Mechanisms of liposomal all-{\it trans\/} retinoic acid and vitamin D3 induced inhibition of cell proliferation and stimulation of apoptosis in aggressive B-cell non-Hodgkin's lymphoma

The Non-Hodgkin's Lymphoma (NHLs) are neoplasms of the immune system. Currently, less than 1% of the etiology of the 22,000 newly diagnosed lymphoma cases in the U.S.A. every year is known. This disease has a significant prevalence and high mortality rate. Cell growth in lymphomas has been shown to be an important parameter in aggressive NHL when establishing prognosis, as well as an integral part in the pathophysiology of the disease process. While many aggressive B cell NHLs respond initially to chemotherapeutic regimens such as CHOP-bleo (adriamycin, vincristine and bleomycin) etc., relapse is common, and the patient is then often refractory to further salvage treatment regimens.^ To assess their potential to inhibit aggressive B cell NHLs and induce apoptosis (also referred to as programmed cell death (PCD)), it was proposed to utilize the following biological agents-liposomal all-trans retinoic acid (L-ATRA) which is a derivative of Vitamin A in liposomes and Vitamin D3. Preliminary evidence indicates that L-ATRA may inhibit cell growth in these cells and may induce PCD as well. Detailed studies were performed to understand the above phenomena by L-ATRA and Vitamin D3 in recently established NHL-B cell lines and primary cell cultures. The gene regulation involved in the case of L-ATRA was also delineated. ^

Validity of using item response theory to analyze data from the Work Limitations Questionnaire

The Work Limitations Questionnaire (WLQ) is used to determine the amount of work loss and productivity which stem from certain health conditions, including rheumatoid arthritis and cancer. The questionnaire is currently scored using methodology from Classical Test Theory. Item Response Theory, on the other hand, is a theory based on analyzing item responses. This study wanted to determine the validity of using Item Response Theory (IRT), to analyze data from the WLQ. Item responses from 572 employed adults with dysthymia, major depressive disorder (MDD), double depressive disorder (both dysthymia and MDD), rheumatoid arthritis and healthy individuals were used to determine the validity of IRT (Adler et al., 2006).^ PARSCALE, which is IRT software from Scientific Software International, Inc., was used to calculate estimates of the work limitations based on item responses from the WLQ. These estimates, also known as ability estimates, were then correlated with the raw score estimates calculated from the sum of all the items responses. Concurrent validity, which claims a measurement is valid if the correlation between the new measurement and the valid measurement is greater or equal to .90, was used to determine the validity of IRT methodology for the WLQ. Ability estimates from IRT were found to be somewhat highly correlated with the raw scores from the WLQ (above .80). However, the only subscale which had a high enough correlation for IRT to be considered valid was the time management subscale (r = .90). All other subscales, mental/interpersonal, physical, and output, did not produce valid IRT ability estimates.^ An explanation for these lower than expected correlations can be explained by the outliers found in the sample. Also, acquiescent responding (AR) bias, which is caused by the tendency for people to respond the same way to every question on a questionnaire, and the multidimensionality of the questionnaire (the WLQ is composed of four dimensions and thus four different latent variables) probably had a major impact on the IRT estimates. Furthermore, it is possible that the mental/interpersonal dimension violated the monotonocity assumption of IRT causing PARSCALE to fail to run for these estimates. The monotonicity assumption needs to be checked for the mental/interpersonal dimension. Furthermore, the use of multidimensional IRT methods would most likely remove the AR bias and increase the validity of using IRT to analyze data from the WLQ.^

Generalized fuzzy clustering for segmentation of multi-spectral magnetic resonance images.

An integrated approach for multi-spectral segmentation of MR images is presented. This method is based on the fuzzy c-means (FCM) and includes bias field correction and contextual constraints over spatial intensity distribution and accounts for the non-spherical cluster's shape in the feature space. The bias field is modeled as a linear combination of smooth polynomial basis functions for fast computation in the clustering iterations. Regularization terms for the neighborhood continuity of intensity are added into the FCM cost functions. To reduce the computational complexity, the contextual regularizations are separated from the clustering iterations. Since the feature space is not isotropic, distance measure adopted in Gustafson-Kessel (G-K) algorithm is used instead of the Euclidean distance, to account for the non-spherical shape of the clusters in the feature space. These algorithms are quantitatively evaluated on MR brain images using the similarity measures.