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The aim of this appraisal of the literature is to elucidate the effects of immunosuppressive and immunomodulating agents used to treat atopic dermatitis (AD) on risk factors for fertility, pregnancy, and breastfeeding. Negative side effects of the psychological and physical stresses associated to AD flairs and uncontrolled AD are discussed, in order to evaluate the consequences of abstaining from treatment. Research on pregnancies in Danish women suggests a tendency towards an increased use of topical steroids and ultraviolet light therapy during pregnancy, compared to before conception, confirming the need for these patients to receive treatment, as well as decreased use of systemic treatments, suggesting a tendency towards undertreatment in this patient population. It is important that effective treatment be provided to pregnant women with AD. Here we present an appraisal of current knowledge on treatments for AD and the risks of exposure for the fetus and breastfed infant. Since little is known about the association between AD, pregnancy, and systemic treatment, we generalize conclusions based on studies on treatments of pregnant women who have undergone organ transplantation and who have inflammatory bowel disease, rheumatic disease, and autoimmune disease. The majority of recommendations are therefore based on a low or very low quality of evidence according to the GRADE system. The selected studies reflect the authors’ assessment regarding originality and importance in the context of this appraisal. It is always the treating doctor’s responsibility to stay updated on current literature when treating patients, especially pregnant patients.

Future networks (5G and beyond) will demand provisioning of novel services with a varied range of service requirements, and an order of magnitude increase of wireless data traffic, while at the same time decreasing the network operation cost. To address these issues, wireless network virtualization is seen as a key enabling technology for future networks. Virtualization offers cost-effective and efficient resource utilization by sharing the resources among multiple operators. Also, in contrast to traditional service specific inflexible network implementations, adoption of innovative network technologies demand certain level of flexibility and control over the networking fabric. In this respect, we argue that, for realizing a programmable and flexible heterogeneous virtual network infrastructure, software defined networking (SDN) & cloud computing technologies are the key tools to leverage on. This will ensure efficient resource utilization through sharing, enable programming of the underlying heterogeneous network infrastructure as a system and provide on-demand, elastic resource provisioning. In this paper, we lay out the key requirements and architectural components of end-to-end programmable heterogeneous virtual wireless networks (HVWNs). To report on the advances made in the pertinent technologies in realizing a HVWN architecture, we also present a brief survey of research on wireless network that leverage SDN, programmable radio plane and cloud computing technologies. We also investigate various actors and their roles in such HVWN environment and address various research issues and challenges in realizing such an architecture.

Graphical abstractDisplay Omitted HighlightsOil and gas pipelines are subject to many types of metal-loss defects.Those defects can pose a high risk to the operational safety of the pipeline.This paper proposes a solution to detect, locate, and estimate the size of defects.The proposed solution uses pattern-adapted wavelets and artificial neural networks.The proposed solution is general and applies to a wide range of defect shapes. Signals collected from the magnetic scans of metal-loss defects have distinct patterns. Experienced pipeline engineers are able to recognize those patterns in magnetic flux leakage (MFL) scans of pipelines, and use them to characterize defect types (e.g., corrosion, cracks, dents, etc.) and estimate their lengths and depths. This task, however, can be highly cumbersome to a human operator, because of the large amount of data to be analyzed. This paper proposes a solution to automate the analysis of MFL signals. The proposed solution uses pattern-adapted wavelets to detect and estimate the length of metal-loss defects. Once the parts of MFL signals corresponding to metal-loss defects are isolated, artificial neural networks are used to predict their depth. The proposed technique is computationally efficient, achieves high levels of accuracy, and works for a wide range of defect shapes.

Capgras Syndrome is a subcategory of delusional disorder. People affected by this syndrome believe that a close associate such as a friend or family member has been replaced by an identical imposter. This case report describes a 23-year-old woman with no prior psychiatric history, whom developed Capgras syndrome, via folie a deux, in the setting of poly-substance use. In this patient, a combination of Aripiprazole 10 mg daily and Escitalopram 10 mg daily were effective in resolving symptoms. Clonazepam was utilized for anxiety and Omega-3 fatty acids 1 g for anti-oxidative effects. Further studies are needed to investigate the effects of a variety of causes and treatments for Capgras Syndrome.

Pain catastrophizing has been suggested as a prospective risk factor for poor postoperative pain outcomes in total joint arthroplasty (TJA). However, results from the previous studies have been mixed and have not controlled for postoperative opiate analgesic intake. This study investigates pain catastrophizing and postoperative pain intensity in TJA patients, adjusting for analgesic intake. We hypothesized that “pain catastrophizers” would exhibit higher pain scores and increased analgesic requirements postoperatively. In this prospective cohort study, patients were defined as catastrophizers (PCS > 30), or non-catastrophizers (PCS ≤ 30). The primary outcome was the visual analog scale (VAS) pain score at 3-month follow-up. Secondary outcomes included length of stay (LOS) for the index hospitalization, total daily opiate analgesic intake, and VAS pain scores on postoperative days 0, 1, 2, and 3 through discharge. Multivariable regression was used to control for total daily morphine equivalent dose consumed during the stay in addition to other clinical and demographic factors. There were 87 patients in the “non-catastrophizing” and 36 in the “catastrophizing” groups. There was no clinically significant difference in VAS pain scores between groups at 3-month follow-up. Patients with a length of stay (LOS) ≥ 3 postoperative days differed in VAS pain scores (“non-catastrophizers” = 5.08 vs. “catastrophizers” = 7.13; p = 0.002) and were 2.4 times more likely to be catastrophizers than non-catastrophizers (p = 0.042). There were no differences in the remaining secondary outcomes. The pain catastrophizing scale is a poor predictor of postoperative pain at 3-month follow-up. However, it may be a risk factor for increased LOS.