Effect of Asbestos Consumption on Malignant Pleural Mesothelioma in Italy: Forecasts of Mortality up to 2040.

Statistical models used to forecast malignant pleural mesothelioma (MPM) trends often do not take into account historical asbestos consumption, possibly resulting in less accurate predictions of the future MPM death toll. We used the distributed lag non-linear model (DLNM) approach to predict future MPM cases in Italy until 2040, based on past asbestos consumption figures. Analyses were conducted using data on male MPM deaths (1970-2014) and annual asbestos consumption using data on domestic production, importation, and exportation. According to our model, the peak of MPM deaths is expected to occur in 2021 (1122 expected cases), with a subsequent decrease in mortality (344 MPM deaths in 2039). The exposure-response curve shows that relative risk (RR) of MPM increased almost linearly for lower levels of exposure but flattened at higher levels. The lag-specific RR grew until 30 years since exposure and decreased thereafter, suggesting that the most relevant contributions to the risk come from exposures which occurred 20-40 years before death. Our results show that the Italian MPM epidemic is approaching its peak and underline that the association between temporal trends of MPM and time since exposure to asbestos is not monotonic, suggesting a lesser role of remote exposures in the development of MPM than previously assumed

IsoBayes Containers

This folder contains all the singularity containers used to execute the entire analysis reported in "IsoBayes: a Bayesian approach for single-isoform proteomics inference" paper.</p

Forecast of Malignant Peritoneal Mesothelioma Mortality in Italy up to 2040

Despite their differences, pleural and peritoneal mesothelioma are frequently lumped together to describe epidemic curves and to forecast future mesothelioma trends. This study aims to describe the malignant peritoneal mesothelioma (MPeM) epidemic in Italy (1996-2016) and to forecast future trends up to 2040 in order to contribute to the assessment of MPeM future burden. All MPeM deaths in Italy from 1996-2016 were collected (as provided by the Italian National Statistical Institute (ISTAT)) in order to estimate MPeM mortality rates for each 3-year period from 1996 to 2016. Poisson age-period-cohort (APC) models were then used to forecast MPeM future trends. Between 2017 and 2040, 1333 MPeM deaths are expected. The number of MPeM deaths, as well as mortality rates, are expected to constantly decrease throughout the considered period. Based on considering the information from this study, it can be concluded that the MPeM epidemic has probably already reached its peak in Italy

Predicted Effects of Stopping COVID-19 Lockdown on Italian Hospital Demand

Italy has been one of the first countries to implement mitigation measures to curb the COVID-19 pandemic. There is currently a debate on when and how such measures should be loosened.To forecast the demand for hospital ICU and non-ICU beds for COVID-19 patients from May-September, we developed two models, assuming a gradual easing of restrictions or an intermittent lockdown

Investigating How Reproducibility and Geometrical Representation in UMAP Dimensionality Reduction Impact the Stratification of Breast Cancer Tumors

Advances in next-generation sequencing have provided high-dimensional RNA-seq datasets, allowing the stratification of some tumor patients based on their transcriptomic profiles. Machine learning methods have been used to reduce and cluster high-dimensional data. Recently, uniform manifold approximation and projection (UMAP) was applied to project genomic datasets in low-dimensional Euclidean latent space. Here, we evaluated how different representations of the UMAP embedding can impact the analysis of breast cancer (BC) stratification. We projected BC RNA-seq data on Euclidean, spherical, and hyperbolic spaces, and stratified BC patients via clustering algorithms. We also proposed a pipeline to yield more reproducible clustering outputs. The results show how the selection of the latent space can affect downstream stratification results and suggest that the exploration of different geometrical representations is recommended to explore data structure and samples&rsquo; relationships

Investigating How Reproducibility and Geometrical Representation in UMAP Dimensionality Reduction Impact the Stratification of Breast Cancer Tumors

Advances in next-generation sequencing have provided high-dimensional RNA-seq datasets, allowing the stratification of some tumor patients based on their transcriptomic profiles. Machine learning methods have been used to reduce and cluster high-dimensional data. Recently, uniform manifold approximation and projection (UMAP) was applied to project genomic datasets in low-dimensional Euclidean latent space. Here, we evaluated how different representations of the UMAP embedding can impact the analysis of breast cancer (BC) stratification. We projected BC RNA-seq data on Euclidean, spherical, and hyperbolic spaces, and stratified BC patients via clustering algorithms. We also proposed a pipeline to yield more reproducible clustering outputs. The results show how the selection of the latent space can affect downstream stratification results and suggest that the exploration of different geometrical representations is recommended to explore data structure and samples’ relationships