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Controlling sediment to reduce debris-flow hazard is generally approached using retention basins that can be closed or have an outlet structure, generally an open check dam. They are usually placed in mild slope zones that allow minimal works for the excavation and the foundation of the outlet structure if present. Recently, it has been shown that the detention of sediments can also be achieved in the high-sloping reaches of debris-flow channels using deposition areas, basins that are open on the downstream side. In this work, we propose an approach for controlling the sediment volume transported by debris flows consisting of a cascade of deposition areas and retention basins. We also include a framework for planning, sizing, and checking the works. Two scenarios are considered, corresponding to the maximum values of the debris-flow peak discharge and volume, respectively. Moreover, the presence or absence of boulders is also considered. For this purpose, a method that evaluates the clogging of a single open check dam as a function of the coarse fraction of the sediment volume is simply extended to the case of multiple dams and implemented in a routing model. The proposed approach is applied along Ru Secco Creek in northeast Italy to defend a resort area and a village hit by a high-magnitude debris flow in 2015. After a careful survey and study, a solution with a combination of deposition areas and retention basins is planned and sized. The validity and performance of the proposed solution are analyzed using debris-flow modeling for two scenarios, considering both the absence and presence of boulders. Most of the sediment volume transported by debris flows is trapped, and a small solid discharge flows downstream of the works. [ABSTRACT FROM AUTHOR]

Debris-flow erosion patterns were investigated for two adjacent catchments, Molinara and Val del Lago creeks (Eastern Alps, Trento Province, Italy), where two debris flows were triggered by an intense storm in the summer of 2010. Both basins have been inactive over the last two centuries. The debris flows were activated by channel and bank erosion under stable bed conditions before the event. The erosive process was analysed by combining a field campaign (two hundred cross sections were surveyed along the creeks) and pre- and post-event LiDAR surveys. Data were analysed by selecting morphologically-homogenous channel reaches and deriving for each reach: erosion depth, creek width, eroded volume and peak discharge. Investigating the frequency distribution of the erosion depth we found out that it follows an EV1 probability distribution. On this basis, a new approach has been proposed to predict event volumes when the expected maximum potential depth erosion is known. The procedure would be of high interest in predicting debris flow volume in mountain channels characterized by long silent periods.

Purpose: We aimed to assess the role of lung ultrasound (LUS) in the diagnosis and prognosis of SARS-CoV-2 pneumonia, by comparing it with High Resolution Computed Tomography (HRCT). Patients and methods: All consecutive patients with laboratory-confirmed SARS-CoV-2 infection and hospitalized in COVID Centers were enrolled. LUS and HRCT were carried out on all patients by expert operators within 48–72 h of admission. A four-level scoring system computed in 12 regions of the chest was used to categorize the ultrasound imaging, from 0 (absence of visible alterations with ultrasound) to 3 (large consolidation and cobbled pleural line). Likewise, a semi-quantitative scoring system was used for HRCT to estimate pulmonary involvement, from 0 (no involvement) to 5 (>75% involvement for each lobe). The total CT score was the sum of the individual lobar scores and ranged from 0 to 25. LUS scans were evaluated according to a dedicated scoring system. CT scans were assessed for typical findings of COVID-19 pneumonia (bilateral, multi-lobar lung infiltration, posterior peripheral ground glass opacities). Oxygen requirement and mortality were also recorded. Results: Ninety-nine patients were included in the study (male 68.7%, median age 71). 40.4% of patients required a Venturi mask and 25.3% required non-invasive ventilation (C-PAP/Bi-level). The overall mortality rate was 21.2% (median hospitalization 30 days). The median ultrasound thoracic score was 28 (IQR 20–36). For the CT evaluation, the mean score was 12.63 (SD 5.72), with most of the patients having LUS scores of 2 (59.6%). The bivariate correlation analysis displayed statistically significant and high positive correlations between both the CT and composite LUS scores and ventilation, lactates, COVID-19 phenotype, tachycardia, dyspnea, and mortality. Moreover, the most relevant and clinically important inverse proportionality in terms of P/F, i.e., a decrease in P/F levels, was indicative of higher LUS/CT scores. Inverse proportionality P/F levels and LUS and TC scores were evaluated by univariate analysis, with a P/F–TC score correlation coefficient of −0.762, p < 0.001, and a P/F–LUS score correlation coefficient of −0.689, p < 0.001. Conclusions: LUS and HRCT show a synergistic role in the diagnosis and disease severity evaluation of COVID-19.

Background: In the past two decades, three coronavirus epidemics have been reported. Coronavirus disease 2019 (COVID-19) is caused by a severe acute respiratory syndrome (SARS)-like coronavirus (SARS-CoV-2). In most patients, the disease is characterized by interstitial pneumonia, but features can affect other organs. Purpose: To document the radiological features of the patients and to perform a narrative review of the literature. Material and Methods: We conducted a retrospective, single-center study on 1060 consecutive hospitalized patients with COVID-19 at our institution. According to the inclusion criteria, we selected patients to be studied in more radiological detail. All images were obtained as per standard of care protocols. We performed a statistic analysis to describe radiological features. We then presented a systematic review of the main and conventional neuroimaging findings in COVID-19. Results: Of 1060 patients hospitalized for COVID-19 disease, 15% (159) met the eligibility criteria. Of these, 16 (10%) did not undergo radiological examinations for various reasons, while 143 (90%) were examined. Of these 143 patients, 48 (33.6%) had positive neuroimaging. We found that the most frequent pathology was acute ischemic stroke (n=16, 33.3%). Much less frequent were Guillain–Barre syndrome (n=9, 18.8%), cerebral venous thrombosis (n=7, 14.6%), encephalitis or myelitis (n=6, 12.5%), intracranial hemorrhage and posterior hemorrhagic encephalopathy syndrome (n=4, 8.3%), exacerbation of multiple sclerosis (n=4, 8.3%), and Miller–Fisher syndrome (n=2, 4.2%). Conclusion: Our data are coherent with the published literature. Knowledge of these patterns will make clinicians consider COVID-19 infection when unexplained neurological findings are encountered. [ABSTRACT FROM AUTHOR]

(1) The aim of our study is to evaluate the capacity of the Visually AcceSAble Rembrandt Images (VASARI) scoring system in discerning between the different degrees of glioma and Isocitrate Dehydrogenase (IDH) status predictions, with a possible application in machine learning. (2) A retrospective study was conducted on 126 patients with gliomas (M/F = 75/51; mean age: 55.30), from which we obtained their histological grade and molecular status. Each patient was analyzed with all 25 features of VASARI, blinded by two residents and three neuroradiologists. The interobserver agreement was assessed. A statistical analysis was conducted to evaluate the distribution of the observations using a box plot and a bar plot. We then performed univariate and multivariate logistic regressions and a Wald test. We also calculated the odds ratios and confidence intervals for each variable and the evaluation matrices with receiver operating characteristic (ROC) curves in order to identify cut-off values that are predictive of a diagnosis. Finally, we did the Pearson correlation test to see if the variables grade and IDH were correlated. (3) An excellent ICC estimate was obtained. For the grade and IDH status prediction, there were statistically significant results by evaluation of the degree of post-contrast impregnation (F4) and the percentage of impregnated area (F5), not impregnated area (F6), and necrotic (F7) tissue. These models showed good performances according to the area under the curve (AUC) values (>70%). (4) Specific MRI features can be used to predict the grade and IDH status of gliomas, with important prognostic implications. The standardization and improvement of these data (aim: AUC > 80%) can be used for programming machine learning software. [ABSTRACT FROM AUTHOR]