

A hybrid neural network system for prediction and recognition of promoter regions in human genome / 浙江大学学报（英文版）（B辑：生物医学和生物技术）

This paper proposes a high specificity and sensitivity algorithm called PromPredictor for recognizing promoter regions in the human genome. PromPredictor extracts compositional features and CpG islands information from genomic sequence, feeding these features as input for a hybrid neural network system (HNN) and then applies the HNN for prediction. It combines a novel promoter recognition model, coding theory, feature selection and dimensionality reduction with machine learning algorithm. Evaluation on Human chromosome 22 was approximately 66% in sensitivity and approximately 48% in specificity. Comparison with two other systems revealed that our method had superior sensitivity and specificity in predicting promoter regions. PromPredictor is written in MATLAB and requires Matlab to run. PromPredictor is freely available at http://www.whtelecom.com/Prompredictor.htm.