Background: Melanomas lacking mutations in BRAF, NRAS and NF1 are frequently referred to as "triple wildtype" (tWT) melanomas. They constitute 5-10 % of all melanomas and remain poorly characterized regarding clinical characteristics and response to therapy. This study investigates the largest multicenter collection of tWTmelanomas to date. Methods: Targeted next-generation sequencing of the TERT promoter and 29 melanoma-associated genes were performed on 3109 melanoma tissue samples of the prospective multicenter study ADOREG/TRIM of the DeCOG revealing 292 patients suffering from tWT-melanomas. Clinical characteristics and mutational patterns were analyzed. As subgroup analysis, we analyzed 141 tWT-melanoma patients receiving either anti-CTLA4 plus antiPD1 or anti PD1 monotherapy as first line therapy in AJCC stage IV. Results: 184 patients with cutaneous melanomas, 56 patients with mucosal melanomas, 34 patients with acral melanomas and 18 patients with melanomas of unknown origin (MUP) were included. A TERT promoter mutation could be identified in 33.2 % of all melanomas and 70.5 % of all tWT-melanomas harbored less than three mutations per sample. For the 141 patients with stage IV disease, mPFS independent of melanoma type was 6.2 months (95 % CI: 4-9) and mOS was 24.8 months (95 % CI: 14.2-53.4) after first line anti-CTLA4 plus anti-PD1 therapy. After first-line anti-PD1 monotherapy, mPFS was 4 months (95 %CI: 2.9-8.5) and mOS was 29.18 months (95 % CI: 17.5-46.2). Conclusions: While known prognostic factors such as TERT promoter mutations and TMB were equally distributed among patients who received either anti-CTLA4 plus anti-PD1 combination therapy or anti-PD1 monotherapy as first line therapy, we did not find a prolonged mPFS or mOS in either of those. For both therapy concepts, mPFS and mOS were considerably shorter than reported for melanomas with known oncogene mutations.

Background and Objectives The SARS-CoV-2 pandemic and the different manifestations of the coronavirus disease 2019 (COVID-19) are a major challenge for health systems worldwide. Medical personnel have a special role in containing the pandemic. The aim of the study was to investigate the SARS-CoV-2 IgG antibody prevalence in extraclinical personnel depending on their operational area in the fight against the COVID-19 pandemic. Methods On May 28 and 29, 2020, serum samples were taken from 732 of 1183 employees (61.9%) of the professional fire brigade and aid organizations in the city area and tested for SARS-CoV-2 IgG antibodies. The employees were divided into four categories according to their type of participation. category 1: decentralized PCR sampling teams, category 2: rescue service, category 3: fire protection, category 4: situation center. Some employees participated in more than one operational area. Results SARS-CoV-2 IgG antibodies were detected in 8 of 732 serum samples. This corresponds to a prevalence of 1.1%. A previous COVID-19 infection was known in 3 employees. In order to make a separate assessment of the other employees possible and to diagnose unknown infections, a corrected collective of 729 employees with 6 SARS-CoV-2 antibody detection was considered separately. The prevalence in the corrected collective is 0.82%. After subdividing the collective into areas of activity, the prevalence was low (1: 0.77%, 2: 0.9%, 3: 1.00%, 4: 1.58%). Conclusions The seroprevalence of SARS-CoV-2 in the study collective is low at 1.1% and 0.82%, respectively. There is an increased seroprevalence in operational areas with a lower risk of virus exposure in comparison to operational areas with a higher risk.

A survey conducted by the German Socio-Economic Panel during the early phase of the SARS-CoV‑2 pandemic in spring 2020 showed that the perceived risks of SARS-CoV‑2 infection were a massive overestimation of the actual risks. A total of 5783 people (2.3

(1) Background: The COVID-19 vaccination has caused uncertainty among employees and employers regarding vaccination reactions and incapacitation. At the time of our study, three vaccines are licensed in Germany to combat the COVID-19 pandemic (BioNTech/Pfizer (Comirnaty), AstraZeneca (Vaxzevria), and Moderna (Spikevax). We aim to assess how often and to what extent frontline healthcare workers had vaccination reactions after the first and second vaccination. The main focus is on the amount of sick leave after the vaccinations. (2) Methods: We create a web-based online questionnaire and deliver it to 270 medical directors in emergency medical services all over Germany. They are asked to make the questionnaire public to employees in their area of responsibility. To assess the association between independent variables and adverse effects of vaccination, we use log-binomial regression to estimate prevalence ratios (PR) with 95% confidence intervals (95%CI) for dichotomous outcomes (sick leave). (3) Results: A total of 3909 individuals participate in the survey for the first vaccination, of whom 3657 (94%) also provide data on the second vaccination. Compared to the first vaccination, mRNA-related vaccine reactions are more intense after the second vaccination, while vaccination reactions are less intense for vector vaccines. (4) Conclusion: Most vaccination reactions are physiological (local or systemic). Our results can help to anticipate the extent to which personnel will be unable to work after vaccination. Even among vaccinated HCWs, there seems to be some skepticism about future vaccinations. Therefore, continuous education and training should be provided to all professionals, especially regarding vaccination boosters. Our results contribute to a better understanding and can therefore support the control of the pandemic.

We aimed to review Semmelweis’s complete work on puerperal sepsis mortality in maternity wards in relation to exposure to cadavers and chlorine handwashing and other factors from the perspective of modern epidemiological methods. We reviewed Semmelweis' complete work and data as published by von Györy 1905 according to current standards. We paid particular attention to Semmelweis's definition of mortality in and of itself, to concepts of modern epidemiology that were already recognizable in Semmelweis's work, and to bias sources. We did several quantitative bias analyses to address selection bias and information bias from outcome measurement error. Semmelweis addressed biases that have become known to modern epidemiology, such as confounding, selection bias and bias from outcome misclassification. Our bias analysis shows that differential loss to follow-up is an unlikely explanation for his results. Bias due to outcome misclassification would only be relevant if misclassification differed between time periods. Confounding by health status was likely but could not be quantitatively addressed. Semmelweis was aware that cause-specific mortality is a function of incidence and prognosis. He reasoned in potential outcome terms to estimate the reduced number of deaths from an intervention. He advanced a hypothesis of clinic overcrowding as a risk factor for puerperal sepsis mortality that turns out to be wrong. Semmelweis’ data provide a great pool for illustrating the logic of scientific discovery by use of the numerical method. The explanatory power of his work was strong and Semmelweis was able to refute several previous causal explanations.

Zusammenfassung Hintergrund und Fragestellung Die SARS-CoV-2-Pandemie und die unterschiedliche Ausprägung des Erkrankungsbilds COVID-19 stellen die Gesundheitssysteme weltweit vor eine große Herausforderung. Medizinischem Personal kommt in der Pandemiebekämpfung eine besondere Rolle zu. Ziel der Studie war, die SARS-CoV-2-IgG-Antikörper-Prävalenz bei Personal in der außenklinischen Pandemiebekämpfung in Abhängigkeit von Tätigkeitsbereichen zu untersuchen. Methoden Es wurden am 28. und 29.05.2020 von 732 der 1183 Mitarbeitenden (61,9 %) der Berufsfeuerwehr sowie der Hilfsorganisationen im Stadtgebiet Serumproben entnommen und auf SARS-CoV-2-IgG-Antikörper getestet. Entsprechend der Einsatzgebiete wurde das Personal in 4 Kategorien eingeteilt. Kategorie 1: dezentrale PCR-Abstrichteams, Kategorie 2: Rettungsdienst, Kategorie 3: Brandschutz, Kategorie 4: Lagezentrum. Die Tätigkeit des Personals war dabei nicht zwingend auf einen Tätigkeitsbereich beschränkt. Ergebnisse In 8 von 732 Serumproben wurden SARS-CoV-2-IgG-Antikörper nachgewiesen. Dies entspricht einer Prävalenz von 1,1 %. Bei 3 Mitarbeitern war eine COVID-19-Infektion schon vor Studienbeginn bekannt. Um eine separate Beurteilung der übrigen Mitarbeiter zu ermöglichen und unbekannte Infektionen zu diagnostizieren, wurde ein korrigiertes Kollektiv aus 729 Mitarbeitern mit 6 SARS-CoV-2-Antikörper-Nachweisen separat betrachtet. Die Prävalenz beträgt im korrigierten Kollektiv 0,82 %. Nach Unterteilung der Kollektive in Tätigkeitsbereiche war die Prävalenz ebenfalls niedrig (1: 0,77 %, 2: 0,9 %, 3: 1,00 %, 4: 1,58 %). Schlussfolgerung Die Seroprävalenz von SARS-CoV‑2 im Studienkollektiv ist mit 1,1 % bzw. 0,82 % niedrig. Die Seroprävalenz ist in Tätigkeitsfeldern mit niedriger Gefahr der Virusexposition gegenüber Tätigkeitsfeldern mit größerer Expositionsgefahr erhöht.

Cutaneous vascular tumors consist of a heterogeneous group of benign proliferations, including a range of hemangiomas and vascular malformations, as well as heterogeneous groups of both borderline and malignant neoplasms such as Kaposi's sarcoma and angiosarcomas. The genetics of these tumors have been assessed independently in smaller individual cohorts making comparisons difficult. In our study, we analyzed a representative cohort of benign vascular proliferations observed in a clinical routine setting as well as a selection of malignant vascular proliferations. Our cohort of 104 vascular proliferations including hemangiomas, malformations, angiosarcomas and Kaposi's sarcoma were screened by targeted next-generation sequencing for activating genetic mutations known or assumed to be potentially relevant in vascular proliferations. An association analysis was performed for mutation status and clinico-pathological parameters. Frequent activating hotspot mutations in GNA genes, including GNA14 Q205, GNA11 and GNAQ Q209 were identified in 16 of 64 benign vascular tumors (25%). GNA gene mutations were particularly frequent (52%) in cherry (senile) hemangiomas (13 of 25). In angiosarcomas, activating RAS mutations (HRAS and NRAS) were identified in three samples (16%). No activating GNA or RAS gene mutations were identified in Kaposi's sarcomas. Our study identifies GNA14 Q205, GNA11 and GNAQ Q209 mutations as being the most common and mutually exclusive mutations in benign hemangiomas. These mutations were not identified in malignant vascular tumors, which could be of potential diagnostic value in distinguishing these entities.

Introduction Excess mortality is a suitable indicator of health consequences of COVID-19 because death from any cause is clearly defined contrary to death from Covid-19. We compared the overall mortality in 2020 with the overall mortality in 2016 to 2019 in Germany, Sweden and Spain. Contrary to other studies, we also took the demographic development between 2016 and 2020 and increasing life expectancy into account. Methods Using death and population figures from the EUROSTAT database, we estimated weekly and cumulative Standardized Mortality Ratios (SMR) with 95% confidence intervals (CI) for the year 2020. We applied two approaches to calculate weekly numbers of death expected in 2020: first, we used mean weekly mortality rates from 2016 to 2019 as expected mortality rates for 2020, and, second, to consider increasing life expectancy, we calculated expected mortality rates for 2020 by extrapolation from mortality rates from 2016 to 2019. Results In the first approach, the cumulative SMRs show that in Germany and Sweden there was no or little excess mortality in 2020 (SMR = 0.976 (95% CI: 0.974–0.978), and 1.030 (1.023–1.036), respectively), while in Spain the excess mortality was 14.8% (1.148 (1.144–1.151)). In the second approach, the corresponding SMRs for Germany and Sweden increased to 1.009 (1.007–1.011) and 1.083 (1.076–1.090), respectively, whereas results for Spain were virtually unchanged. Conclusion In 2020, there was barely any excess mortality in Germany for both approaches. In Sweden, excess mortality was 3% without, and 8% with consideration of increasing life expectancy.

Current European research estimates the number of undetected active SARS-CoV-2 infections (dark figure) to be two- to 130-fold the number of detected cases. We revisited the population-wide antigen tests in Slovakia and South Tyrol and calculated the dark figure of active cases in the vulnerable populations and the number of undetected active cases per detected active case at the time of the population-wide tests. Our analysis follows three steps: using the sensitivities and specificities of the used antigen tests, we first calculated the number of test-positive individuals and the proportion of actual positives in those who participated in the antigen tests. We then calculated the dark figure in the total population of Slovakia and South Tyrol, respectively. Finally, we calculated the ratio of the dark figure in the vulnerable population to the number of newly detected infections through PCR tests. Per one positive PCR result, another 0.15 to 0.71 cases must be added in South Tyrol and 0.01 to 1.25 cases in Slovakia. The dark figure was in both countries lower than assumed by earlier studies.

The Personal View by Eskild Petersen and colleagues1Petersen E Koopmans M Go U et al.Comparing SARS-CoV-2 with SARS-CoV and influenza pandemics.Lancet Infect Dis. 2020; (published online July 3.)https://doi.org/10.1016/S1473-3099(20)30484-9Summary Full Text Full Text PDF Scopus (844) Google Scholar is a brilliant piece of comparative, historic-epidemiological research. At some important points, however, the contribution of Petersen and colleagues is too vague, although quantitative information is available. We would therefore like to supplement the work with this information. Petersen and colleagues say the incubation period of the 1918 pandemic influenza is unknown. However, it is mentioned in several contemporary studies, such as the one by Nuzm and colleagues,2Nuzum JW Pilot I Stangl FH Bonar BE Pandemic influenza and pneumonia in a large civil hospital.JAMA. 1918; 71: 1562-1565Crossref Scopus (54) Google Scholar which says that, in 1918, the incubation period ranged from a few hours to 2 days. Petersen and colleagues do not provide an exact estimate for the proportion of patients requiring hospitalisation during the Spanish flu. The statistical yearbooks of Switzerland from 1924, 1925, and 1929 allow estimation of the number of hospitalisations during the Spanish flu.3Eidgenössisches Statistisches BureauStatistisches Jahrbuch der Schweiz.https://www.bfs.admin.ch/bfs/de/home/statistiken/kataloge-datenbanken/publikationen/uebersichtsdarstellungen/statistisches-jahrbuch.htmlDate accessed: July 30, 2020Google Scholar As shown in the appendix, Switzerland had an average population of 3·9 million in the years 1917–25 and about 90 000 hospitalisations in non-pandemic times. In 1918, during the first two waves of the Spanish flu, the number of hospitalisations was around 121 000, about 30 000 more than usual. Therefore, by dividing the number of people infected with influenza in 1918 by the population size for that year, we estimate that about 17% of the Swiss population was infected with influenza in 1918. Moreover, by dividing the number of excess hospitalisations by the number infected, we estimate that about 4·5% of infected people were hospitalised. As stated by Petersen and colleagues, the number of patients treated in an intensive care unit (ICU) during the Spanish flu is unknown because the first ICU worldwide opened in 1953 in Copenhagen, Denmark.4Berthelsen PG Cronqvist M The first intensive care unit in the world: Copenhagen 1953.Acta Anaesthesiol Scand. 2003; 47: 1190-1195Crossref PubMed Scopus (108) Google Scholar However, people treated in hospitals during the Spanish flu should be regarded as ICU patients because there are many reports that hospitals were overcrowded. For example, in Berlin, Germany, people were admitted to hospital only if they had a fever of at least 41°C.5Witte W Tollkirschen und quarantäne: die geschichte der Spanischen grippe. Wagenbach Klaus, Berlin2010: 6Google Scholar If we again use the data from Switzerland (appendix), this means that about 30 000 of 665 000 infected people in 1918 required ICU treatment, which is about 4500 per 100 000 infected people and about 4500 times higher than in the 2009 influenza pandemic.1Petersen E Koopmans M Go U et al.Comparing SARS-CoV-2 with SARS-CoV and influenza pandemics.Lancet Infect Dis. 2020; (published online July 3.)https://doi.org/10.1016/S1473-3099(20)30484-9Summary Full Text Full Text PDF Scopus (844) Google Scholar Furthermore, Petersen and colleagues speculated that, based on their historical perspective, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) might have a second wave. We have recently published a paper on subsequent waves in viral pandemics and found that more than 90% of epidemic events in influenza pandemics and about half of the epidemic events during the severe acute respiratory syndrome coronavirus pandemic had a second wave.6Standl F Joeckel K-H Kowall B Schmidt B Stang A Subsequent waves of viral pandemics, a hint for the future course of the SARS-CoV-2 pandemic.medRxiv. 2020; (published online July 14.) (preprint)https://doi.org/10.1101/2020.07.10.20150698Google Scholar It is unlikely that data from the Spanish flu in Switzerland will allow any predictions about the current SARS-CoV-2 pandemic. In Switzerland, SARS-CoV-2 resulted in 33 000 confirmed cases within 6 months. However, the Spanish flu resulted in 40 times more cases than SARS-CoV-2 during the same time interval, when adjusted for population sizes. We declare no competing interests. Download .pdf (.17 MB) Help with pdf files Supplementary appendix Comparing SARS-CoV-2 with SARS-CoV and influenza pandemicsThe objective of this Personal View is to compare transmissibility, hospitalisation, and mortality rates for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with those of other epidemic coronaviruses, such as severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV), and pandemic influenza viruses. The basic reproductive rate (R0) for SARS-CoV-2 is estimated to be 2·5 (range 1·8–3·6) compared with 2·0–3·0 for SARS-CoV and the 1918 influenza pandemic, 0·9 for MERS-CoV, and 1·5 for the 2009 influenza pandemic. Full-Text PDF

Background It is unknown if the SARS-CoV-2 pandemic will have a second wave. We analysed published data of five influenza pandemics (such as the Spanish Flu and the Swine Flu) and the SARS-CoV-1 pandemic to describe whether there were subsequent waves and how they differed. Methods We reanalysed literature and WHO reports on SARS-CoV-1 and literature on five influenza pandemics. We report frequencies of second and third waves, wave heights, wavelengths and time between subsequent waves. From this, we estimated peak-to-peak ratios to compare the wave heights, and wave-length-to-wave-length ratios to compare the wavelengths differences in days. Furthermore, we analysed the seasonality of the wave peaks and the time between the peak values of two waves. Results Second waves, the Spanish Flu excluded, were usually about the same height and length as first waves and were observed in 93% of the 57 described epidemic events of influenza pandemics and in 42% of the 19 epidemic events of the SARS-CoV-1 pandemic. Third waves occurred in 54% of the 28 influenza and in 11% of the 19 SARS-CoV-1 epidemic events. Third waves, the Spanish Flu excluded, usually peaked higher than second waves with a peak-to-peak ratio of 0.5. Conclusion While influenza epidemics are usually accompanied by 2nd waves, this is only the case in the minority of SARS-Cov1 epidemics.

SARS-CoV-2 is circulating the world and causing people to suffer from COVID-19.Many countries answer with lockdowns and quarantine [1,2] and in these countries, public life has come to a halt.For example, all childcare centers, Kindergarten, schools, universities, restaurants, sport and fitness clubs, shops that are not relevant to the universal service, and other facilities are currently closed in Germany and elsewhere.People are encouraged to work at home if possible.

Objectives: The first wave of the SARS-CoV-2 pandemic in Germany lasted from week 10 to 23 in 2020. The aim is to provide estimates of excess mortality in Germany during this time. Methods: We analyzed age-specific numbers of deaths per week from 2016 to week 26 in 2020. We used weekly mean numbers of deaths of 2016-2019 to estimate expected weekly numbers for 2020. We estimated standardized mortality ratios (SMR) and 95% confidence intervals. Results: During the first wave observed numbers of deaths were higher than expected for age groups 6069, 80-89, and 90+. The age group 70-79 years did not show excess mortality. The net excess number of deaths for weeks 10-23 was +8,071. The overall SMR was 1.03 (95%CI 1.03-1 .04). The largest increase occurred among people aged 80-89 and 90+ (SMR= 1.08 and SMR= 1.09). A sensitivity analysis that accounts for demographic changes revealed an overall SMR of 0.98 (95%CI 0.98-0.99) and a deficit of 4,926 deaths for week 10-23, 2020. Conclusions: The excess mortality existed for two months. The favorable course of the first wave may be explained by a younger age at infection at the beginning of the pandemic, lower contact rates, and a more efficient pandemic management. (C) 2020 The British Infection Association. Published by Elsevier Ltd. All rights reserved.