Previous studies have shown that small-molecule BCL-2 inhibitors can have a synergistic interaction with ABCG2 substrates in chemotherapy. Venetoclax is a potent and selective BCL-2 inhibitor, approved by the FDA in 2016 for the treatment of patients with chronic lymphocytic leukemia (CLL). This study showed that, at a non-toxic concentration, venetoclax at 10 µM significantly reversed multidrug resistance (MDR) mediated by wild-type ABCG2, without significantly affecting MDR mediated by mutated ABCG2 (R482G and R482T) and ABCB1, while moderate or no reversal effects were observed at lower concentrations (0.5 to 1 µM). The results showed that venetoclax increased the intracellular accumulation of chemotherapeutic agents, which was the result of directly blocking the wild-type ABCG2 efflux function and inhibiting the ATPase activity of ABCG2. Our study demonstrated that venetoclax potentiates the efficacy of wild-type ABCG2 substrate drugs. These findings may provide useful guidance in combination therapy against wild-type ABCG2-mediated MDR cancer in clinical practice.

We present a new approach that enables investors to seek a reasonably robust policy for portfolio selection in the presence of rare but high-impact realization of moment uncertainty. In practice, portfolio managers face diffculty in seeking a balance between relying on their knowledge of a reference financial model and taking into account possible ambiguity of the model. Based on the concept of Distributionally Robust Optimization (DRO), we introduce a new penalty framework that provides investors flexibility to define prior reference models using moment information and accounts for model ambiguity in terms of "extreme" moment uncertainty. We show that in our approach a globally-optimal portfolio can in general be obtained in a computationally tractable manner. We also show that for a wide range of specifications our proposed model can be recast as semidefinite programs. Computational experiments show that our penalized moment-based approach outperforms classical DRO approaches in terms of both average and downside-risk performance using historical data.

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Purpose We investigated fractional anisotropy (FA) and radial diffusivity (RD) in a canine model of mucopolysaccharidosis (MPS). We hypothesized that canines affected with MPS would exhibit decreased FA and increased RD values when compared to unaffected canines, a trend that has been previously described in humans with white matter diseases. Methods Four unaffected canines and two canines with MPS were euthanized at 18 weeks of age. Their brains were imaged using high-resolution diffusion tensor imaging (DTI) on a 7T small-animal magnetic resonance imaging system. One hundred regions of interest (ROIs) were placed in each of four white matter regions: anterior and posterior regions of the internal capsule (AIC and PIC, respectively) and anterior and posterior regions of the centrum semiovale (ACS and PCS, respectively). For each specimen, average FA and RD values and associated 95% confidence intervals were calculated from 100 ROIs for each brain region. Results For each brain region, the FA values in MPS brains were consistently lower than in unaffected dogs, and the RD values in MPS dogs were consistently higher, supporting our hypothesis. The confidence intervals for affected and unaffected canines did not overlap in any brain region. Conclusion FA and RD values followed the predicted trend in canines affected with MPS, a trend that has been described in humans with lysosomal storage and dysmyelinating diseases. These findings suggest that the canine model parallels MPS in humans, and further indicates that quantitative DTI analysis of such animals may be suitable for future study of disease progression and therapeutic response in MPS.

Purpose The purpose of this study was to compare quantitative diffusion tensor imaging metrics in dogs affected with a model of Krabbe disease to age-matched normal controls. We hypothesized that fractional anisotropy would be decreased and radial diffusivity would be increased in the Krabbe dogs. Methods We used a highly reproducible region-of-interest interrogation technique to measure fractional anisotropy and radial diffusivity in three different white matter regions within the internal capsule and centrum semiovale in four Krabbe affected brains and three age-matched normal control brains. Results Despite all four Krabbe dogs manifesting pelvic limb paralysis at the time of death, age-dependent differences in DTI metrics were observed. In the 9, 12, and 14 week old Krabbe dogs, FA values unexpectedly increased and RD values decreased. FA values were generally higher and RD values generally lower in both regions of the internal capsule in the Krabbe brains during this period. FA values in the brain from the 16 week old Krabbe dog decreased and were lower than in control brains and RD values increased and were higher than in control brain. Conclusion Our findings suggest that FA and RD in the internal capsule and centrum semiovale are affected differently at different ages, despite disease having progressed to pelvic limb paralysis in all dogs evaluated. In 9, 12, and 14 week old Krabbe dogs, higher FA values and lower RD values are seen in the internal capsule. However, in the 16 week old Krabbe dog, lower FA and higher RD values are seen, consistent with previous observations in Krabbe dogs, as well as observations in human Krabbe patients.

Purpose We describe a novel technique for measuring diffusion tensor imaging metrics in the canine brain. We hypothesized that a standard method for region of interest placement could be developed that is highly reproducible, with less than 10% difference in measurements between raters. Methods Two sets of canine brains (three seven-week-old full-brains and two 17-week-old single hemispheres) were scanned ex-vivo on a 7T small-animal magnetic resonance imaging system. Strict region of interest placement criteria were developed and then used by two raters to independently measure diffusion tensor imaging metrics within four different white-matter regions within each specimen. Average values of fractional anisotropy, radial diffusivity, and the three eigenvalues (λ1, λ2, and λ3) within each region in each specimen overall and within each individual image slice were compared between raters by calculating the percentage difference between raters for each metric. Results The mean percentage difference between raters for all diffusion tensor imaging metrics when pooled by each region and specimen was 1.44% (range: 0.01-5.17%). The mean percentage difference between raters for all diffusion tensor imaging metrics when compared by individual image slice was 2.23% (range: 0.75-4.58%) per hemisphere. Conclusion Our results indicate that the technique described is highly reproducible, even when applied to canine specimens of differing age, morphology, and image resolution. We propose this technique for future studies of diffusion tensor imaging analysis in canine brains and for cross-sectional and longitudinal studies of canine brain models of human central nervous system disease.

Purpose The purpose of this study was to investigate a novel tensor shape plot analysis technique of diffusion tensor imaging data as a means to assess microstructural differences in brain tissue. We hypothesized that this technique could distinguish white matter regions with different microstructural compositions. Methods Three normal canines were euthanized at seven weeks old. Their brains were imaged using identical diffusion tensor imaging protocols on a 7T small-animal magnetic resonance imaging system. We examined two white matter regions, the internal capsule and the centrum semiovale, each subdivided into an anterior and posterior region. We placed 100 regions of interest in each of the four brain regions. Eigenvalues for each region of interest triangulated onto tensor shape plots as the weighted average of three shape metrics at the plot's vertices: CS, CL, and CP. Results The distribution of data on the plots for the internal capsule differed markedly from the centrum semiovale data, thus confirming our hypothesis. Furthermore, data for the internal capsule were distributed in a relatively tight cluster, possibly reflecting the compact and parallel nature of its fibers, while data for the centrum semiovale were more widely distributed, consistent with the less compact and often crossing pattern of its fibers. This indicates that the tensor shape plot technique can depict data in similar regions as being alike. Conclusion Tensor shape plots successfully depicted differences in tissue microstructure and reflected the microstructure of individual brain regions. This proof of principle study suggests that if our findings are reproduced in larger samples, including abnormal white matter states, the technique may be useful in assessment of white matter diseases.

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