IsoBayes: a Bayesian approach for single-isoform proteomics inference.

Studying protein isoforms is an essential step in biomedical research; at present, the main approach for analyzing proteins is via bottom-up mass spectrometry proteomics, which return peptide identifications, that are indirectly used to infer the presence of protein isoforms. However, the detection and quantification processes are noisy; in particular, peptides may be erroneously detected, and most peptides, known as shared peptides, are associated to multiple protein isoforms. As a consequence, studying individual protein isoforms is challenging, and inferred protein results are often abstracted to the gene-level or to groups of protein isoforms. Here, we introduce IsoBayes, a novel statistical method to perform inference at the isoform level. Our method enhances the information available, by integrating mass spectrometry proteomics and transcriptomics data in a Bayesian probabilistic framework. To account for the uncertainty in the measurement process, we propose a two-layer latent variable approach: first, we sample if a peptide has been correctly detected (or, alternatively filter peptides); second, we allocate the abundance of such selected peptides across the protein(s) they are compatible with. This enables us, starting from peptide-level data, to recover protein-level data; in particular, we: i) infer the presence/absence of each protein isoform (via a posterior probability), ii) estimate its abundance (and credible interval), and iii) target isoforms where transcript and protein relative abundances significantly differ. We benchmarked our approach in simulations, and in two multi-protease real datasets: our method displays good sensitivity and specificity when detecting protein isoforms, its estimated abundances highly correlate with the ground truth, and can detect changes between protein and transcript relative abundances. IsoBayes is freely distributed as a Bioconductor R package, and is accompanied by an example usage vignette.

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.

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.

Excess Deaths and Hospital Admissions for COVID-19 Due to a Late Implementation of the Lockdown in Italy.

In Italy, the COVID-19 epidemic curve started to flatten when the health system had already exceeded its capacity, raising concerns that the lockdown was indeed delayed. The aim of this study was to evaluate the health effects of late implementation of the lockdown in Italy. Using national data on the daily number of COVID-19 cases, we first estimated the effect of the lockdown, employing an interrupted time series analysis. Second, we evaluated the effect of an early lockdown on the trend of new cases, creating a counterfactual scenario where the intervention was implemented one week in advance. We then predicted the corresponding number of intensive care unit (ICU) admissions, non-ICU admissions, and deaths. Finally, we compared results under the actual and counterfactual scenarios. An early implementation of the lockdown would have avoided about 126,000 COVID-19 cases, 54,700 non-ICU admissions, 15,600 ICU admissions, and 12,800 deaths, corresponding to 60% (95%CI: 55% to 64%), 52% (95%CI: 46% to 57%), 48% (95%CI: 42% to 53%), and 44% (95%CI: 38% to 50%) reduction, respectively. We found that the late implementation of the lockdown in Italy was responsible for a substantial proportion of hospital admissions and deaths associated with the COVID-19 pandemic.

Prone Positioning in Non-Intubated Patients With COVID-19 Outside of the Intensive Care Unit: More Evidence Needed.

The coronavirus disease (COVID-19) pandemic has brought the Italian National Health System to its knees. The abnormally high influx of patients, together with the limited resources available, has forced clinicians to make unprecedented decisions and provide compassionate treatments for which little or no evidence is yet available. This is the case for the use of noninvasive positive pressure ventilation and continuous airway pressure ventilation, combined with prone position in patients with COVID-19 and acute respiratory distress syndrome treated outside of intensive care units. In our article, we comment on the evidence available, so far, and provide a brief summary of data collected at our health institution in Piedmont, Italy.

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

OBJECTIVES: Italy has been one of the first countries to implement mitigation measures to curb the coronavirus disease 2019 (COVID-19) pandemic. There is currently a debate on when and how such measures should be loosened. To forecast the demand for hospital intensive care unit (ICU) and non-ICU beds for COVID-19 patients from May to September, we developed 2 models, assuming a gradual easing of restrictions or an intermittent lockdown. METHODS: We used a compartmental model to evaluate 2 scenarios: (A) an intermittent lockdown; (B) a gradual relaxation of the lockdown. Predicted ICU and non-ICU demand was compared with the peak in hospital bed use observed in April 2020. RESULTS: Under scenario A, while ICU demand will remain below the peak, the number of non-ICU will substantially rise and will exceed it (133%; 95% confidence interval [CI]: 94-171). Under scenario B, a rise in ICU and non-ICU demand will start in July and will progressively increase over the summer 2020, reaching 95% (95% CI: 71-121) and 237% (95% CI: 191-282) of the April peak. CONCLUSIONS: Italian hospital demand is likely to remain high in the next months. If restrictions are reduced, planning for the next several months should consider an increase in health-care resources to maintain surge capacity across the country.

Predictions of Mortality from Pleural Mesothelioma in Italy After the Ban of Asbestos Use.

Even if the epidemic of malignant pleural mesothelioma (MPM) is still far from being over worldwide, the health effects of regulations banning asbestos can be evaluated in the countries that implemented them early. Estimates of MPM future burden can be useful to inform and support the implementation of anti-asbestos health policies all around the world. With this aim we described the trends of MPM deaths in Italy (1970-2014) and predicted the future number of cases in both sexes (2015-2039), with consideration of the national asbestos ban that was issued in 1992. The Italian National Statistical Institute (ISTAT) provided MPM mortality figures. Cases ranging from 25 to 89 years of age were included in the analysis. For each five-year period from 1970 to 2014, mortality rates were calculated and age-period-cohort Poisson models were used to predict future burden of MPM cases until 2039. During the period 1970-2014 a total number of 28,907 MPM deaths were observed. MPM deaths increased constantly over the study period, ranging from 1356 cases in 1970-1974 to 5844 cases in 2010-2014. The peak of MPM cases is expected to be reached in the period 2020-2024 (about 7000 cases). The decrease will be slow: about 26,000 MPM cases are expected to occur in Italy during the next 20 years (2020-2039). The MPM epidemic in Italy is far from being concluded despite the national ban implemented in 1992, and the peak is expected in 2020-2024, in both sexes. Our results are consistent with international literature.