The influences of hepatitis B virus precore and basic core promoter region mutations on the immune responses of specific cytotoxic T lymphocytes / 中华传染病杂志

Objective To investigate the influences of mutation at precore and basic core promoter(BCP) region in hepatitis B virus(HBV) on the immune response of specific cytotoxic T lymphocytes(CTL) in patients with chronic hepatitis B(CHB).Methods The number of specific CTL in peripheral blood mononuclear(PBMC) of CHB patients were tested by cytokine flow cytome- try(CFC) and HBV core18-27 peptide.HBV precore and BCP fragments were directly sequenced. Results Twenty-one(38.9%) samples were HBV precore G1896A mutation.Twenty-six(48.1%) samples were BCP region 1762/1764 combined mutation.Thirteen(24.1%) stains were three sites mutated simultaneously.Stimulated with HBV core 18-27 in vitro,the specific CTL level was signifi- cantly higher in the patients with G1896A mutation and BCP region mutation [(0.41?0.09)%, (0.36?0.08)%,(0.48?0.08)%,respectively]than those without mutation[(0.11?0.06)%, P

Segmenting lung fields in serial chest radiographs using both population and patient-specific shape statistics / 中国医疗器械杂志

This paper presents a new deformable model using both population-based and patient-specific shape statistics to segment lung fields from serial chest radiographs. First, a modified scale-invariant feature transform (SIFT) local descriptor is used to characterize the image features in the vicinity of each pixel, so that the deformable model deforms in a way that seeks for the region with similar SIFT local descriptors; second, the deformable model is constrained by both population-based and patient-specific shape statistics. At first, population-based shape statistics plays an leading role when the number of serial images is small, and gradually, patient-specific shape statistics plays a more and more important role after a sufficient number of segmentation results on the same patient have been obtained. The proposed deformable model can adapt to the shape variability of different patients, and obtain more robust and accurate segmentation results.

Non-linear registration of MR brain images integrated with machine learning / 中国医疗器械杂志

This paper presents a machine learning method to select best geometric features for deformable brain registration for each brain location. By incorporating those learned best attribute vector into the framework of HAMMER registration algorithm, The accuracy has increased by about 10% in estimating the simulated deformation fields. At the same time, on real MR brain images, we have found a great deal of improvement of registration in cortical regions.