Abstract: Since hyaluronic acid (HA) receptor such as CD44 is
over-expressed at sites of cancer cells, HA can be used as a targeting
vehicles for anti-cancer drugs. The aim of this study is to synthesize
block copolymer composed of hyaluronic acid and
poly(ε-caprolactone) (HAPCL) and to fabricate polymeric micelles for
anticancer drug targeting against CD44 receptor of tumor cells.
Chemical composition of HAPCL was confirmed using 1H NMR
spectroscopy. Doxorubicin (DOX) was incorporated into polymeric
micelles of HAPCL. The diameters of HAPHS polymeric micelles
were changed around 80nm and have spherical shapes. Targeting
potential was investigated using CD44-overexpressing. When
DOX-incorporated polymeric micelles was added to KB cells, they
revealed strong red fluorescence color while blocking of CD44
receptor by pretreatment of free HA resulted in reduced intensity,
indicating that HAPCL polymeric micelles have targetability against
CD44 receptor.
Abstract: Aptamers are useful tools in microorganism
researches, diagnoses, and treatment. Aptamers are specific target
molecules formed by oligonucleic acid molecules, and are not
decomposed by alcohol. Aptamers used to detect Mycobacterium
tuberculosis (MTB) have been proved to have specific affinity to the
outer membrane proteins of MTB. This article presents a biosensor
chip set with aptamers for early detection of MTB with high specificity
and sensitivity, even in very low concentration. Meanwhile, we have
already made a modified hydrophobic facial mask module with
internal rendering hydrophobic for effectively collecting M.
tuberculosis.
Abstract: To evaluate the ability to predict xerostomia after
radiotherapy, we constructed and compared neural network and
logistic regression models. In this study, 61 patients who completed a
questionnaire about their quality of life (QoL) before and after a full
course of radiation therapy were included. Based on this questionnaire,
some statistical data about the condition of the patients’ salivary
glands were obtained, and these subjects were included as the inputs of
the neural network and logistic regression models in order to predict
the probability of xerostomia. Seven variables were then selected from
the statistical data according to Cramer’s V and point-biserial
correlation values and were trained by each model to obtain the
respective outputs which were 0.88 and 0.89 for AUC, 9.20 and 7.65
for SSE, and 13.7% and 19.0% for MAPE, respectively. These
parameters demonstrate that both neural network and logistic
regression methods are effective for predicting conditions of parotid
glands.
Abstract: An alarming emergence of multidrug-resistant strains
of the tuberculosis pathogen Mycobacterium tuberculosis and
continuing high worldwide incidence of tuberculosis has invigorated
the search for novel drug targets. The enzyme glutamate racemase
(MurI) in bacteria catalyzes the stereoconversion of L-glutamate to
D-glutamate which is a component of the peptidoglycan cell wall of
the bacterium. The inhibitors targeted against MurI from several
bacterial species have been patented and are advocated as promising
antibacterial agents. However there are none available against MurI
from Mycobacterium tuberculosis, due to the lack of its threedimensional
structure. This work accomplished two major objectives.
First, the tertiary structure of MtMurI was deduced computationally
through homology modeling using the templates from bacterial
homologues. It is speculated that like in other Gram-positive bacteria,
MtMurI exists as a dimer and many of the protein interactions at the
dimer interface are also conserved. Second, potent candidate
inhibitors against MtMurI were identified through docking against
already known inhibitors in other organisms.