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Cornelia Wiegand,1 Ulrike Wesenberg,2 Jan Heggemann3 1Department of Dermatology, Jena University Hospital, Friedrich Schiller University, Jena, Germany; 2Community Hospital Herdecke, Herdecke, Germany; 3Christian Hospital Melle, Niels Stensen Hospitals, Melle, GermanyCorrespondence: Cornelia Wiegand, Department of Dermatology, Jena University Hospital, Am Klinikum 1, Jena, D-07747, Germany, Tel +49 3641 9328878, Fax +49 3641 9328876, Email c.wiegand@med.uni-jena.dePurpose: Chronic wounds are impaired in their healing processes and often require a tissue boost to restart healing progression. Dressings of all forms have been used extensively for decades and a variety of dressings was specifically designed for chronic wounds. A dressing based on a hydropolymer gel matrix with a semi-permeable polyurethane backing is thought to provide an optimal balanced wound moisture environment and could hence positively affect the biochemical composition of exudate. However, the mechanism behind the technology is described sparsely.Patients and Methods: We herein report in vitro data to support the mode of action of the hydropolymer gel dressing concerning fluid management, protease binding and thermal insulation. Moreover, a non-interventional prospective study examined the use of the unique hydropolymer gel dressing on pain levels.Results: Data demonstrated a possible tissue boost by retaining as well as providing fluid, depending on the exudate amount, and by binding proinflammatory proteases. The dressing was further able to stabilize growth factors and provide thermal insulation, thereby positively supporting keratinocyte proliferation and migration in vitro. Furthermore, a significant reduction of the pain level after application could be observed clinically.Conclusion: Together, these factors provide a beneficial environment for wound healing and restarting stagnating wounds (or providing optimal conditions for acute wounds). In addition, the dressing appeared to be a good choice for painful wounds.Keywords: hydrogel, pain relief, cooling effect, thermal insulation, wound healing

Background: Marginal zone lymphomas of mucosa-associated lymphatic tissues (MZL of MALT) are a group of indolent B-cell neoplasms, which are thought to arise from chronic antigenic stimulation of B-cells either due to underlying chronic infection or autoimmune disease. Little is known about potential causative pathogens in pulmonary MZL (PMZL), although some data suggests a potential role of Achromobacter (A.) xylosoxidans. Methods: An index case of chronic pulmonary colonisation with Tropheryma (T.) whipplei and subsequent development of PMZL was identified by T. whipplei specific PCR and metagenomic next genome sequencing (mNGS). This case prompted a retrospectively conducted analysis of T. whipplei-specific PCRs in lung tissue from PMZL patients (n = 22), other pulmonary lymphomas, and normal controls. Positive results were confirmed by mNGS. A systematic search for T. whipplei and A. xylosoxidans in our in-house mNGS dataset comprising autopsy lungs, lung biopsies and lung resection specimens (n = 181) was subsequently performed. Results: A 69-year-old patient presented with weight loss and persistent pulmonary consolidation. Subsequent mNGS analysis detected T. whipplei in the resected lung specimen. An antibiotic regimen eventually eliminated the bacterium. However, the consolidation persisted, and the diagnosis of PMZL was made in a second lung resection specimen. A second case of T. whipplei-associated PMZL was subsequently detected in the retrospectively analysed PMZL cohort. Both cases showed comparatively few mutations and no mutations in genes encoding for NF-κB pathway components, suggesting that T. whipplei infection may substitute for mutations in these PMZL. None of the samples in our in-house dataset tested positive for T. whipplei. In contrast, A. xylosoxidans was frequently found in both autopsy lungs and lung biopsy / resection specimens that were not affected by PMZL (> 50%). Conclusions: Our data suggests that T. whipplei colonisation of lungs may trigger PMZL as a potential driver. Systematic analyses with larger cohorts should be conducted to further support this hypothesis. The frequent detection of A. xylosoxidans in lung tissue suggests that it is a common component of the pulmonary microbiome and therefore less likely to trigger lymphomas. [ABSTRACT FROM AUTHOR]

Coronal mass ejections (CMEs) drive space weather effects at Earth and the heliosphere. Predicting their arrival is a major part of space weather forecasting. In 2013, the Community Coordinated Modeling Center started collecting predictions from the community, developing an Arrival Time Scoreboard (ATSB). Riley et al. (2018, https://doi.org/10.1029/2018sw001962) analyzed the first 5 years of the ATSB, finding a bias of a few hours and uncertainty of order 15 hr. These metrics have been routinely quoted since 2018, but have not been updated despite continued predictions. We revise analysis of the ATSB using a sample 3.5 times the size of that in the original study. We find generally the same overall metrics, a bias of −2.5 hr, mean absolute error of 13.2 hr, and standard deviation of 17.4 hr, with only a slight improvement comparing between the previously‐used and new sets. The most well‐established, frequently‐submitted model results tend to outperform those from seldomly‐contributed models. These "best" models show a slight improvement over the 11 year span, with more scatter between the models during early times and a convergence toward the same error metrics in recent years. We find little evidence of any correlations between the arrival time errors and any other properties. The one noticeable exception is a tendency for late predictions for short transit times and vice versa. We propose that any model‐driven systematic errors may be washed out by the uncertainties in CME reconstructions in characterization of the background solar wind, and suggest that improving these may be the key to better predictions. Plain Language Summary: Coronal mass ejections (CMEs) are huge explosions that erupt from the Sun and travel through the solar system. It is important to have warning of when they reach Earth, so many researchers model the arrival time (AT) of CMEs. Many predictions are collected in the AT Scoreboard (ATSB), and the general errors from the first 5 years of the ATSB were determined in 2018. These errors are very important as a measure of the uncertainty of predictions, but they have not been updated in over 6 years. Now the data set is over 3.5 times larger so we determine updated results but ultimately only see a small change in the errors. We find a bias of only a few hours, so no strong tendency for either early or late predictions. We find an average absolute error of 13.2 hr but a wide range about this value for individual cases. We do see some evidence that the most commonly used models have improved over time, but the change is very small and there are many factors affecting whether it is significant or not. We suggest that improving how we reconstruct CME properties and model the background solar wind may improve predictions. Key Points: From 1702 arrival time predictions we find a bias of −2.5 hr, mean absolute error of 13.2 hr, and standard deviation of 17.4 hrThe routinely‐submitted models all perform fairly similar but there is more variation in models with smaller data setsWe find evidence of late predictions for coronal mass ejections (CMEs) with short transit times, and early predictions for CMEs with long transit times [ABSTRACT FROM AUTHOR]