Polycystic ovary syndrome (PCOS) is the most common female endocrinopathy associated with both reproductive and metabolic disorders. Dehydroepiandrosterone (DHEA) is currently used to induce a PCOS mouse model. High-fat diet (HFD) has been shown to cause obesity and infertility in female mice. The possible effect of an HFD on the phenotype of DHEA-induced PCOS mice is unknown. The aim of the present study was to investigate both reproductive and metabolic features of DHEA-induced PCOS mice fed a normal chow or a 60% HFD. Prepubertal C57BL/6 mice (age 25 days) on the normal chow or an HFD were injected (s.c.) daily with the vehicle sesame oil or DHEA for 20 consecutive days. At the end of the experiment, both reproductive and metabolic characteristics were assessed. Our data show that an HFD did not affect the reproductive phenotype of DHEA-treated mice. The treatment of HFD, however, caused significant metabolic alterations in DHEA-treated mice, including obesity, glucose intolerance, dyslipidemia, and pronounced liver steatosis. These findings suggest that HFD induces distinct metabolic features in DHEA-induced PCOS mice. The combined DHEA and HFD treatment may thus serve as a means of studying the mechanisms involved in metabolic derangements of this syndrome, particularly in the high prevalence of hepatic steatosis in women with PCOS.
We investigate an optimization-based reconstruction, with an emphasis on image-artifact reduction, from data collected in C-arm cone-beam computed tomography (CBCT) employed in image-guided interventional procedures. In the study, an image to be reconstructed is formulated as a solution to a convex optimization program in which a weighted data divergence is minimized subject to a constraint on the image total variation (TV); a data-derivative fidelity is introduced in the program specifically for effectively suppressing dominant, low-frequency data artifact caused by, e.g., data truncation; and the Chambolle-Pock (CP) algorithm is tailored to reconstruct an image through solving the program. Like any other reconstructions, the optimization-based reconstruction considered depends upon numerous parameters. We elucidate the parameters, illustrate their determination, and demonstrate their impact on the reconstruction. The optimization-based reconstruction, when applied to data collected from swine and patient subjects, yields images with visibly reduced artifacts in contrast to the reference reconstruction, and it also appears to exhibit a high degree of robustness against distinctively different anatomies of imaged subjects and scanning conditions of clinical significance. Knowledge and insights gained in the study may be exploited for aiding in the design of practical reconstructions of truly clinical-application utility.
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