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The oral and gastrointestinal mucosae represent the main targets of the toxic effect of chemo and/or radiotherapy administered during the conditioning regimen before hematopoietic stem cell transplant (HSCT). These harmful consequences and the immunological complications that may occur after the transplant (such as Graft versus Host Disease, GvHD) are responsible for the clinical symptoms associated with mucositis during the aplasia phase, like pain, nausea, vomiting, and diarrhea. These toxicities could play a critical role in the oral and gastrointestinal microbiomes during the post-transplant phase, and the degree of microbial dysbiosis and dysregulation among different bacterial species could also be crucial in intestinal mucosa homeostasis, altering the host's innate and adaptive immune responses and favoring abnormal immune responses responsible for the occurrence of GvHD. This prospective pediatric study aims to analyze longitudinally oral and gut microbiomes in 17 pediatric patients who received allogeneic HSCT for malignant and non-malignant diseases. The oral mucositis was mainly associated with an increased relative abundance of Fusobacteria, and Prevotella species, while Streptococcus descendants showed a negative correlation. The fecal microbiome of subjects affected by cutaneous acute GvHD (aGvHD) correlated with Proteobacteria. Oral mucosal microbiota undergoes changes after HSCT, Fusobacteria, and Prevotella represent bacterial species associated with mucositis and they could be the target for future therapeutic approaches, while fecal microbiome in patients with acute GvHD (aGvHD) revealed an increase of different class of Proteobacteria (Alphaproteobacteria and Deltaproteobacteria) and a negative correlation with the class of Gammaproteobacteria., (© 2024. The Author(s).)

Widespread uptake of vaccines is necessary to achieve herd immunity. However, uptake rates have varied across U.S. states during the first six months of the COVID-19 vaccination program. Misbeliefs may play an important role in vaccine hesitancy, and there is a need to understand relationships between misinformation, beliefs, behaviors, and health outcomes. Here we investigate the extent to which COVID-19 vaccination rates and vaccine hesitancy are associated with levels of online misinformation about vaccines. We also look for evidence of directionality from online misinformation to vaccine hesitancy. We find a negative relationship between misinformation and vaccination uptake rates. Online misinformation is also correlated with vaccine hesitancy rates taken from survey data. Associations between vaccine outcomes and misinformation remain significant when accounting for political as well as demographic and socioeconomic factors. While vaccine hesitancy is strongly associated with Republican vote share, we observe that the effect of online misinformation on hesitancy is strongest across Democratic rather than Republican counties. Granger causality analysis shows evidence for a directional relationship from online misinformation to vaccine hesitancy. Our results support a need for interventions that address misbeliefs, allowing individuals to make better-informed health decisions., (© 2022. The Author(s).)

Lockdowns implemented to address the COVID-19 pandemic have disrupted human mobility flows around the globe to an unprecedented extent and with economic consequences which are unevenly distributed across territories, firms and individuals. Here we study socioeconomic determinants of mobility disruption during both the lockdown and the recovery phases in Italy. For this purpose, we analyze a massive data set on Italian mobility from February to October 2020 and we combine it with detailed data on pre-existing local socioeconomic features of Italian administrative units. Using a set of unsupervised and supervised learning techniques, we reliably show that the least and the most affected areas persistently belong to two different clusters. Notably, the former cluster features significantly higher income per capita and lower income inequality than the latter. This distinction persists once the lockdown is lifted. The least affected areas display a swift (V-shaped) recovery in mobility patterns, while poorer, most affected areas experience a much slower (U-shaped) recovery: as of October 2020, their mobility was still significantly lower than pre-lockdown levels. These results are then detailed and confirmed with a quantile regression analysis. Our findings show that economic segregation has, thus, strengthened during the pandemic., (© 2021. The Author(s).)

The COVID-19 pandemic is one of the defining events of our time. National Governments responded to the global crisis by implementing mobility restrictions to slow down the spread of the virus. To assess the impact of those policies on human mobility, we perform a massive comparative analysis on geolocalized data from 13 M Facebook users in France, Italy, and the UK. We find that lockdown generally affects national mobility efficiency and smallworldness-i.e., a substantial reduction of long-range connections in favor of local paths. The impact, however, differs among nations according to their mobility infrastructure. We find that mobility is more concentrated in France and UK and more distributed in Italy. In this paper we provide a framework to quantify the substantial impact of the mobility restrictions. We introduce a percolation model mimicking mobility network disruption and find that node persistence in the percolation process is significantly correlated with the economic and demographic characteristics of countries: areas showing higher resilience to mobility disruptions are those where Value Added per Capita and Population Density are high. Our methods and findings provide important insights to enhance preparedness for global critical events and to incorporate resilience as a relevant dimension to estimate the socio-economic consequences of mobility restriction policies.

The spread of SARS-COV-2 has affected many economic and social systems. This paper aims at estimating the impact on regional productive systems in Italy of the interplay between the epidemic and the mobility restriction measures put in place to contain the contagion. We focus then on the economic consequences of alternative lockdown lifting schemes. We leverage a massive dataset of human mobility which describes daily movements of over four million individuals in Italy and we model the epidemic spreading through a metapopulation SIR model, which provides the fraction of infected individuals in each Italian district. To quantify economic backslashes this information is combined with socio-economic data. We then carry out a scenario analysis to model the transition to a post-lockdown phase and analyze the economic outcomes derived from the interplay between (a) the timing and intensity of the release of mobility restrictions and (b) the corresponding scenarios on the severity of virus transmission rates. Using a simple model for the spreading disease and parsimonious assumptions on the relationship between the infection and the associated economic backlashes, we show how different policy schemes tend to induce heterogeneous distributions of losses at the regional level depending on mobility restrictions. Our work shed lights on how recovery policies need to balance the interplay between mobility flows of disposable workers and the diffusion of contagion.

Aplastic anemia (AA) is a serious hematological disorder, which is solely cured by hematopoietic stem cell transplantation (HSCT). Haploidentical HSCT is an emerging modality with encouraging outcomes in several blood conditions. The present study aims to comprehensively assess the feasibility and safety of haploidentical HSCT in patients with severe and very severe AA. It is a systematic review and meta-analysis of studies related to haploidentical stem cell transplantation in idiopathic AA investigating rates of successful engraftment, acute graft-versus-host disease (aGvHD), chronic GvHD (cGvHD), transplant-related mortality (TRM), and posttransplantation viral infections (including cytomegalovirus [CMV]) in patients with AA. The effects of reduced-intensity conditioning (RIC) and nonmyeloablative conditioning (NMA), as well as various GvHD prophylaxis regimens on these outcomes were evaluated. In total 15 studies were identified, (577 patients, 58.9% males), successful engraftment was observed in 97.3% of patients (95% CI, 95.9-98.7) while grades II-IV aGvHD and cGvHD were reported in 26.6% and 25.0%, respectively. The pooled incidence of TRM was 6.7% per year (95% CI, 4.0-9.4). RIC regimens were associated with higher proportions of successful engraftment (97.7% vs 91.7%, P = 0.03) and aGvHD (29.5% vs 18.7%, P = 0.008) when compared with NMA regimens with no differences in cGvHD or mortality incidence. When compared with methotrexate-containing regimens and other regimens, posttransplant cyclophosphamide-containing regimens reduced the rates of aGvHD (28.6%, 27.8%, and 12.8%, respectively, P = 0.02), CMV viremia (55.7%, 38.6%, and 10.4%, respectively, P < 0.001), and CMV disease in initially viremic patients (2.1%, 33.0%, and 0%, respectively, P < 0.001). We have concluded that Haploidentical HSCT was associated with promising outcomes in terms of successful engraftment and reduced complications. Future prospective trials are needed to identify the preferred conditioning regimen, GvHD prophylaxis, and graft source in the setting of haploidentical transplant for AA.

In recent years, malicious information had an explosive growth in social media, with serious social and political backlashes. Recent important studies, featuring large-scale analyses, have produced deeper knowledge about this phenomenon, showing that misleading information spreads faster, deeper and more broadly than factual information on social media, where echo chambers, algorithmic and human biases play an important role in diffusion networks. Following these directions, we explore the possibility of classifying news articles circulating on social media based exclusively on a topological analysis of their diffusion networks. To this aim we collected a large dataset of diffusion networks on Twitter pertaining to news articles published on two distinct classes of sources, namely outlets that convey mainstream, reliable and objective information and those that fabricate and disseminate various kinds of misleading articles, including false news intended to harm, satire intended to make people laugh, click-bait news that may be entirely factual or rumors that are unproven. We carried out an extensive comparison of these networks using several alignment-free approaches including basic network properties, centrality measures distributions, and network distances. We accordingly evaluated to what extent these techniques allow to discriminate between the networks associated to the aforementioned news domains. Our results highlight that the communities of users spreading mainstream news, compared to those sharing misleading news, tend to shape diffusion networks with subtle yet systematic differences which might be effectively employed to identify misleading and harmful information.