Nephrogenic remnants: occasional ultrasound diagnosis and follow-up

Nephrogenic remnants (NRs) are nodular collections of undifferentiated renal blastema cells in the postnatal kidney that are recognized as putative precursor lesions of Wilms tumor (WT). NRs may remain stationary, undergo regression, or proliferate. In the last case, there is a high risk for the development of a WT. During infancy, they are most frequently of microscopic size, to be found only at autopsy in approximately 1% of infant kidneys. Approximately 1 out of 100 microscopic lesions persist and grow developing lesions large enough to be seen by ultrasound in the first months of life. We report on a case of NRs in a six year old child, as incidental finding during abdominal ultrasound performed for other purposes. In consideration of the potential evolution in WT, after a period of close surveillance of 14 months, the lesion was resected. Histological examination revealed the presence of NRs, no neoplastic lesions were found. Currently the patient is 16 years old, in good health, and there have been no signs of recurrence

The first 110,593 COVID-19 patients hospitalised in Lombardy: a regionwide analysis of case characteristics, risk factors and clinical outcomes

Objectives: To describe the monthly distribution of COVID-19 hospitalisations, deaths and case-fatality rates (CFR) in Lombardy (Italy) throughout 2020. Methods: We analysed de-identified hospitalisation data comprising all COVID-19-related admissions from 1 February 2020 to 31 December 2020. The overall survival (OS) from time of first hospitalisation was estimated using the Kaplan-Meier method. We estimated monthly CFRs and performed Cox regression models to measure the effects of potential predictors on OS. Results: Hospitalisation and death peaks occurred in March and November 2020. Patients aged ≥70 years had an up to 180 times higher risk of dying compared to younger patients [70–80: HR 58.10 (39.14–86.22); 80–90: 106.68 (71.01–160.27); ≥90: 180.96 (118.80–275.64)]. Risk of death was higher in patients with one or more comorbidities [1: HR 1.27 (95% CI 1.20–1.35); 2: 1.44 (1.33–1.55); ≥3: 1.73 (1.58–1.90)] and in those with specific conditions (hypertension, diabetes). Conclusion: Our data sheds light on the Italian pandemic scenario, uncovering mechanisms and gaps at regional health system level and, on a larger scale, adding to the body of knowledge needed to inform effective health service planning, delivery, and preparedness in times of crisis

Vaccines meet big data: State-ofthe- Art and future prospects. From the classical 3is ("isolate-inactivate- inject") vaccinology 1.0 to vaccinology 3.0, vaccinomics, and Beyond: A historical overview

Vaccines are public health interventions aimed at preventing infections-related mortality, morbidity, and disability. While vaccines have been successfully designed for those infectious diseases preventable by preexisting neutralizing specific antibodies, for other communicable diseases, additional immunological mechanisms should be elicited to achieve a full protection. "New vaccines" are particularly urgent in the nowadays society, in which economic growth, globalization, and immigration are leading to the emergence/ reemergence of old and new infectious agents at the animal-human interface. Conventional vaccinology (the so-called "vaccinology 1.0") was officially born in 1796 thanks to the contribution of Edward Jenner. Entering the twenty-first century, vaccinology has shifted from a classical discipline in which serendipity and the Pasteurian principle of the three Is (isolate, inactivate, and inject) played a major role to a science, characterized by a rational design and plan ("vaccinology 3.0"). This shift has been possible thanks to Big Data, characterized by different dimensions, such as high volume, velocity, and variety of data. Big Data sources include new cutting-edge, high-throughput technologies, electronic registries, social media, and social networks, among others. The current mini-review aims at exploring the potential roles as well as pitfalls and challenges of Big Data in shaping the future vaccinology, moving toward a tailored and personalized vaccine design and administration. © 2018 Bragazzi, Gianfredi, Villarini, Rosselli, Nasr, Hussein, Martini and Behzadifar

Vaccines meet big data: State-ofthe- Art and future prospects. From the classical 3is ("isolate-inactivate- inject") vaccinology 1.0 to vaccinology 3.0, vaccinomics, and Beyond: A historical overview

Vaccines are public health interventions aimed at preventing infections-related mortality, morbidity, and disability. While vaccines have been successfully designed for those infectious diseases preventable by preexisting neutralizing specific antibodies, for other communicable diseases, additional immunological mechanisms should be elicited to achieve a full protection. "New vaccines" are particularly urgent in the nowadays society, in which economic growth, globalization, and immigration are leading to the emergence/ reemergence of old and new infectious agents at the animal-human interface. Conventional vaccinology (the so-called "vaccinology 1.0") was officially born in 1796 thanks to the contribution of Edward Jenner. Entering the twenty-first century, vaccinology has shifted from a classical discipline in which serendipity and the Pasteurian principle of the three Is (isolate, inactivate, and inject) played a major role to a science, characterized by a rational design and plan ("vaccinology 3.0"). This shift has been possible thanks to Big Data, characterized by different dimensions, such as high volume, velocity, and variety of data. Big Data sources include new cutting-edge, high-throughput technologies, electronic registries, social media, and social networks, among others. The current mini-review aims at exploring the potential roles as well as pitfalls and challenges of Big Data in shaping the future vaccinology, moving toward a tailored and personalized vaccine design and administration. © 2018 Bragazzi, Gianfredi, Villarini, Rosselli, Nasr, Hussein, Martini and Behzadifar

In vitro testing of estragole in HepG2 cells: Cytokinesis-block micronucleus assay and cell-cycle analysis

The alkenylbenzene estragole (systematic name, 1-allyl-4-methoxybenzene) is a natural component of essential oils from various spices and herbs, including fennel, and it is used as a food and beverage flavouring agent. Estragole has been reported to be hepatocarcinogenic at high doses in rodents. However, in a previous in vitro study, we found that estragole did not exhibit cytotoxic effects after 4 hours of exposure, nor did it induce DNA damage or apoptosis in human HepG2 hepatoblastoma cells. As fennel tea is widely used for symptomatic treatment of spasmodic gastrointestinal conditions in infants, we aimed at further assessing its safety in a different experimental setting. We thus searched for possible cytogenetic effects and interference with cell-cycle progression in the same human hepatoblastoma cell line. Estragole did not show any clastogenic/aneugenic activities in the cytokinesis-block micronucleus assay, and no effects on cell-cycle checkpoints were observed

Wikipedia searches and the epidemiology of infectious diseases: A systematic review

This review aims to collect, analyse and synthesize the available evidence that can be provided by Wikipedia for epidemiologic surveillance purposes. PRISMA guidelines were followed. PubMed/Medline and Scopus were consulted. Out of 238 retrieved articles, 16 articles were included in the systematic review. The most frequently assessed infectious disease was Influenza, followed by arboviruses and measles. Influenza studies show that Wikipedia could be consid-ered a scientifically valid surveillance system that fills the main gaps in existing traditional surveillance systems. As regards arboviruses, searches on the Web have positively mediated the relationship between epidemiological data and the number of Wikipedia page visualization. Regarding measles, studies showed a strong/moderate temporal correlation between infectious disease notification bulletins and Wikipedia search trends. Despite the type of infectious agents, three main aims can be detected: (i) understand the public's interest, (ii) explore the use of Wikipedia by organizations, and (iii) assess the accuracy of Wikipedia content. These new strategies for surveillance of infectious diseases should be implemented, to date they could be useful in supporting traditional surveillance

Wikipedia searches and the epidemiology of infectious diseases: A systematic review

This review aims to collect, analyse and synthesize the available evidence that can be provided by Wikipedia for epidemiologic surveillance purposes. PRISMA guidelines were followed. PubMed/Medline and Scopus were consulted. Out of 238 retrieved articles, 16 articles were included in the systematic review. The most frequently assessed infectious disease was Influenza, followed by arboviruses and measles. Influenza studies show that Wikipedia could be consid-ered a scientifically valid surveillance system that fills the main gaps in existing traditional surveillance systems. As regards arboviruses, searches on the Web have positively mediated the relationship between epidemiological data and the number of Wikipedia page visualization. Regarding measles, studies showed a strong/moderate temporal correlation between infectious disease notification bulletins and Wikipedia search trends. Despite the type of infectious agents, three main aims can be detected: (i) understand the public's interest, (ii) explore the use of Wikipedia by organizations, and (iii) assess the accuracy of Wikipedia content. These new strategies for surveillance of infectious diseases should be implemented, to date they could be useful in supporting traditional surveillance

What can internet users' behaviours reveal about the mental health impacts of the COVID-19 pandemic? A systematic review

Objectives: At the end of 2019, an acute infectious pneumonia (coronavirus disease 2019 [COVID-19]) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) began in Wuhan, China, and subsequently spread around the world starting a pandemic. Globally, to date, there have been >118 million confirmed cases, including >2 million deaths. In this context, it has been shown that the psychological impact of the pandemic is important and that it can be associated with an increase in internet searches related to fear, anxiety, depression, as well as protective behaviours, health knowledge and even maladaptive behaviours.Study design: This is a systematic review.Methods: This review aims to collect, analyse and synthesise available evidence on novel data streams for surveillance purposes and/or their potential for capturing the public reaction to epidemic outbreaks, particularly focusing on mental health effects and emotions.Results: At the end of the screening process, 19 articles were included in this systematic review. Our results show that the COVID-19 pandemic had a great impact on internet searches for mental health of entire populations, which manifests itself in a significant increase of depressed, anxious and stressed internet users' emotions.Conclusions: Novel data streams can support public health experts and policymakers in establishing priorities and setting up long-term strategies to mitigate symptoms and tackle mental health disorders. (C) 2021 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved