Massive hemorrhage after percutaneous nephrolithotomy: Saving the kidney when angioembolization has failed or is unavailable

AbstractObjectivesTo describe the management protocol in cases with massive hemorrhage after percutaneous nephrolithotomy (PCNL) with a failed angioembolization or when angioembolization is not available.Patients and methodsBetween October 2006 and December 2012, the charts of patients who had undergone PCNL and were complicated with massive post procedural bleeding unresponsive to conservative management were reviewed. Those cases in whom angioembolization had failed, or was unavailable, or could not be afforded by the patient were selected and studied. These patients underwent open surgical exploration through a midline transperitoneal or a flank retroperitoneal approach. In both approaches, kidney mobilization outside the Gerota's fascia, temporal renal pedicle clamping and partial nephrectomy or renorrhaphy were done in a stepwise manner.ResultsDuring the study period, we had 8 patients for whom angioembolization had failed (n = 4), was not available (n = 2) or the patient could not afford it (n = 2). Median patients' age was 31 years (range 16–59 years). We did a partial nephrectomy in 2 and renorrhaphy in 6 of patients with a successful outcome. Median operative time was 2.25 h and median warm ischemia time was 26 min (range 24–42 min). After a median follow up period of 21 months, the involved renal unit, in all cases, remained functional in the postoperative intravenous urography.ConclusionMassive hemorrhage after PCNL when angioembolization failed or was not feasible due to any reason could be controlled by partial nephrectomy or renorrhaphy with the same principles as that used for surgical exploration in patients with high grade renal trauma

Massive hemorrhage after percutaneous nephrolithotomy: Saving the kidney when angioembolization has failed or is unavailable

AbstractObjectivesTo describe the management protocol in cases with massive hemorrhage after percutaneous nephrolithotomy (PCNL) with a failed angioembolization or when angioembolization is not available.Patients and methodsBetween October 2006 and December 2012, the charts of patients who had undergone PCNL and were complicated with massive post procedural bleeding unresponsive to conservative management were reviewed. Those cases in whom angioembolization had failed, or was unavailable, or could not be afforded by the patient were selected and studied. These patients underwent open surgical exploration through a midline transperitoneal or a flank retroperitoneal approach. In both approaches, kidney mobilization outside the Gerota's fascia, temporal renal pedicle clamping and partial nephrectomy or renorrhaphy were done in a stepwise manner.ResultsDuring the study period, we had 8 patients for whom angioembolization had failed (n = 4), was not available (n = 2) or the patient could not afford it (n = 2). Median patients' age was 31 years (range 16–59 years). We did a partial nephrectomy in 2 and renorrhaphy in 6 of patients with a successful outcome. Median operative time was 2.25 h and median warm ischemia time was 26 min (range 24–42 min). After a median follow up period of 21 months, the involved renal unit, in all cases, remained functional in the postoperative intravenous urography.ConclusionMassive hemorrhage after PCNL when angioembolization failed or was not feasible due to any reason could be controlled by partial nephrectomy or renorrhaphy with the same principles as that used for surgical exploration in patients with high grade renal trauma

A conservative framework for obtaining uncertain bands of multiple wind farms in electric power networks by proposed IGDT-based approach considering decision-maker's preferences

Exploiting clean energy resources (CERs) is an applicable way to enhance sustainable development and have the cleaner production of electricity. On the other hand, variability and intermittency of these clean resources are the important disadvantages for determining the reliable operation of electrical grids. Thus, using the uncertainty modeling techniques seems necessary to have more practical values for the decision-making variables. The current paper demonstrates a novel architecture based on Information Gap Decision Theory (IGDT) to model the randomness of multiple Wind Farms (WFs) existing in electric power networks. Note that employing only the IGDT technique cannot consider the preferences defined by the decision-maker. In contrast, the proposed method tackles this issue by considering different values for radii of uncertainty related to the uncertain parameters. It has been proven that the presented approach is time-saving if compared with Monte Carlo Simulation (MCS) and the Epsilon-constraint-based-IGDT. Moreover, the execution time of the presented methodology does not considerably depend on the number of WFs for a power system. It means that if the number of WFs increases in a particular case study, consequently, the execution time does not noticeably rise if compared with the MCS and the Epsilon-constraint-based-IGDT. Furthermore, the equivalent Mixed Integer Linear Programming (MILP) of the original model is employed to guarantee the optimum solution. The performances of the presented methodology have been demonstrated by utilizing IEEE 30 BUS and IEEE 62 BUS systems.© 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

Development of Chitosan/Gelatin/Keratin Composite Containing Hydrocortisone Sodium Succinate as a Buccal Mucoadhesive Patch to Treat Desquamative Gingivitis

The aim of this research was to develop chitosan/gelatin/keratin composite containing hydrocortisone sodium succinate as a buccal mucoadhesive patch to treat desquamative gingivitis, which was fabricated through an environmental friendly process. Mucoadhesive films increase the advantage of higher efficiency and drug localization in the affected region. In this research, mucoadhesive films, for the release of hydrocortisone sodium succinate, were prepared using different ratios of chitosan, gelatin and keratin. In the first step, chitosan and gelatin proportions were optimized after evaluating the mechanical properties, swelling capacity, water uptake, stability, and biodegradation of the films. Then, keratin was added at different percentages to the optimum composite of chitosan and gelatin together with the drug. The results of surface pH showed that none of the samples were harmful to the buccal cavity. FTIR analysis confirmed the influence of keratin on the structure of the composite. The presence of a higher amount of keratin in the composite films resulted in high mechanical, mucoadhesive properties and stability, low water uptake and biodegradation in phosphate buffer saline (pH = 7.4) containing 104 U/ml lysozyme. The release profile of the films ascertained that keratin is a rate controller in the release of the hydrocortisone sodium succinate. Finally, chitosan/gelatin/keratin composite containing hydrocortisone sodium succinate can be employed in dental applications

Convolutional Neural Network Optimization and Parallel Compressive Sensing Algorithms for Accelerated MRI Reconstruction

Magnetic resonance imaging (MRI) is a noninvasive imaging modality that produces high-quality images. One of the biggest challenges in MRI is the lengthy scan procedure which could lead to motion artifact and patient discomfort. Due to the physical and physiological limits, undersampling the signals in the k-space signals has been used to shorten the scan time. However, the undersampling of k-space data results in undersampling artifacts that require advanced reconstruction algorithms to compensate for the missed signals. Many reconstruction algorithms have been proposed to address this problem. Linear interpolations in parallel imaging (PI) techniques usually suffer from high noise-like interpolation artifacts, and compressive sensing (CS) reconstructions are usually blurred in high-order undersampling factors. In this study, we first introduce a hybrid CS-PI algorithm and show it outperforms CS or PI individually in image reconstructions using actual data from MR-guided radiotherapy. Nevertheless, PI, CS, and hybrid CS-PI highly depend on the number of ACS in the center of the k-space and require a particular sampling strategy. Deep learning models can solve these problems with lower scan and reconstruction time with fewer interpolation artifacts and blurriness. In deep learning-based MRI reconstruction methods, the network’s architecture plays a crucial role in the quality of the reconstructed image. According to the large number of architectures that can be considered for these models, manually designing architectures and testing all the possible solutions are not practical. We introduce a new evolutionary-based search strategy to design a deep network for MR reconstruction automatically. We use different numerical metrics to compare the results of the optimized model with the ad-hoc model and three different published methods. The results showed that the proposed algorithm could automatically design a network that is not limited to only one particular sampling strategy and outperforms three related published models