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.

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.

To the Editor, Since the end of February 2020, Italy, first non-Asian Country, has reported an ever increasing number of COronaVIrus Disease 19 (COVID-19) patients, which has reached over 200 000 confirmed severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infected subjects, resulting in more than 34 000 deaths (data updated to June 19th, 2020 1). Patients with asthma are potentially more severely affected by SARS-CoV-2 infection and respiratory viruses are known to be associated with severe adverse asthma outcomes, including increased risk of asthma exacerbation episodes. 2 Nonetheless, according to the epidemiological studies published so far, asthma is not among the most common clinical conditions in COVID-19 patients. 3 About 5%-10% of asthmatics are severe, 4 and one would expect increased vulnerability to SARS-CoV-2 infection, but no data are so far available to confirm this hypothesis. We investigated the incidence of COVID-19, describing its clinical course, in the population of the Severe Asthma Network in Italy (SANI), one of the largest registry for severe asthma worldwide, 5 and in an additional Center (Azienda Ospedaliero Univeristaria di Ferrara, Ferrara, Italy). All centres have been contacted and inquired to report confirmed or highly suspect cases of COVID-19 (ie, patients with symptoms, laboratory findings and lung imaging typical of COVID-19 but without access to nasopharyngeal or oropharyngeal swab specimens because of clinical contingencies/emergency) among their cohorts of severe asthma. Demographic and clinical have been obtained from the

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assessed. These results, however, are heavily influenced by local factors such as the number of nondetected but infected patients, availability of hospital and ICU beds, local treatment protocols, and lack of prompt reporting of those recovered. The implications of A, The model accuracy curve is achieved using a 3rd-grade polynomial curve in Italy, Germany, Spain, and New York State. It highlights the differences between real and simulated data after inputting the first 17 d. B, The curves depicting the predictions of the expected deaths are obtained by a 3rd-grade polynomial curve up to daily peak and later by a parametric 5PL asymmetrical sigmoidal. The predictions are calculated starting from the first 17 d. The curve of expected deaths per country splits considering the number of days supposed to reach daily peak after the lockdown (28 d: upper curve; 21 d: lower curve)