Biomedical subjects

Hyunjin Park

Publications and source records attributed to Hyunjin Park.

5 recordsLinked to original sources

PubMed2006

Dietary supplementation of probiotic Bacillus polyfermenticus, Bispan strain, modulates natural killer cell and T cell subset populations and immunoglobulin G levels in human subjects.

A probiotic is a viable microbial dietary supplement that has beneficial effects such as prevention and treatment of specific gastrointestinal disorders, including counteracting gut barrier dysfunction associated with inflammation and infection. Probiotic Bacillus polyfermenticus, which is commonly called Bispan strain, has been appropriately used for the treatment of long-term intestinal disorders. The use of B. polyfermenticus for immune-related chronic intestinal disease may be appropriate considering that about 80% of the body's immune system is localized in the gastrointestinal tract. The current study aimed to evaluate the effect of probiotic B. polyfermenticus on the immune response of human subjects through the quantification of immune cell population and serum levels of immunoglobulins (Igs). Twenty-five male subjects, 20-35 years of age, were randomly assigned to either a control group (n =12) supplemented with a placebo or the experimental group (n = 13) supplemented with B. polyfermenticus tablets at a dose of 3.1 x 10(8) colony-forming units/day for 8 weeks. Dietary intake analyses from 3-day dietary records from three consecutive days including one weekend day and two weekdays revealed no significant differences in total energy and nutrient intakes between the two groups. The humoral immune response was monitored by the number of total B lymphocytes and serum concentrations of IgG, IgA, and IgM. To investigate the changes in immune cell populations, percentages of total T lymphocytes, CD4+ helper T cells, CD8+ cytotoxic T cells, and CD56+ natural killer (NK) cells were quantified. The concentration of IgG in the experimental group was 12% higher than in the placebo group after 8 weeks of Bispan supplementation. Also, the percentages of CD4+ helper T cells, CD8+cytotoxic T cells, and CD56+ NK cells in the Bispan strain-supplemented group were 32%, 28%, and 35% higher, respectively, compared with the control group. Because of a higher increment of the CD4+ T cell subset than CD8+ T cells, the ratio of CD4+/CD8+ T cells was greater in the experimental group. This study suggests that the supplementation of B. polyfermenticus has a potentially positive effect on immune function by enhancing IgG production as well as by modulating the number of immune cell population such as CD4+ and CD8+ T cells and NK cells.

Adult↗

PubMed2005

Least biased target selection in probabilistic atlas construction.

Probabilistic atlas has broad applications in medical image segmentation and registration. The most common problem building a probabilistic atlas is picking a target image upon which to map the rest of the training images. Here we present a method to choose a target image that is the closest to the mean geometry of the population under consideration as determined by bending energy. Our approach is based on forming a distance matrix based on bending energies of all pair-wise registrations and performing multidimensional scaling (MDS) on the distance matrix.

Algorithms↗

PubMed2004

Adaptive registration using local information measures.

Rapidly advancing registration methods increasingly employ warping transforms. High degrees of freedom (DOF) warpings can be specified by manually placing control points or instantiating a regular, dense grid of control points everywhere. The former approach is laborious and prone to operator bias, whereas the latter is computationally expensive. We propose to improve upon the latter approach by adaptively placing control points where they are needed. Local estimates of mutual information (MI) and entropy are used to identify local regions requiring additional DOF.

Abdomen↗

PubMed2003

Construction of an abdominal probabilistic atlas and its application in segmentation.

There have been significant efforts to build a probabilistic atlas of the brain and to use it for many common applications, such as segmentation and registration. Though the work related to brain atlases can be applied to nonbrain organs, less attention has been paid to actually building an atlas for organs other than the brain. Motivated by the automatic identification of normal organs for applications in radiation therapy treatment planning, we present a method to construct a probabilistic atlas of an abdomen consisting of four organs (i.e., liver, kidneys, and spinal cord). Using 32 noncontrast abdominal computed tomography (CT) scans, 31 were mapped onto one individual scan using thin plate spline as the warping transform and mutual information (MI) as the similarity measure. Except for an initial coarse placement of four control points by the operators, the MI-based registration was automatic. Additionally, the four organs in each of the 32 CT data sets were manually segmented. The manual segmentations were warped onto the "standard" patient space using the same transform computed from their gray scale CT data set and a probabilistic atlas was calculated. Then, the atlas was used to aid the segmentation of low-contrast organs in an additional 20 CT data sets not included in the atlas. By incorporating the atlas information into the Bayesian framework, segmentation results clearly showed improvements over a standard unsupervised segmentation method.

Algorithms↗

PubMed2003

Method for quantifying volumetric lesion change in interval liver CT examinations.

We propose a method of using a relatively low degree of freedom (DOF) warping to accurately measure the interval change of lesions having homogeneous contrast. The setting presented here presupposes the use of interval computed tomography (CT) liver exams. After a 3 x 24 DOF warping of the later examination to match the liver's pose in the earlier exam of the interval pair is performed, the lesion's volume change is estimated using the computed difference volume of the two data sets via a novel method that counts partial volume contributions and is insensitive to slight misregistration. A mathematically generated phantom is used to quantify accuracy in the presence of noise. We also quantify the accuracy of our CT liver registrations using microcoils implanted for chemotherapy. A probabilistic liver atlas is used to support automatic masking and liver-focused registration.

Carcinoma, Hepatocellular↗