Publications by authors named "Chuan-bo Chen"

Hematopoietic pre-B cell leukemia transcription factor (PBX)-interacting protein (HPIP), a co-repressor for the transcription factor PBX, is a nucleo-cytoplasmic shuttling protein. Increasing evidence suggests that HPIP is an oncogene which is frequently overexpressed in many human carcinomas. However, the role of HPIP in thyroid carcinoma is still unclear.

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Objective: To explore the effects of aptamer-siRNA nucleic acid compound on growth and apoptosis in myeloid leukemia cell line K562.

Methods: 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.

Results: The remarkably changes of morphology and structure of K562 cells treated with 200 µmol/L aptamer-siRNA were observed under fluorescence microscopy and electromicroscopy.

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Article Synopsis
  • - The study aimed to explore the connection between salt intake, the ability to taste salt, and the risk of developing gastric cancer, using a sample of 300 gastric cancer patients compared to 600 healthy individuals.
  • - Findings revealed that consuming alcohol and tobacco significantly increased gastric cancer risk, while regular consumption of fruits and vegetables appeared to offer protective benefits.
  • - A high sensitivity to salty taste (STST) was linked to a greater risk of gastric cancer, suggesting that this sensitivity could be used as a simple clinical tool to assess salt intake and its potential health implications.
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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.

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