The intestinal microbiota is a crucial regulator of human health and disease because of its interactions with the immune system. Tobacco smoke also influences the human ecosystem with implications for disease development. This systematic review aims to analyze the available evidence, until June 2021, on the relationship between traditional and/or electronic cigarette smoking and intestinal microbiota in healthy human adults. Of the 2645 articles published in PubMed, Scopus, and Web of Science, 13 were included in the review. Despite differences in design, quality, and participants’ characteristics, most of the studies reported a reduction in bacterial species diversity, and decreased variability indices in smokers’ fecal samples. At the phylum or genus level, the results are very mixed on bacterial abundance both in smokers and non-smokers with two exceptions. Prevotella spp. appears significantly increased in smokers and former smokers but not in electronic cigarette users, while Proteobacteria showed a progressive increase in Desulfovibrio with the number of pack-years of cigarette (p = 0.001) and an increase in Alphaproteobacteria (p = 0.04) in current versus never smokers. This attempt to systematically characterize the effects of tobacco smoking on the composition of gut microbiota gives new perspectives on future research in smoking cessation and on a new possible use of probiotics to contrast smoke-related dysbiosis.

Background. This cross-sectional study aimed to explore the microbial composition of the gut and its possible association with the Mediterranean diet (MD) after adjusting for demographic and anthropometric characteristics in a sample of healthy young Italian adults. Methods. Gut microbiota, demographic information, and data on adherence to MD and physical activity (PA) habits were collected in a sample of 140 university students (48.6% males, mean age 22.5 ± 2.9) with a mean body mass index (BMI) of 22.4 ± 2.8 kg/m² (15.2–33.8) and a mean PA level of 3006.2 ± 2973.6 metabolic equivalent (MET)-minutes/week (148–21,090). Results. A high prevalence of Firmicutes and Bacteroidetes was found in all the fecal samples. Significant dissimilarities in the microbiota composition were found on the basis of MD adherence and PA levels (p = 0.001). At the genus level, Streptococcus and Dorea were highly abundant in overweight/obese individuals, Ruminococcus and Oscillospira in participants with lower adherence to MD, and Lachnobacterium in subjects with low levels of PA (p = 0.001). A significantly higher abundance of Paraprevotella was shown by individuals with lower BMI, lower MD adherence, and lower PA levels (p = 0.001). Conclusions. This study contributes to the characterization of the gut microbiome of healthy humans. The findings suggest the role of diet and PA in determining gut microbiota variability.

Introduction: Italy was the first European country affected by COVID-19. Thanks to governmental containment measures (9 March 2020), the spread of COVID-19 was limited. However, in this context, accurate data assessment is crucial and mortality is a more reliable indicator of the virus spread compared to the count of positive cases. This study aimed to retrospectively evaluate the impact of the pandemic in different areas of Italy using the time series analysis of official deaths and excess COVID-19 deaths.Methods: Mortality data (23 February–30 April 2022) by Istituto Nazionale di Statistica (ISTAT) were analyzed, including four waves of COVID-19. Previous mortality data (January 2015–November 2019) were used to estimate a Poisson regression model of the pre-pandemic mortality pattern and derive the excess COVID-19 deaths as the difference between the actual deaths number and the extrapolation of the previous mortality pattern to the pandemic period, separately for Northern, Central, and Southern Italy, to compare the impact of mortality across time periods and geographical areas.Results: Estimated excess compared with official COVID-19 mortality shows that, during the first wave, there was an underestimation of deaths. COVID-19 mortality rate almost doubled the official rate in the North (1.60‰ vs. 0.86‰) and nearly tripled it in the South (0.22‰ vs. 0.08‰). In late 2020-early 2021, official and estimated mortality curves are closer, displaying just a small gap at the start of the second wave. During the fourth wave (end of 2021-early 2022), Northern and Central Italy show reasonable agreement; the South presents a large relative underestimation of deaths (+90% increase), with a large increase in its excess deaths national quota, 9% in the first wave to 42% in the fourth.Discussion: The results provide a measure of the COVID-19 excess deaths and an unbiased estimate of Italian mortality rates. In the first wave, the gap between official COVID-19 and excess mortality was particularly high and lockdown measures may have reduced the spread of the infection. In the fourth wave, the gap for the South increases again, probably because the healthcare system may not have coped with the prolonged pressure of the pandemic, or for a decreased compliance with the official paper-based mortality surveillance system that could be overcome in the future by digitalizing the process.

Background: Italy, particularly the northern region of Lombardy, has experienced very high rates of COVID-19 cases and deaths. Several indicators, i.e., the number of new positive cases, deaths and hospitalizations, have been used to monitor virus spread, but all suffer from biases. The aim of this study was to evaluate an alternative data source from Emergency Medical Service (EMS) activities for COVID-19 monitoring. Methods: Calls to the emergency number (112) in Lombardy (years 2015–2022) were studied and their overlap with the COVID-19 pandemic, influenza and official mortality peaks were evaluated. Modeling it as a counting process, a specific cause contribution (i.e., COVID-19 symptoms, the “signal”) was identified and enucleated from all other contributions (the “background”), and the latter was subtracted from the total observed number of calls using statistical methods for excess event estimation. Results: A total of 6,094,502 records were analyzed and filtered for respiratory and cardiological symptoms to identify potential COVID-19 patients, yielding 742,852 relevant records. Results show that EMS data mirrored the time series of cases or deaths in Lombardy, with good agreement also being found with seasonal flu outbreaks. Conclusions: This novel approach, combined with a machine learning predictive approach, could be a powerful public health tool to signal the start of disease outbreaks and monitor the spread of infectious diseases.

Tobacco use is one of the main risk factors for non-communicable diseases. Avoiding youth initiation and treating addiction are fundamental public health issues to ensure better health. Among tobacco control policies, increasing tobacco price is the single most effective intervention. It reduces tobacco consumption, especially among youths, while representing a government financing source. This study aimed to assess the agreement with the proposal of a one-euro increase in tobacco price earmarked to health issues among students at Sapienza University. Two convenience samples were surveyed, five years apart, on World No Tobacco Days. Smoking habits, agreement with the proposal and reasons for it were collected. Results from the 208 questionnaires (107 in 2014, 101 in 2019) showed 46.6% of agreement with the proposal (53.3% in 2014, 39.2% in 2019, p = 0.044). Main predictive factor for agreement was being a non-smoker (OR = 6.33 p < 0.001), main reason (64.8%) was it could trigger smokers to quit or reduce consumption. Several factors might have influenced this finding, including the introduction of novel tobacco products and their increased advertisement on social media. In 2024, European Union is planning to update the Tobacco Taxation Directive which could greatly contribute to the reduction of non-communicable diseases and premature deaths.