Managing hospital visitor admission during Covid-19: A discrete-event simulation by the data of a German University Hospital

The Corona pandemic and the associated need for visitor restrictions have defined an entirely new management task in hospitals: The hospital visitor management. The admission process of hospital visitors and the implementation of associated infection-prevention strategies such as the delivery of face masks thereby pose major challenges. In this work, we evaluate both implemented and planned admission processes in a German University Hospital based on a discrete-event simulation model and provide distinct recommendations for hospital visitor management with special consideration of digitalization, antigen testing, waiting times, space and staff utilization. We find the extraordinary potential of digitalization with a reduction of visitor waiting and service times of up to 90 percent, the significant burden for personnel and room capacity, in terms of antigen testing, especially, and the need for visitor restrictions in terms of a maximum number of visitors per inpatient

Artificial intelligence-based detection of pneumonia in chest radiographs

Artificial intelligence is gaining increasing relevance in the field of radiology. This study retrospectively evaluates how a commercially available deep learning algorithm can detect pneumonia in chest radiographs (CR) in emergency departments. The chest radiographs of 948 patients with dyspnea between 3 February and 8 May 2020, as well as 15 October and 15 December 2020, were used. A deep learning algorithm was used to identify opacifications associated with pneumonia, and the performance was evaluated by using ROC analysis, sensitivity, specificity, PPV and NPV. Two radiologists assessed all enrolled images for pulmonal infection patterns as the reference standard. If consolidations or opacifications were present, the radiologists classified the pulmonal findings regarding a possible COVID-19 infection because of the ongoing pandemic. The AUROC value of the deep learning algorithm reached 0.923 when detecting pneumonia in chest radiographs with a sensitivity of 95.4%, specificity of 66.0%, PPV of 80.2% and NPV of 90.8%. The detection of COVID-19 pneumonia in CR by radiologists was achieved with a sensitivity of 50.6% and a specificity of 73%. The deep learning algorithm proved to be an excellent tool for detecting pneumonia in chest radiographs. Thus, the assessment of suspicious chest radiographs can be purposefully supported, shortening the turnaround time for reporting relevant findings and aiding early triage

Evaluation of the ESGE recommendations for COVID-19 pre-endoscopy risk-stratification in a high-volume center in Germany

Background and study aims The European Society of Gastrointestinal Endoscopy (ESGE) has defined COVID-19 infection prevention and control strategies within the endoscopy unit. These include pre-endoscopic questionnaire-based risk-stratification as well as pre-procedure viral testing. Real-life data on the effectiveness of these measures are presented here. Patients and methods Data from the outpatient endoscopic unit of the University Hospital Augsburg between July 1, 2020 and December 31, 2020 including the second pandemic wave were reviewed retrospectively. All patients were assessed with a pre-endoscopic risk-stratification questionnaire as well as viral testing using an antigen point-of-care test (Ag-POCT) in conjunction with a standard polymerase chain reaction (PCR) test. Highly elective procedures were postponed. The theoretically expected number of SARS-CoV-2-positive patients was simulated and compared with the actual number. In addition, endoscopy staff was evaluated with a rapid antibody test to determine the number of infections among the personnel. Results In total, 1029 procedures, 591 questionnaires, 591 Ag-POCTs, and 529 standard PCR tests were performed in 591 patients. 247 procedures in 142 patients were postponed. One Ag-POCT was positive but with a negative PCR test, while one PCR test was positive but with a negative Ag-POCT. This was lower than the theoretically expected number of COVID-19-positive patients (n = 15). One of 43 employees (2.3 %) in the outpatient endoscopy unit was seropositive. Conclusions Pre-endoscopic risk management including questionnaire-based risk stratification and viral testing seems to be an effective tool in combination with personal protective equipment for SARS-CoV-2 infection prevention and control within the endoscopy unit even in a high-prevalence setting

One year after mild COVID-19: the majority of patients maintain specific immunity, but one in four still suffer from long-term symptoms

After COVID-19, some patients develop long-term symptoms. Whether such symptoms correlate with immune responses, and how long immunity persists, is not yet clear. This study focused on mild COVID-19 and investigated correlations of immunity with persistent symptoms and immune longevity. Persistent complications, including headache, concentration difficulties and loss of smell/taste, were reported by 51 of 83 (61%) participants and decreased over time to 28% one year after COVID-19. Specific IgA and IgG antibodies were detectable in 78% and 66% of participants, respectively, at a 12-month follow-up. Median antibody levels decreased by approximately 50% within the first 6 months but remained stable up to 12 months. Neutralizing antibodies could be found in 50% of participants; specific INFgamma-producing T-cells were present in two thirds one year after COVID-19. Activation-induced marker assays identified specific T-helper cells and central memory T-cells in 80% of participants at a 12-month follow-up. In correlative analyses, older age and a longer duration of the acute phase of COVID-19 were associated with higher humoral and T-cell responses. A weak correlation between long-term loss of taste/smell and low IgA levels was found at early time points. These data indicate a long-lasting immunological memory against SARS-CoV-2 after mild COVID-19

Comparison of the development of SARS-Coronavirus-2-specific cellular immunity, and central memory CD4+ T-Cell responses following infection versus vaccination

Memory T-cell responses following infection with coronaviruses are reportedly long-lived and provide long-term protection against severe disease. Whether vaccination induces similar long-lived responses is not yet clear since, to date, there are limited data comparing memory CD4+ T-cell responses induced after SARS-CoV-2 infection versus following vaccination with BioNTech/Pfizer BNT162b2. We compared T-cell immune responses over time after infection or vaccination using ELISpot, and memory CD4+ T-cell responses three months after infection/vaccination using activation-induced marker flow cytometric assays. Levels of cytokine-producing T-cells were remarkably stable between three and twelve months after infection, and were comparable to IFNγ+ and IFNγ+IL-2+ T-cell responses but lower than IL-2+ T-cell responses at three months after vaccination. Consistent with this finding, vaccination and infection elicited comparable levels of SARS-CoV-2 specific CD4+ T-cells after three months in addition to comparable proportions of specific central memory CD4+ T-cells. By contrast, the proportions of specific effector memory CD4+ T-cells were significantly lower, whereas specific effector CD4+ T-cells were higher after infection than after vaccination. Our results suggest that T-cell responses—as measured by cytokine expression—and the frequencies of SARS-CoV-2-specific central memory CD4+T-cells—indicative of the formation of the long-lived memory T-cell compartment—are comparably induced after infection and vaccination

Quantitative Expression of C-Type Lectin Receptors in Humans and Mice

C-type lectin receptors, their adaptor molecules and S-type lectins (galectins) are involved in the recognition of glycosylated self-antigens and pathogens. However, little is known about the species- and organ-specific expression profiles of these molecules. We therefore determined the mRNA expression levels of Dectin-1, MR1, MR2, DC-SIGN, Syk, Card-9, Bcl-10, Malt-1, Src, Dec-205, Galectin-1, Tim-3, Trem-1, and DAP-12 in 11 solid organs of human and mice. Mouse organs revealed lower mRNA levels of most molecules compared to spleen. However, Dec-205 and Galectin-1 in thymus, Src in brain, MR2, Card-9, Bcl-10, Src, and Dec-205 in small intestine, MR2, Bcl-10, Src, Galectin-1 in kidney, and Src and Galectin-1 in muscle were at least 2-fold higher expressed compared to spleen. Human lung, liver and heart expressed higher mRNA levels of most genes compared to spleen. Dectin-1, MR1, Syk and Trem-1 mRNA were strongly up-regulated upon ischemia-reperfusion injury in murine kidney. Tim3, DAP-12, Card-9, DC-SIGN and MR2 were further up-regulated during renal fibrosis. Murine kidney showed higher DAP-12, Syk, Card-9 and Dectin-1 mRNA expression during the progression of lupus nephritis. Thus, the organ-, and species-specific expression of C-type lectin receptors and galectins is different between mice and humans which must be considered in the interpretation of related studies

An artificial intelligence algorithm is highly accurate for detecting endoscopic features of eosinophilic esophagitis

The endoscopic features associated with eosinophilic esophagitis (EoE) may be missed during routine endoscopy. We aimed to develop and evaluate an Artificial Intelligence (AI) algorithm for detecting and quantifying the endoscopic features of EoE in white light images, supplemented by the EoE Endoscopic Reference Score (EREFS). An AI algorithm (AI-EoE) was constructed and trained to differentiate between EoE and normal esophagus using endoscopic white light images extracted from the database of the University Hospital Augsburg. In addition to binary classification, a second algorithm was trained with specific auxiliary branches for each EREFS feature (AI-EoE-EREFS). The AI algorithms were evaluated on an external data set from the University of North Carolina, Chapel Hill (UNC), and compared with the performance of human endoscopists with varying levels of experience. The overall sensitivity, specificity, and accuracy of AI-EoE were 0.93 for all measures, while the AUC was 0.986. With additional auxiliary branches for the EREFS categories, the AI algorithm (AI-EoE-EREFS) performance improved to 0.96, 0.94, 0.95, and 0.992 for sensitivity, specificity, accuracy, and AUC, respectively. AI-EoE and AI-EoE-EREFS performed significantly better than endoscopy beginners and senior fellows on the same set of images. An AI algorithm can be trained to detect and quantify endoscopic features of EoE with excellent performance scores. The addition of the EREFS criteria improved the performance of the AI algorithm, which performed significantly better than endoscopists with a lower or medium experience level

Detection of duodenal villous atrophy on endoscopic images using a deep learning algorithm

Background and aims Celiac disease with its endoscopic manifestation of villous atrophy is underdiagnosed worldwide. The application of artificial intelligence (AI) for the macroscopic detection of villous atrophy at routine esophagogastroduodenoscopy may improve diagnostic performance. Methods A dataset of 858 endoscopic images of 182 patients with villous atrophy and 846 images from 323 patients with normal duodenal mucosa was collected and used to train a ResNet 18 deep learning model to detect villous atrophy. An external data set was used to test the algorithm, in addition to six fellows and four board certified gastroenterologists. Fellows could consult the AI algorithm’s result during the test. From their consultation distribution, a stratification of test images into “easy” and “difficult” was performed and used for classified performance measurement. Results External validation of the AI algorithm yielded values of 90 %, 76 %, and 84 % for sensitivity, specificity, and accuracy, respectively. Fellows scored values of 63 %, 72 % and 67 %, while the corresponding values in experts were 72 %, 69 % and 71 %, respectively. AI consultation significantly improved all trainee performance statistics. While fellows and experts showed significantly lower performance for “difficult” images, the performance of the AI algorithm was stable. Conclusion In this study, an AI algorithm outperformed endoscopy fellows and experts in the detection of villous atrophy on endoscopic still images. AI decision support significantly improved the performance of non-expert endoscopists. The stable performance on “difficult” images suggests a further positive add-on effect in challenging cases

Machine learning based prediction of COVID-19 mortality suggests repositioning of anticancer drug for treating severe cases

Despite available vaccinations COVID-19 case numbers around the world are still growing, and effective medications against severe cases are lacking. In this work, we developed a machine learning model which predicts mortality for COVID-19 patients using data from the multi-center ‘Lean European Open Survey on SARS-CoV-2-infected patients’ (LEOSS) observational study (>100 active sites in Europe, primarily in Germany), resulting into an AUC of almost 80%. We showed that molecular mechanisms related to dementia, one of the relevant predictors in our model, intersect with those associated to COVID-19. Most notably, among these molecules was tyrosine kinase 2 (TYK2), a protein that has been patented as drug target in Alzheimer's Disease but also genetically associated with severe COVID-19 outcomes. We experimentally verified that anti-cancer drugs Sorafenib and Regorafenib showed a clear anti-cytopathic effect in Caco2 and VERO-E6 cells and can thus be regarded as potential treatments against COVID-19. Altogether, our work demonstrates that interpretation of machine learning based risk models can point towards drug targets and new treatment options, which are strongly needed for COVID-19