In 2001, the US Food and Drug Administration (FDA) approved the first transvaginal mesh kit to treat pelvic organ prolapse (POP). Since the introduction of vaginal mesh kits, some vaginal meshes have been associated with chronic pelvic pain after reconstructive pelvic floor surgery. Pelvic pain results in between 0 % and 30 % of patients following transvaginal mesh placement. Common causes of chronic pelvic pain include pelvic floor muscle spasm, pudendal neuralgia, and infection. Paucity of data exists on the effective management of chronic pelvic pain after pelvic reconstructive surgery with mesh. We outline the management of chronic pelvic pain after transvaginal mesh placement for reconstructive pelvic floor repair based on our clinical experience and adaptation of data used in other aspects of managing chronic pelvic pain conditions.
T he parenteral administration route is the most effective and common form of delivery for active drug substances with poor bioavailability and the drugs with a narrow therapeutic index. Drug delivery technology that can reduce the total number of injection throughout the drug therapy period will be truly advantageous not only in terms of compliance, but also to improve the quality of the therapy. Such reduction in frequency of drug dosing is achieved by the use of specific formulation technologies that guarantee the release of the active drug substance in a slow and predictable manner. The development of new injectable drug delivery system has received considerable attention over the past few years. A number of technological advances have been made in the area of parenteral drug delivery leading to the development of sophisticated systems that allow drug targeting and the sustained or controlled release of parenteral medicines.
Hyperspectral images (HSIs) have high spectral resolution, but they suffer from low spatial resolution. In this paper, a new learning-based approach for super-resolution (SR) using the discrete wavelet transform (DWT) is proposed. The novelty of our approach lies in designing application-specific wavelet basis (filter coefficients). An initial estimate of SR is obtained by using these filter coefficients while learning the high-frequency details in the wavelet domain. The final solution is obtained using a sparsity-based regularization framework, in which image degradation and the sparseness of SR are estimated using the estimated wavelet filter coefficients (EWFCs) and the initial SR estimate, respectively. The advantage of the proposed algorithm lies in 1) the use of EWFCs to represent an optimal point spread function to model image acquisition process; 2) use of sparsity prior to preserve neighborhood dependencies in SR image; and 3) avoiding the use of registered images while learning the initial estimate. Experiments are conducted on three different kinds of images. Visual and quantitative comparisons confirm the effectiveness of the proposed method.
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