Search

Injection drug use represents an important contributor to hepatitis C virus (HCV) transmission, hence therapeutic communities (TCs) are promising points of care for the identification and treatment of HCV-infected persons who inject drugs (PWIDs). We evaluated the effectiveness and efficacy of an HCV micro-elimination program targeting PWIDs in the context of a drug-free TC; we applied the cascade of care (CoC) evaluation by calculating frequencies of infection diagnosis, confirmation, treatment and achievement of a sustained virological response (SVR). We also evaluated the risk of reinfection of PWIDs achieving HCV eradication by collecting follow-up virologic information of previously recovered individuals and eventual relapse in drug use, assuming the latter as a potential source of reinfection. We considered 811 PWIDs (aged 18+ years) residing in San Patrignano TC at the beginning of the observation period (January 2018-March 2022) or admitted thereafter, assessing for HCV and HIV serology and viral load by standard laboratory procedures. Ongoing infections were treated with direct-acting antivirals (DAA), according to the current national guidelines. Out of the 792 individuals tested on admission, 503 (63.5%) were found to be seropositive for antibodies against HCV. A total of 481 of these 503 individuals (95.6%) underwent HCV RNA testing. Out of the 331 participants positive for HCV RNA, 225 were ultimately prescribed a DAA treatment with a sustained viral response (SVR), which was achieved by 222 PWIDs (98.7%). Of the 222 PWIDs, 186 (83.8%) with SVR remained HCV-free on follow-up (with a median follow-up of 2.73 years after SVR ascertainment). The CoC model in our TC proved efficient in implementing HCV micro-elimination, as well as in preventing reinfection and promoting retention in the care of individuals, which aligns with the therapeutic goals of addiction treatment.

Background: Since February 2020, the COVID-19 epidemic has rapidly spread throughout Italy. Some studies showed an association of environmental factors, such as PM 10 , PM 2.5, NO 2 , temperature, relative humidity, wind speed, solar radiation and mobility with the spread of the epidemic. In this work, we aimed to predict via Deep Learning the real-time transmission of SARS-CoV-2 in the province of Reggio Emilia, Northern Italy, in a grid with a small resolution (12 km × 12 km), including satellite information., Methods: We focused on the Province of Reggio Emilia, which was severely hit by the first wave of the epidemic. The outcomes included new SARS-CoV-2 infections and COVID-19 hospital admissions. Pollution, meteorological and mobility data were analyzed. The spatial simulation domain included the Province of Reggio Emilia in a grid of 40 cells of (12 km) 2 . We implemented a ConvLSTM, which is a spatio-temporal deep learning approach, to perform a 7-day moving average to forecast the 7th day after. We used as training and validation set the new daily infections and hospital admissions from August 2020 to March 2021. Finally, we assessed the models in terms of Mean Absolute Error (MAE) compared with Mean Observed Value (MOV) and Root Mean Squared Error (RMSE) on data from April to September 2021. We tested the performance of different combinations of input variables to find the best forecast model., Findings: Daily new cases of infection, mobility and wind speed resulted in being strongly predictive of new COVID-19 hospital admissions (MAE = 2.72 in the Province of Reggio Emilia; MAE = 0.62 in Reggio Emilia city), whereas daily new cases, mobility, solar radiation and PM 2.5 turned out to be the best predictors to forecast new infections, with appropriate time lags., Interpretation: ConvLSTM achieved good performances in forecasting new SARS-CoV-2 infections and new COVID-19 hospital admissions. The spatio-temporal representation allows borrowing strength from data neighboring to forecast at the level of the square cell (12 km) 2 , getting accurate predictions also at the county level, which is paramount to help optimise the real-time allocation of health care resources during an epidemic emergency., (Copyright © 2022 Elsevier Inc. All rights reserved.)

When designing air pollution reduction policies, regional decision makers face a limited budget to choose the most efficient measures which will have impacts on several pollutants in different ways. RIAT+ is a regional integrated assessment tool that supports the policy maker in this selection of the optimal emission reduction technologies, to improve air quality at minimum costs. In this paper, this tool is formalized and applied to the specific case of a French region (Alsace), to illustrate how focusing on one single pollutant may exacerbate problems related to other pollutants, on top of conflicts related to budget allocation. In our case, results are shown for possible trade-offs between NO2 and O3 control policies. The paper suggests an approach to prioritize policy maker objectives when planning air pollution policies at regional scale., (Copyright © 2014 Elsevier B.V. All rights reserved.)

A case of an immunocompetent man with acute CMV infection associated with a pulmonary embolism is described. Acute CMV infection could be a risk factor for developing thromboembolism. Pulmonary embolism should be included in differential diagnosis in patients with acute CMV infections and pulmonary opacities.