The relationship between changes in the Economic Sentiment Indicator and real GDP growth: a time-varying coefficient approach

The aim of this paper is to capture the time-varying effects of the relationship between changes in the Economic Sentiment Indicator (ESI) and economic growth. We use penalized regression splines to estimate the different point effects over time. Evidence from six European countries supports the idea that the elasticity of the ESI is time-varying.Economic Sentiment Indicator; Real GDP Growth; Thin Plate Regression Splines; Time-Varying Coefficient Model

Impacts of extreme weather events on mortgage risks and their evolution under climate change:A case study on Florida

International audienceWe develop an additive Cox proportional hazard model with time-varying covariates, including spatio-temporal characteristics of weather events, to study the impact of weather extremes (heavy rains and tropical cyclones) on the probability of mortgage default and prepayment. We compare the survival model with a flexible logistic model and an extreme gradient boosting algorithm. We estimate the models on a portfolio of mortgages in Florida, consisting of 69,046 loans and 3,707,831 loan-month observations with localization data at the five-digit ZIP code level. We find a statistically significant and non-linear impact of tropical cyclone intensity on default as well as a significant impact of heavy rains in areas with large exposure to flood risks. These findings confirm existing results in the literature and also provide estimates of the impact of the extreme event characteristics on mortgage risk, e.g. the impact of tropical cyclones on default more than doubles in magnitude when moving from a hurricane of category two to a hurricane of category three or more. We build on the identified effect of exposure to flood risk (in interaction with heavy rainfall) on mortgage default to perform a scenario analysis of the future impacts of climate change using the First Street flood model, which provides projections of exposure to floods in 2050 under RCP 4.5. We find a systematic increase in risk under climate change that can vary based on the scenario of extreme events considered. Climate-adjusted credit risk allows risk managers to better evaluate the impact of climate-related risks on mortgage portfolios

Endoscopic evacuation of massive intraventricular hemorrhages reduces shunt dependency: a meta-analysis

Intraventricular hemorrhage (IVH) is characterized by severe prognosis. The amount of intraventricular blood is the most important, disease-specific, prognostic factor, as acute complications are strictly dependent on clot formation. Although external ventricular drain (EVD) placement is the standard treatment, in the past 15 years neuroendoscopic (NE) evacuation of IVH has been advocated, but available comparative data are limited. A systematic review of the literature was performed according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Included articles compare the treatment of primary and secondary IVH with NE and EVD. The meta-analysis was performed in terms of shunt dependency. Cochran's Q-test and I2 statistics were used to assess heterogeneity in the studies. No heterogeneity was considered for p greater than 0.05 and I2 less than 20%. A random-effect model was used, with restricted maximum likelihood to estimate the heterogeneity variance. After screening 744 articles, 5 were included in the meta-analysis. A total of 303 patients presenting with primary or pure (50 patients) and secondary (253 patients) IVH, undergoing either NE (151) or EVD (152), were included in the metanalysis. The risk of ventriculoperitoneal (VP) shunt was higher in the EVD group (relative risk: 1.93, 95% confidence interval: 1.28-2.92, p  = 0.0094). The risk of VP shunt was higher in the EVD group, but the overall outcome remains poor for patients with IVH, with a moderate-to-high disability. Large randomized controlled trials are needed to evaluate more deeper both advantages and effects on the outcome of NE over EVD

The impact of COVID-19 pandemic on surgical neuro-oncology: A survey from the Italian society of neurosurgery (SINch)

Background: The COVID-19 pandemic and its impact on hospitals' activity and organization has imposed a vast change in standard neurosurgical oncology practice to accommodate for shifting resources. Aims: This investigation aims to analyse the nationwide capability in reorganizing the surgical neuro-oncological activity during the COVID-19 pandemic to evaluate whether COVID-19-pandemic influenced the surgical management in these patients. Method: A web-based dataset model organized by the Italian Neurosurgical Society (SINCh) was sent to all the Italian neurosurgical departments in May 2021, requesting to report the types and numbers of surgical procedures performed in the pre-pandemic period (from March 9th 2019 to March 9th 2020) compared to the pandemic period (from March 10th 2020 to March 10th 2021). Results: This multicentre investigation included the surgical activity of 35 Italian Neurosurgical Departments in a pre-pandemic year versus a pandemic year. During the COVID period, 699 fewer neuro-oncological patients were operated on than in the pre-COVID period. We noted a slight increase in urgency and a more severe decrease in elective and benign pathology. None of these differences was statistically significant. Surgically treated patients who tested positive for SARS-CoV-2 were 36, of which 11 died. Death was found to be COVID-related only in 2 cases. Conclusion: The reorganization of the Italian Neurosurgical Departments was able to guarantee a redistribution of the CNS tumors during the inter-pandemic periods, demonstrating that patients even in the pandemic era could be treated without compromising the efficacy and safety of the surgical procedure

Mechanical and Biological Characterization of PMMA/Al2O3 Composites for Dental Implant Abutments

The mechanical and biological behaviors of PMMA/Al2O3 composites incorporating 30 wt.%, 40 wt.%, and 50 wt.% of Al2O3 were thoroughly characterized as regards to their possible application in implant-supported prostheses. The Al2O3 particles accounted for an increase in the flexural modulus of PMMA. The highest value was recorded for the composite containing 40 wt.% Al2O3 (4.50 GPa), which was about 18% higher than that of its unfilled counterpart (3.86 GPa). The Al2O3 particles caused a decrease in the flexural strength of the composites, due to the presence of filler aggregates and voids, though it was still satisfactory for the intended application. The roughness (Ra) and water contact angle had the same trend, ranging from 1.94 microns and 77.2° for unfilled PMMA to 2.45 microns and 105.8° for the composite containing the highest alumina loading, respectively, hence influencing both the protein adsorption and cell adhesion. No cytotoxic effects were found, confirming that all the specimens are biocompatible and capable of sustaining cell growth and proliferation, without remarkable differences at 24 and 48 h. Finally, Al2O3 was able to cause strong cell responses (cell orientation), thus guiding the tissue formation in contact with the composite itself and not enhancing its osteoconductive properties, supporting the PMMA composite’s usage in the envisaged application

Modelling the impact of women’s education on fertility in Malawi

Many studies have suggested that there is an inverse relationship between education and number of children among women from sub-Saharan Africa countries, including Malawi. However, a crucial limitation of these analyses is that they do not control for the potential endogeneity of education. The aim of our study is to estimate the role of women’s education on their number of children in Malawi, accounting for the possible presence of endogeneity and for nonlinear effects of continuous observed confounders. Our analysis is based on micro data from the 2010 Malawi Demographic Health Survey, and uses a flexible instrumental variable regression approach. The results suggest that the relationship of interest is affected by endogeneity and exhibits an inverted U-shape among women living in rural areas of Malawi, whereas it exhibits an inverse (nonlinear) relationship for women living in urban areas

Effects of pre-operative isolation on postoperative pulmonary complications after elective surgery: an international prospective cohort study

We aimed to determine the impact of pre-operative isolation on postoperative pulmonary complications after elective surgery during the global SARS-CoV-2 pandemic. We performed an international prospective cohort study including patients undergoing elective surgery in October 2020. Isolation was defined as the period before surgery during which patients did not leave their house or receive visitors from outside their household. The primary outcome was postoperative pulmonary complications, adjusted in multivariable models for measured confounders. Pre-defined sub-group analyses were performed for the primary outcome. A total of 96,454 patients from 114 countries were included and overall, 26,948 (27.9%) patients isolated before surgery. Postoperative pulmonary complications were recorded in 1947 (2.0%) patients of which 227 (11.7%) were associated with SARS-CoV-2 infection. Patients who isolated pre-operatively were older, had more respiratory comorbidities and were more commonly from areas of high SARS-CoV-2 incidence and high-income countries. Although the overall rates of postoperative pulmonary complications were similar in those that isolated and those that did not (2.1% vs 2.0%, respectively), isolation was associated with higher rates of postoperative pulmonary complications after adjustment (adjusted OR 1.20, 95%CI 1.05-1.36, p = 0.005). Sensitivity analyses revealed no further differences when patients were categorised by: pre-operative testing; use of COVID-19-free pathways; or community SARS-CoV-2 prevalence. The rate of postoperative pulmonary complications increased with periods of isolation longer than 3 days, with an OR (95%CI) at 4-7 days or >= 8 days of 1.25 (1.04-1.48), p = 0.015 and 1.31 (1.11-1.55), p = 0.001, respectively. Isolation before elective surgery might be associated with a small but clinically important increased risk of postoperative pulmonary complications. Longer periods of isolation showed no reduction in the risk of postoperative pulmonary complications. These findings have significant implications for global provision of elective surgical care

Upper limb function in Duchenne muscular dystrophy: 24 month longitudinal data

The aim of the study was to establish 24 month changes in upper limb function using a revised version of the performance of upper limb test (PUL 2.0) in a large cohort of ambulant and non-ambulant boys with Duchenne muscular dystrophy and to identify possible trajectories of progression. Of the 187 patients studied, 87 were ambulant (age range: 7\u201315.8 years), and 90 non-ambulant (age range: 9.08\u201324.78). The total scores changed significantly over time (p<0.001). Non-ambulant patients had lower total scores at baseline (mean 19.7) when compared to the ambulant ones (mean 38.4). They also had also a bigger decrease in total scores over 24 months compared to the ambulant boys (4.36 vs 2.07 points). Multivariate model analysis showed that the Performance of Upper Limb changes reflected the entry level and ambulation status, that were independently associated to the slope of Performance of Upper Limb changes. This information will be of help both in clinical practice and at the time of designing clinical trials

PKC Theta Ablation Improves Healing in a Mouse Model of Muscular Dystrophy

Inflammation is a key pathological characteristic of dystrophic muscle lesion formation, limiting muscle regeneration and resulting in fibrotic and fatty tissue replacement of muscle, which exacerbates the wasting process in dystrophic muscles. Limiting immune response is thus one of the therapeutic options to improve healing, as well as to improve the efficacy of gene- or cell-mediated strategies to restore dystrophin expression. Protein kinase C θ (PKCθ) is a member of the PKCs family highly expressed in both immune cells and skeletal muscle; given its crucial role in adaptive, but also innate, immunity, it is being proposed as a valuable pharmacological target for immune disorders. In our study we asked whether targeting PKCθ could represent a valuable approach to efficiently prevent inflammatory response and disease progression in a mouse model of muscular dystrophy. We generated the bi-genetic mouse model mdx/θ−/−, where PKCθ expression is lacking in mdx mice, the mouse model of Duchenne muscular dystrophy. We found that muscle wasting in mdx/θ−/− mice was greatly prevented, while muscle regeneration, maintenance and performance was significantly improved, as compared to mdx mice. This phenotype was associated to reduction in inflammatory infiltrate, pro-inflammatory gene expression and pro-fibrotic markers activity, as compared to mdx mice. Moreover, BM transplantation experiments demonstrated that the phenotype observed was primarily dependent on lack of PKCθ expression in hematopoietic cells

Gammapy: A Python package for gamma-ray astronomy

In this article, we present Gammapy, an open-source Python package for the analysis of astronomical $\gamma$-ray data, and illustrate the functionalities of its first long-term-support release, version 1.0. Built on the modern Python scientific ecosystem, Gammapy provides a uniform platform for reducing and modeling data from different $\gamma$-ray instruments for many analysis scenarios. Gammapy complies with several well-established data conventions in high-energy astrophysics, providing serialized data products that are interoperable with other software packages. Starting from event lists and instrument response functions, Gammapy provides functionalities to reduce these data by binning them in energy and sky coordinates. Several techniques for background estimation are implemented in the package to handle the residual hadronic background affecting $\gamma$-ray instruments. After the data are binned, the flux and morphology of one or more $\gamma$-ray sources can be estimated using Poisson maximum likelihood fitting and assuming a variety of spectral, temporal, and spatial models. Estimation of flux points, likelihood profiles, and light curves is also supported. After describing the structure of the package, we show, using publicly available $\gamma$-ray data, the capabilities of Gammapy in multiple traditional and novel $\gamma$-ray analysis scenarios, such as spectral and spectro-morphological modeling and estimations of a spectral energy distribution and a light curve. Its flexibility and power are displayed in a final multi-instrument example, where datasets from different instruments, at different stages of data reduction, are simultaneously fitted with an astrophysical flux model.Comment: 26 pages, 16 figure