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    Accuracy of Radiomics in Predicting IDH Mutation Status in Diffuse Gliomas: A Bivariate Meta-Analysis

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    Purpose: To perform a systematic review and meta-analysis assessing the predictive accuracy of radiomics in the noninvasive determination of isocitrate dehydrogenase (IDH) status in grade-4 and lower-grade diffuse gliomas. Materials and Methods: A systematic search was performed in the PubMed, Scopus, Embase, Web of Science and Cochrane library databases for relevant articles published between January 1, 2010 and July 7, 2021. Pooled sensitivity and specificity across studies was estimated. Risk of bias was evaluated using Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2), and methodology was evaluated using the radiomics quality score (RQS). Additional subgroup analyses were performed according to tumor grade, RQS, and number of sequences used (PROSPERO ID: CRD42021268958). Results: Twenty-six studies that included a total of 3,280 patients were included for analysis. The pooled sensitivity and specificity of radiomics for the detection of IDH mutation were 79% (95%CI: 76%–83%) and 80% (95%CI: 76%–83%), respectively. Low RQS scores were found overall for the included works. Subgroup analyses showed lower false positive rates in very low RQS studies (RQS < 6) (meta-regression z = −1.9, P = .02) compared with adequate RQS studies. No substantial differences were found in pooled sensitivity/specificity for the pure grade-4 gliomas group compared with the all-grade gliomas group (81%/86% versus 79%/79% respectively) and for studies using single versus multiple sequences (80%/77% versus 79%/82% respectively). Conclusion: The pooled data showed that radiomics achieved good accuracy performance in distinguishing IDH mutation status in patients with grade-4 and lower-grade diffuse gliomas. Overall methodological quality (RQS) is low and introduces potential bias

    Thermomechanical response of liquid crystal elastomers: role of crosslinker density

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    In this work thermomechanical properties of main-chain liquid crystal elastomers (MC-LCEs) with different degrees of crosslinking were investigated, and gradual loss of thermomechanical response was observed on repetitive measurements. Specifically, six samples of MC-LCEs were prepared, with crosslinker-to-mesogen relative concentration ranging from 5% to 10% in steps of 1%. The obtained results were then compared to thermomechanical response of side-chain liquid crystal elastomers (SC-LCEs). Additionally, thermomechanical response of polymer dispersed main-chain liquid crystal elastomers (MC-PDLCEs) was investigated. Results indicate that in MC-LCEs the concentration of crosslinker defines thermomechanical response and affects stability of the system. The loss of thermomechanical response is negligible in the case of crosslinker to mesogen ratio being the smallest, namely in 5% sample, and it is unaffected by glasslike to nematic phase transition. SC-LCEs do not show any sign of such behaviour and remain stable after several cycles of thermomechanical measurements

    Multiscale ecological drivers of Echinococcus multilocularis spatial distribution in wild hosts: A systematic review

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    : Understanding the ecological factors that drive the spatial patterns of parasites transmission is essential to predict their distribution under global change and to direct proactive surveillance efforts. Here, we systematically reviewed the literature to assess the main ecological drivers responsible for the spatial distribution and transmission of the zoonotic cestode Echinococcus multilocularis, the aetiological agent of alveolar echinococcosis, focusing on wild hosts. The 23 retrieved studies suggested that the dispersal of definitive hosts, climatic and biotic factors (distribution of intermediate hosts, composition of host communities) shape continental-scale distribution patterns of E. multilocularis, whereas the relative importance of climate and land cover in driving E. multilocularis distribution at a smaller (country/regional) scale varies with the geographic area considered. At a local scale, two additional factors contribute to determine the distribution of micro-foci of transmission: the trophic relationships between carnivores definitive hosts and small mammals intermediate hosts, and the defecation and marking behaviour of definitive hosts

    Critical Needs for Integrated Surveillance: Wastewater-Based and Clinical Epidemiology in Evolving Scenarios with Lessons Learned from SARS-CoV-2

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    : During the COVID-19 pandemic, wastewater-based epidemiology (WBE) and clinical surveillance have been used as tools for analyzing the circulation of SARS-CoV-2 in the community, but both approaches can be strongly influenced by some sources of variability. From the challenging perspective of integrating environmental and clinical data, we performed a correlation analysis between SARS-CoV-2 concentrations in raw sewage and incident COVID-19 cases in areas served by medium-size wastewater treatment plants (WWTPs) from 2021 to 2023. To this aim, both datasets were adjusted for several sources of variability: WBE data were adjusted for factors including the analytical protocol, sewage flow, and population size, while clinical data adjustments considered the demographic composition of the served population. Then, we addressed the impact on the correlation of differences among sewerage networks and variations in the frequency and type of swab tests due to changes in political and regulatory scenarios. Wastewater and clinical data were significantly correlated when restrictive containment measures and limited movements were in effect (ρ = 0.50) and when COVID-19 cases were confirmed exclusively through molecular testing (ρ = 0.49). Moreover, a positive (although weak) correlation arose for WWTPs located in densely populated areas (ρ = 0.37) and with shorter sewerage lengths (ρ = 0.28). This study provides methodological approaches for interpreting WBE and clinical surveillance data, which could also be useful for other infections. Data adjustments and evaluation of possible sources of bias need to be carefully considered from the perspective of integrated environmental and clinical surveillance of infections

    Physiological Noise: Definition, Estimation, and Characterization in Complex Biomedical Signals

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    Background: Nonlinear physiological systems exhibit complex dynamics driven by intrinsic dynamical noise. In cases where there is no specific knowledge or assumption about system dynamics, such as in physiological systems, it is not possible to formally estimate noise. Aim: We introduce a formal method to estimate the power of dynamical noise, referred to as physiological noise, in a closed form, without specific knowledge of the system dynamics. Methodology: Assuming that noise can be modeled as a sequence of independent, identically distributed (IID) random variables on a probability space, we demonstrate that physiological noise can be estimated through a nonlinear entropy profile. We estimated noise from synthetic maps that included autoregressive, logistic, and Pomeau-Manneville systems under various conditions. Noise estimation is performed on 70 heart rate variability series from healthy and pathological subjects, and 32 electroencephalographic (EEG) healthy series. Results: Our results showed that the proposed model-free method can discern different noise levels without any prior knowledge of the system dynamics. Physiological noise accounts for around 11% of the overall power observed in EEG signals and approximately 32% to 65% of the power related to heartbeat dynamics. Cardiovascular noise increases in pathological conditions compared to healthy dynamics, and cortical brain noise increases during mental arithmetic computations over the prefrontal and occipital regions. Brain noise is differently distributed across cortical regions. Conclusion: Physiological noise is very part neurobiological dynamics and can be measured using the proposed framework in any biomedical series

    The Tourism Area Life Cycle Hypothesis: a Micro-Foundation

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    We provide a simple micro-foundation of the tourism area life cycle hypothesis, based on tourists’ utility maximization. As a result of social interactions among tourists which determine destinations popularity, the market share of visitors which decides to visit a specific destination follows a logistic dynamics, consistent with what predicted by the tourism area life cycle hypothesis. We show that different preference drivers explain the duration of the different tourism area life cycle stages: the net benefit from visiting the destination characterizes the exploration, involvement, and development phases, while social effects associated with destination popularity characterize the phases of consolidation and stagnation Different from previous studies our results hold true independently of whether we focus on the repeating or non-repeating segment of the tourism market. We also provide a calibration of our model to the case of the city of Venice (Italy) showing that it performs well in capturing the evolution of tourism in the historical center of the city over the last 60 years, suggesting that TALC-like dynamics may occur even in the context of cultural and heritage destinations

    The local environment and germline genetic variation predict cancer risk in the UK Biobank prospective cohort

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    Background: There is a growing body of evidence on the effect of the local environment exposure on cancer susceptibility. Nonetheless, several of the associations remain controversial. Moreover, our understanding of the possible interaction between the local environment and the genetic variability is still very limited. Objective: The aim of this study was to clarify the role of the local environment and its possible interplay with genetics on common cancers development.Methods: Using the UK Biobank (UKBB) prospective cohort, we selected 12 local environment exposures: nitrogen oxides, nitrogen dioxides, particulate matter (10 and 2.5 mu m), noise pollution, urban traffic, living distance from the coast, percentage of greenspace, natural environment, water, and domestic garden within 1000 m from the residential coordinates of each participant. All these exposures were tested for association with 17 different types of cancer for a total of 53,270 cases and 302,645 controls. Additionally, a polygenic score (PGS) was computed for each cancer, to test possible gene-environment interactions. Finally, mediation analyses were carried out.Results: Thirty-six statistically significant associations considering multiple testing (p < 2.19 x 10(-4)) were observed. Among the novel associations we observed that individuals living farther from the coast had a higher risk of developing prostate cancer (OR = 1.13, CI95% = 1.06-1.20, P = 1.98 x 10(-4)). This association was partially mediated by physical activity (indirect effect (IE) =-8.48 x 10(-7)) and the time spent outdoor (IE = 9.07 x 10(-6)). All PGSs showed statistically significant associations. Finally, genome-environment interaction analysis showed that local environment and genetic variability affect cancer risk independently.Discussion: Living close to the coast and air pollution were associated with a decreased risk of prostate cancer and skin melanoma, respectively. These findings from the UKBB support the role of the local environment on cancer development, which is independent from genetics and may be mediated by several lifestyle factors

    Passignano dopo la crisi di metà Trecento

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