Search

Natural Language Processing (NLP) is a subset of artificial intelligence that enables machines to understand and respond to human language through Large Language Models (LLMs)‥ These models have diverse applications in fields such as medical research, scientific writing, and publishing, but concerns such as hallucination, ethical issues, bias, and cybersecurity need to be addressed. To understand the scientific community's understanding and perspective on the role of Artificial Intelligence (AI) in research and authorship, a survey was designed for corresponding authors in top medical journals. An online survey was conducted from July 13th, 2023, to September 1st, 2023, using the SurveyMonkey web instrument, and the population of interest were corresponding authors who published in 2022 in the 15 highest-impact medical journals, as ranked by the Journal Citation Report. The survey link has been sent to all the identified corresponding authors by mail. A total of 266 authors answered, and 236 entered the final analysis. Most of the researchers (40.6%) reported having moderate familiarity with artificial intelligence, while a minority (4.4%) had no associated knowledge. Furthermore, the vast majority (79.0%) believe that artificial intelligence will play a major role in the future of research. Of note, no correlation between academic metrics and artificial intelligence knowledge or confidence was found. The results indicate that although researchers have varying degrees of familiarity with artificial intelligence, its use in scientific research is still in its early phases. Despite lacking formal AI training, many scholars publishing in high-impact journals have started integrating such technologies into their projects, including rephrasing, translation, and proofreading tasks. Efforts should focus on providing training for their effective use, establishing guidelines by journal editors, and creating software applications that bundle multiple integrated tools into a single platform., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Salvagno et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: COVID-19 dramatically reshaped mortality across Europe. This study aimed to assess its impact on total mortality in European countries taking into consideration the relationship with selected country-level socioeconomic indicators, non-pharmaceutical interventions, and vaccine uptake., Methods: We obtained weekly mortality data from 2010 to 2023 from the Short-term Mortality Fluctuations data series, the annual population data from the United Nations archives, selected sociodemographic and economic indicators from the World Bank's database, the stringency index and the percentage of the population fully vaccinated from Our World in Data. A quasi-Poisson regression model trained on pre-pandemic years was used to estimate expected number of deaths in 2020-2023 in 29 European countries. Excess mortality was estimated using three different metrics: excess deaths (number), relative excess mortality (% different from expected deaths) and age-standardized excess death rate per 10,000 population. The relationship between socioeconomic indicators and excess mortality was evaluated using linear regression models, which included both linear and quadratic terms for the predictors to account for possible non-linear relationships., Findings: We estimated 1,642,586 excess deaths (95% confidence interval, CI: 1,607,161-1,678,010) across all countries over the four years (+8.0% compared to the expected number of deaths). Excess mortality was mainly concentrated in 2020-2022 (0.52 million excess deaths in 2020, 0.57 million in 2021 and 0.44 million in 2022), with no substantial excess (0.11 million) estimated for 2023. Over the period 2020-23, the highest number of excess deaths was estimated for Italy (227,736 deaths, +8.7%), Poland (223,735 deaths, +13.7%), and Germany (218,111 deaths, +5.6%), while the highest excesses in relative terms were in Bulgaria (72,328 deaths, +17.2%), Lithuania (23,813 deaths, +16.1%), and Slovakia (31,984 deaths, +14.9%). The age-standardised death rates ranged from 1.8 per 10,000 population in Sweden to 24.7 in Bulgaria. The percentage of the population living below the poverty line and the Gini index were significantly associated with an increased excess death rate, with p -values for the linear and quadratic terms being 0.003 and 0.003 for the Gini index, and 0.024 and 0.017 for the population living below the poverty line. Conversely, gross domestic product per capita ( p -values for the linear and quadratic terms: <0.001, 0.003), health expenditure (0.001, 0.273) and the percentage of people fully vaccinated by the end of 2021 (<0.001, 0.989) or 2022 (0.001, 0.890) were inversely associated with excess death rate. No significant association was observed with population density and stringency index., Interpretation: The observed geographic disparities in total mortality excess across Europe can be related to differences in socioeconomic contexts, as well as to suboptimal vaccine uptakes in some countries., Funding: This research was supported by European Union (EU) funding within the NextGeneration EU-MUR PNRR Extended Partnership initiative on Emerging Infectious Diseases (Project no. PE00000007, INF-ACT). The funding source had no role in study design; in the collection, analysis, and interpretation of data; in the writing of the report; and in the decision to submit the paper for publication., Competing Interests: The authors have no conflict of interest related to this work., (© 2024 The Author(s).)

Background: The COVID-19 pandemic has had an unprecedent impact of everyday life with deleterious consequences on global health, economics, and society. Thus, accurate and timely information is critical for monitoring its spread and mitigating its impact. ChatGPT is a large language model chatbot with artificial intelligence, developed by OpenAI, that can provide both textual content and R code for predictive models. It may prove to be useful in analyzing and interpreting COVID-19-related data., Methods: This paper explores the application of ChatGPT to the monitoring of the COVID-19 pandemic, presenting R code for predictive models and demonstrating the model's capabilities in sentiment analysis, information extraction, and predictive modelling. We used the prediction models suggested by ChatGPT to predict the daily number of COVID-19 deaths in Italy. The prediction accuracy of the models was compared using the following metrics: mean squared error (MSE), mean absolute deviation (MAD) and root mean squared error (RMSE)., Results: ChatGPT suggested three different predictive models, including ARIMA, Random Forest and Prophet. The ARIMA model outperformed the other two models in predicting the daily number of COVID-19 deaths in Italy, with lower MSE, MAD, and RMSE values as compared to the Random Forest and Prophet., Conclusions: This paper demonstrates the potential of ChatGPT as a valuable tool in the monitoring of the pandemic. By processing large amounts of data and providing relevant information, ChatGPT has the potential to provide accurate and timely insights, and support decision-making processes to mitigate the spread and impact of pandemics. The paper highlights the importance of exploring the capabilities of artificial intelligence in the management of public emergencies and provides a starting point for future research in this area.

Background: Italy had a persistent excess of total mortality up to July 2022. This study provides updated estimates of excess mortality in Italy until February 2023., Methods: Mortality and population data from 2011 to 2019 were used to estimate the number of expected deaths during the pandemic. Expected deaths were obtained using over-dispersed Poisson regression models, fitted separately for men and women, including calendar year, age group, and a smoothed function of the day of the year as predictors. The excess deaths were then obtained by calculating the difference between observed and expected deaths and were computed at all ages and working ages (25-64 years)., Results: We estimated 26,647 excess deaths for all ages and 1248 for working ages from August to December 2022, resulting in a percent excess mortality of 10.2% and 4.7%, respectively. No excess mortality was detected in January and February 2023., Conclusions: Our study indicates substantial excess mortality beyond those directly attributed to COVID-19 during the BA.4 and BA.5 Omicron wave in the latter half of 2022. This excess could be attributed to additional factors, such as the heatwave during the summer of 2022 and the early onset of the influenza season.

This paper discusses the use of Artificial Intelligence Chatbot in scientific writing. ChatGPT is a type of chatbot, developed by OpenAI, that uses the Generative Pre-trained Transformer (GPT) language model to understand and respond to natural language inputs. AI chatbot and ChatGPT in particular appear to be useful tools in scientific writing, assisting researchers and scientists in organizing material, generating an initial draft and/or in proofreading. There is no publication in the field of critical care medicine prepared using this approach; however, this will be a possibility in the next future. ChatGPT work should not be used as a replacement for human judgment and the output should always be reviewed by experts before being used in any critical decision-making or application. Moreover, several ethical issues arise about using these tools, such as the risk of plagiarism and inaccuracies, as well as a potential imbalance in its accessibility between high- and low-income countries, if the software becomes paying. For this reason, a consensus on how to regulate the use of chatbots in scientific writing will soon be required., (© 2023. The Author(s).)

Background: The impact of new lineages and sub-lineages of Omicron on total and excess mortality is largely unknown. This study aims to provide estimates of excess mortality during the circulation of the Omicron variant in Italy updated to July 2022., Methods: Over-dispersed Poisson regression models, fitted separately for men and women, on 2011-2019 mortality data were used to estimate the expected number of deaths during the Covid-19 pandemic. The excess deaths were then obtained by the difference between observed and expected deaths and computed at all ages and at working ages (25-64 years)., Results: Between April and June 2022, we estimated 9,631 excess deaths (+6.3%) at all ages (4,400 in April, 3,369 in May, 1,862 in June) and 12,090 in July 2022 (+23.4%). At working ages, the excess was 763 (+4.9%) in April-June 2022 and 679 (+13.0%) in July 2022., Conclusions: Excess total mortality persisted during the circulation of different lineages and sub-lineages of the Omicron variant in Italy. This excess was not limited to the elderly population but involved also working age individuals, though the absolute number of deaths was small. The substantial excess found in July 2022 is, however, largely attributable to high temperatures. At the end of the year, this may translate into 30 to 35,000 excess deaths, i.e. over 5% excess mortality. This reversed the long-term trend toward increasing life expectancy, with the relative implications in social security and retirement schemes.

Beckwith-Wiedemann syndrome (BWS) is a clinically and genetically heterogeneous overgrowth disease. BWS is caused by (epi)genetic defects at the 11p15 chromosomal region, which harbors two clusters of imprinted genes, IGF2 / H19 and CDKN1C / KCNQ1OT1 , regulated by differential methylation of imprinting control regions, H19/IGF2 :IG DMR and KCNQ1OT1 :TSS DMR, respectively. A subset of BWS patients show multi-locus imprinting disturbances (MLID), with methylation defects extended to other imprinted genes in addition to the disease-specific locus. Specific (epi)genotype-phenotype correlations have been defined in order to help clinicians in the classification of patients and referring them to a timely diagnosis and a tailored follow-up. However, specific phenotypic correlations have not been identified among MLID patients, thus causing a debate on the usefulness of multi-locus testing in clinical diagnosis. Finally, the high incidence of BWS monozygotic twins with discordant phenotypes, the high frequency of BWS among babies conceived by assisted reproductive technologies, and the female prevalence among BWS-MLID cases provide new insights into the timing of imprint establishment during embryo development. In this review, we provide an overview on the clinical and molecular diagnosis of single- and multi-locus BWS in pre- and post-natal settings, and a comprehensive analysis of the literature in order to define possible (epi)genotype-phenotype correlations in MLID patients.

Background: To date, the European experience with COVID-19 mortality has been different to that observed in China and Asia. We aimed to forecast mortality trends in the 27 countries of the European Union (EU), plus Switzerland and the UK, where lockdown dates and confinement interventions have been heterogeneous, and to explore its determinants., Methods: We have adapted our predictive model of COVID-19-related mortality, which rested on the observed mortality within the first weeks of the outbreak and the date of the respective lockdown in each country. It was applied in a training set of three countries (Italy, Germany and Spain), and then applied to the EU plus the UK and Switzerland. In addition, we explored the effects of timeliness and rigidity of the lockdown (on a five-step scale) and population density in our forecasts. We report r 2 , and percent variation of expected versus observed deaths, all following TRIPOD guidance., Results: We identified a homogeneous distribution of deaths, and found a median of 24 days after lockdown adoption to reach the maximum daily deaths. Strikingly, cumulative deaths up to April 25 th , 2020 observed in Europe separated countries in three waves, according to the time lockdown measures were adopted following the onset of the outbreak: after a week, within a week, or even prior to the outbreak (r 2 =0.876). In contrast, no correlation neither with lockdown rigidity nor population density were observed., Conclusions: The European experience confirms that early, effective interventions of lockdown are fundamental to minimizing the COVID-19 death toll.

Background: COVID-19 is an infectious disease caused by a novel coronavirus (SARS-CoV-2). The immunopathogenesis of the infection is currently unknown. Healthcare workers (HCWs) are at highest risk of infection and disease. Aim of the study was to assess the sero-prevalence of SARS-CoV-2 in an Italian cohort of HCWs exposed to COVID-19 patients., Methods: A point-of-care lateral flow immunoassay (BioMedomics IgM-IgG Combined Antibody Rapid Test) was adopted to assess the prevalence of IgG and IgM against SARS-CoV-2. It was ethically approved ("Milano Area 1" Ethical Committee prot. n. 2020/ST/057)., Results: A total of 202 individuals (median age 45 years; 34.7% males) were retrospectively recruited in an Italian hospital (Milan, Italy). The percentage (95% CI) of recruited individuals with IgM and IgG were 14.4% (9.6-19.2%) and 7.4% (3.8-11.0%), respectively. IgM were more frequently found in males (24.3%), and in individuals aged 20-29 (25.9%) and 60-69 (30.4%) years. No relationship was found between exposure to COVID-19 patients and IgM and IgG positivity., Conclusions: The present study did show a low prevalence of SARS-CoV-2 IgM in Italian HCWs. New studies are needed to assess the prevalence of SARS-CoV-2 antibodies in HCWs exposed to COVID-19 patients, as well the role of neutralizing antibodies.