Increasing age is a known negative prognostic factor for glioblastoma. https://www.selleckchem.com/products/10-dab-10-deacetylbaccatin.html However, a multifactorial approach is necessary to achieve optimal neuro-oncological treatment. It remains unclear to what extent frailty, comorbidity burden, and obesity might exert influence on survival in geriatric glioblastoma patients. We have therefore reviewed our institutional database to assess the prognostic value of these factors in elderly glioblastoma patients. Between 2012 and 2018, patients aged ≥ 65years with newly diagnosed glioblastoma were included in this retrospective analysis. Patients frailty was analyzed using the modified frailty index (mFI), while patients comorbidity burden was assessed according to the Charlson comorbidity index (CCI). Body mass index (BMI) was used as categorized variable. A total of 110 geriatric patients with newly diagnosed glioblastoma were identified. Geriatric patients categorized as least-frail achieved a median overall survival (mOS) of 17months, whereas most frail patients achieved a mOS of 8months (p = 0.003). Patients with a CCI > 2 had a lower mOS of 6months compared to patients with a lower comorbidity burden (12months; p = 0.03). Multivariate analysis identified "subtotal resection" (p = 0.02), "unmethylated MGMT promoter status" (p = 0.03), "BMI < 30" (p = 0.04), and "frail patient (mFI ≥ 0.27)" (p = 0.03) as significant and independent predictors of 1-year mortality in geriatric patients with surgical treatment of glioblastoma (Nagelkerke's R 0.31). The present study concludes that both increased frailty and comorbidity burden are significantly associated with poor OS in geriatric patients with glioblastoma. Further, the present series suggests an obesity paradox in geriatric glioblastoma patients. The present study concludes that both increased frailty and comorbidity burden are significantly associated with poor OS in geriatric patients with glioblastoma. Further, the present series suggests an obesity paradox in geriatric glioblastoma patients. Data augmentation is a common technique to overcome the lack of large annotated databases, a usual situation when applying deep learning to medical imaging problems. Nevertheless, there is no consensus on which transformations to apply for a particular field. This work aims at identifying the effect of different transformations on polyp segmentation using deep learning. A set of transformations and ranges have been selected, considering image-based (width and height shift, rotation, shear, zooming, horizontal and vertical flip and elastic deformation), pixel-based (changes in brightness and contrast) and application-based (specular lights and blurry frames) transformations. A model has been trained under the same conditions without data augmentation transformations (baseline) and for each of the transformation and ranges, using CVC-EndoSceneStill and Kvasir-SEG, independently. Statistical analysis is performed to compare the baseline performance against results of each range of each transformation on the sed transformations behave similarly in both datasets. Polyp area, brightness and contrast of the dataset have an influence on these differences. Sodium oxybate (Xyrem ), approved by the European Medicines Agency (EMA) for narcolepsy with cataplexy, is only available through risk mitigation programs due to potential adverse effects including respiratory and central nervous system depression, neuropsychiatric events, and misuse. We report findings from a survey evaluating effectiveness of the European Union Xyrem Risk Management Plan (RMP). A cross-sectional, online, multiple-choice survey was distributed to randomly selected healthcare professionals (HCPs) from six European countries (April 2016-May 2018). Eligibility criteria current/potential Xyrem prescriber and/or sleep disorder specialist; contact information available; on the Xyrem RMP educational materials mailing list. proportion of respondents answering each question correctly (< 50% responses correct = unsatisfactory comprehension, 50% to < 70% = satisfactory, ≥ 70% = excellent), with precision assessed using 95% confidence intervals (CIs). Of the 709 HCPs contacted, 60improve understanding of how best to develop educational materials. EUPAS15024. EUPAS15024.Artemisia annua L. has been utilized for the first time in a nanofibrous wound dressing composition. The extract of this valuable plant provides anti-inflammatory, anti-bacterial and anti-microbial properties which can be considered as a promising medicinal component in therapeutic applications. In the present work, Artemisia annua L. was picked up from Gorgan forest area of Northern Iran and its extract was prepared by methanol as the extraction solvent. In the fabrication of wound dressing, Artemisia annua L. extract was mixed with gelatin and a nanofibrous structure was formed by electrospinning technique. To have a wound dressing with acceptable stability and optimum mechanical properties, this biologically active layer was formed on a PCL nanofibrous base layer. The fabricated double-layer wound dressing was analyzed chemically, structurally, mechanically and biologically. ATR-FTIR spectra of the prepared wound dressing contain functional groups of Artemisia annua L. as peroxide groups, etc. SEM micrographs of electrospun gelatin/Artemisia annua L. confirmed the successful electrospinning process for producing Artemisia annua L.-containing nanofibers with mean diameter of 242.00 ± 67.53 nm. In vitro Artemisia annua L. release study of the fabricated wound dressings suggests a sustain release over 7 days for the crosslinked sample. In addition, evaluation of the in vitro structural stability of the prepared wound dressings confirmed the stability of the crosslinked nanofibrous structures in PBS solution environment. Biological study of the Artemisia annua L.-containing wound dressing revealed no cytotoxicity, good proliferation and attachment of the seeded fibroblasts cells and acceptable antibacterial property against Staphylococcus aureus bacteria.